From f39f911cec490539f9bdc452a9e03c79d730d576 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 16 Jul 2024 14:35:07 +0800
Subject: [PATCH 01/32] moved docs out of the source directory and removed
 spnix-related files

---
 docs/Makefile                                 |  26 --
 .../contents/about/index.md => about.md}      |   0
 docs/{source/contents => }/apis/index.md      |   0
 .../contents => }/apis/inference/index.md     |   0
 .../contents => }/apis/inference/v2.md        |   0
 .../contents => }/apis/internal/agent.md      |   0
 .../contents => }/apis/internal/chainer.md    |   0
 .../contents => }/apis/internal/index.md      |   0
 .../contents => }/apis/scheduler/index.md     |   0
 .../index.md => architecture/README.md}       |   8 +-
 .../contents => }/architecture/dataflow.md    |   6 +-
 docs/{source/contents => }/cli/docs/seldon.md |   0
 .../contents => }/cli/docs/seldon_config.md   |   0
 .../cli/docs/seldon_config_activate.md        |   0
 .../cli/docs/seldon_config_add.md             |   0
 .../cli/docs/seldon_config_deactivate.md      |   0
 .../cli/docs/seldon_config_list.md            |   0
 .../cli/docs/seldon_config_remove.md          |   0
 .../cli/docs/seldon_experiment.md             |   0
 .../cli/docs/seldon_experiment_list.md        |   0
 .../cli/docs/seldon_experiment_start.md       |   0
 .../cli/docs/seldon_experiment_status.md      |   0
 .../cli/docs/seldon_experiment_stop.md        |   0
 .../contents => }/cli/docs/seldon_model.md    |   0
 .../cli/docs/seldon_model_infer.md            |   0
 .../cli/docs/seldon_model_list.md             |   0
 .../cli/docs/seldon_model_load.md             |   0
 .../cli/docs/seldon_model_metadata.md         |   0
 .../cli/docs/seldon_model_status.md           |   0
 .../cli/docs/seldon_model_unload.md           |   0
 .../contents => }/cli/docs/seldon_pipeline.md |   0
 .../cli/docs/seldon_pipeline_infer.md         |   0
 .../cli/docs/seldon_pipeline_inspect.md       |   0
 .../cli/docs/seldon_pipeline_list.md          |   0
 .../cli/docs/seldon_pipeline_load.md          |   0
 .../cli/docs/seldon_pipeline_status.md        |   0
 .../cli/docs/seldon_pipeline_unload.md        |   0
 .../contents => }/cli/docs/seldon_server.md   |   0
 .../cli/docs/seldon_server_list.md            |   0
 .../cli/docs/seldon_server_status.md          |   0
 docs/{source/contents => }/cli/index.md       |   0
 docs/conversational_memory.ipynb              | 386 ++++++++++++++++++
 .../contents => }/development/index.md        |   0
 .../contents => }/development/licenses.md     |   0
 .../development/release/.images/release-1.png | Bin
 .../development/release/.images/release-2.png | Bin
 .../development/release/.images/release-3.png | Bin
 .../development/release/.images/release-4.png | Bin
 .../development/release/.images/release-5.png | Bin
 .../development/release/.images/release-6.png | Bin
 .../development/release/index.md              |   0
 docs/{source/contents => }/drift/index.md     |   0
 .../examples/batch-examples-k8s.md            |   0
 .../examples/batch-examples-local.md          |   0
 .../{source/contents => }/examples/cifar10.md |   0
 .../contents => }/examples/custom-servers.md  |   0
 .../examples/experiment-versions.md           |   0
 .../examples/explainer-examples.md            |   0
 .../contents => }/examples/huggingface.md     |   0
 docs/{source/contents => }/examples/income.md |   0
 docs/{source/contents => }/examples/index.md  |   0
 .../contents => }/examples/inference.md       |   0
 .../contents => }/examples/k8s-clusterwide.md |   0
 .../contents => }/examples/k8s-examples.md    |   0
 .../{source/contents => }/examples/k8s-pvc.md |   0
 .../contents => }/examples/local-examples.md  |   0
 .../examples/local-experiments.md             |   0
 .../examples/local-overcommit-examples.md     |   0
 .../contents => }/examples/model-zoo.md       |   0
 .../contents => }/examples/multi-version.md   |   0
 .../contents => }/examples/pandasquery.md     |   0
 .../examples/pipeline-examples.md             |   0
 .../examples/pipeline-ready-and-metadata.md   |   0
 .../examples/pipeline-to-pipeline.md          |   0
 .../examples/speech-to-sentiment.md           |   0
 .../examples/tritonclient-examples.md         |   0
 .../contents => }/experiments/index.md        |   0
 .../{source/contents => }/explainers/index.md |   0
 docs/{source/contents => }/faqs/index.md      |   0
 .../contents => }/getting-started/cli.md      |   0
 .../getting-started/configuration/index.md    |   0
 .../docker-installation/index.md              |   0
 .../contents => }/getting-started/index.md    |   0
 .../kubernetes-installation/ansible.md        |   0
 .../kubernetes-installation/helm.md           |   0
 .../kubernetes-installation/index.md          |   0
 .../security/aws-msk-mtls.md                  |   0
 .../security/aws-msk-sasl.md                  |   0
 .../security/azure-event-hub-sasl.md          |   0
 .../security/confluent-oauth.md               |   0
 .../security/confluent-sasl.md                |   0
 .../kubernetes-installation/security/index.md |   0
 .../security/networking.jpg                   | Bin
 .../security/reference.md                     |   0
 .../security/strimzi-mtls.md                  |   0
 .../security/strimzi-sasl.md                  |   0
 .../architecture => images}/architecture.png  | Bin
 .../autoscaling_architecture.png              | Bin
 .../dataflow-inference-graph.png              | Bin
 .../architecture => images}/dataflow.png      | Bin
 .../inference_graph.png                       | Bin
 .../contents/about => images}/intro.png       | Bin
 .../architecture => images}/joins.png         | Bin
 .../architecture => images}/kafka.png         | Bin
 .../contents/about => images}/mesh.png        | Bin
 .../contents/about => images}/multimodel1.png | Bin
 .../orchestrator-inference-graph.png          | Bin
 .../scheduler_autoscaling_state_diagram.png   | Bin
 .../contents/about => images}/servers.png     | Bin
 docs/{source/contents => }/inference/index.md |   0
 .../kubernetes/autoscaling/index.md           |   0
 .../{source/contents => }/kubernetes/index.md |   0
 .../contents => }/kubernetes/kafka/index.md   |   0
 .../contents => }/kubernetes/metrics/index.md |   0
 .../kubernetes/resources/experiment/index.md  |   0
 .../kubernetes/resources/index.md             |   0
 .../kubernetes/resources/model/index.md       |   0
 .../kubernetes/resources/pipeline/index.md    |   0
 .../resources/seldonconfig/index.md           |   0
 .../resources/seldonruntime/index.md          |   0
 .../kubernetes/resources/server/index.md      |   0
 .../resources/serverconfig/index.md           |   0
 .../contents => }/kubernetes/scaling/index.md |   0
 .../service-meshes/ambassador/index.md        |   0
 .../kubernetes/service-meshes/index.md        |   0
 .../kubernetes/service-meshes/istio/index.md  |   0
 .../service-meshes/traefik/index.md           |   0
 .../kubernetes/storage-secrets/index.md       |   0
 .../contents => }/kubernetes/tracing/index.md |   0
 .../kubernetes/tracing/jaeger-trace.png       | Bin
 docs/make.bat                                 |  35 --
 .../contents => }/metrics/dashboard.png       | Bin
 .../metrics/hodometer-architecture.png        | Bin
 docs/{source/contents => }/metrics/index.md   |   0
 .../metrics/local-metrics-test.md             |   0
 .../contents => }/metrics/operational.md      |   0
 docs/{source/contents => }/metrics/usage.md   |   0
 docs/{source/contents => }/models/index.md    |   0
 .../models/inference-artifacts/index.md       |   0
 docs/{source/contents => }/models/mms/mms.md  |   0
 docs/{source/contents => }/models/mms/mms.png | Bin
 .../contents => }/models/mms/overcommit.png   | Bin
 .../models/parameterized-models/index.md      |   0
 .../parameterized-models/pandasquery.md       |   0
 .../contents => }/models/rclone/index.md      |   0
 docs/{source/contents => }/outlier/index.md   |   0
 .../contents => }/performance-tests/index.md  |   0
 docs/{source/contents => }/pipelines/index.md |   0
 .../pipelines/jaeger-tracing.png              | Bin
 docs/requirements-docs.txt                    |  21 -
 docs/{source/contents => }/servers/index.md   |   0
 docs/source/_static/versions.json             |   3 -
 docs/source/conf.py                           | 184 ---------
 docs/{source/contents => }/tutorials/index.md |   0
 .../contents => }/tutorials/workflow.png      | Bin
 docs/{source/contents => }/upgrading/index.md |   0
 156 files changed, 393 insertions(+), 276 deletions(-)
 delete mode 100644 docs/Makefile
 rename docs/{source/contents/about/index.md => about.md} (100%)
 rename docs/{source/contents => }/apis/index.md (100%)
 rename docs/{source/contents => }/apis/inference/index.md (100%)
 rename docs/{source/contents => }/apis/inference/v2.md (100%)
 rename docs/{source/contents => }/apis/internal/agent.md (100%)
 rename docs/{source/contents => }/apis/internal/chainer.md (100%)
 rename docs/{source/contents => }/apis/internal/index.md (100%)
 rename docs/{source/contents => }/apis/scheduler/index.md (100%)
 rename docs/{source/contents/architecture/index.md => architecture/README.md} (94%)
 rename docs/{source/contents => }/architecture/dataflow.md (97%)
 rename docs/{source/contents => }/cli/docs/seldon.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_config.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_config_activate.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_config_add.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_config_deactivate.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_config_list.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_config_remove.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_experiment.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_experiment_list.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_experiment_start.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_experiment_status.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_experiment_stop.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_model.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_model_infer.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_model_list.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_model_load.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_model_metadata.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_model_status.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_model_unload.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_pipeline.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_pipeline_infer.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_pipeline_inspect.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_pipeline_list.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_pipeline_load.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_pipeline_status.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_pipeline_unload.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_server.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_server_list.md (100%)
 rename docs/{source/contents => }/cli/docs/seldon_server_status.md (100%)
 rename docs/{source/contents => }/cli/index.md (100%)
 create mode 100644 docs/conversational_memory.ipynb
 rename docs/{source/contents => }/development/index.md (100%)
 rename docs/{source/contents => }/development/licenses.md (100%)
 rename docs/{source/contents => }/development/release/.images/release-1.png (100%)
 rename docs/{source/contents => }/development/release/.images/release-2.png (100%)
 rename docs/{source/contents => }/development/release/.images/release-3.png (100%)
 rename docs/{source/contents => }/development/release/.images/release-4.png (100%)
 rename docs/{source/contents => }/development/release/.images/release-5.png (100%)
 rename docs/{source/contents => }/development/release/.images/release-6.png (100%)
 rename docs/{source/contents => }/development/release/index.md (100%)
 rename docs/{source/contents => }/drift/index.md (100%)
 rename docs/{source/contents => }/examples/batch-examples-k8s.md (100%)
 rename docs/{source/contents => }/examples/batch-examples-local.md (100%)
 rename docs/{source/contents => }/examples/cifar10.md (100%)
 rename docs/{source/contents => }/examples/custom-servers.md (100%)
 rename docs/{source/contents => }/examples/experiment-versions.md (100%)
 rename docs/{source/contents => }/examples/explainer-examples.md (100%)
 rename docs/{source/contents => }/examples/huggingface.md (100%)
 rename docs/{source/contents => }/examples/income.md (100%)
 rename docs/{source/contents => }/examples/index.md (100%)
 rename docs/{source/contents => }/examples/inference.md (100%)
 rename docs/{source/contents => }/examples/k8s-clusterwide.md (100%)
 rename docs/{source/contents => }/examples/k8s-examples.md (100%)
 rename docs/{source/contents => }/examples/k8s-pvc.md (100%)
 rename docs/{source/contents => }/examples/local-examples.md (100%)
 rename docs/{source/contents => }/examples/local-experiments.md (100%)
 rename docs/{source/contents => }/examples/local-overcommit-examples.md (100%)
 rename docs/{source/contents => }/examples/model-zoo.md (100%)
 rename docs/{source/contents => }/examples/multi-version.md (100%)
 rename docs/{source/contents => }/examples/pandasquery.md (100%)
 rename docs/{source/contents => }/examples/pipeline-examples.md (100%)
 rename docs/{source/contents => }/examples/pipeline-ready-and-metadata.md (100%)
 rename docs/{source/contents => }/examples/pipeline-to-pipeline.md (100%)
 rename docs/{source/contents => }/examples/speech-to-sentiment.md (100%)
 rename docs/{source/contents => }/examples/tritonclient-examples.md (100%)
 rename docs/{source/contents => }/experiments/index.md (100%)
 rename docs/{source/contents => }/explainers/index.md (100%)
 rename docs/{source/contents => }/faqs/index.md (100%)
 rename docs/{source/contents => }/getting-started/cli.md (100%)
 rename docs/{source/contents => }/getting-started/configuration/index.md (100%)
 rename docs/{source/contents => }/getting-started/docker-installation/index.md (100%)
 rename docs/{source/contents => }/getting-started/index.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/ansible.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/helm.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/index.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/security/aws-msk-mtls.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/security/aws-msk-sasl.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/security/azure-event-hub-sasl.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/security/confluent-oauth.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/security/confluent-sasl.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/security/index.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/security/networking.jpg (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/security/reference.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/security/strimzi-mtls.md (100%)
 rename docs/{source/contents => }/getting-started/kubernetes-installation/security/strimzi-sasl.md (100%)
 rename docs/{source/contents/architecture => images}/architecture.png (100%)
 rename docs/{source/contents/architecture => images}/autoscaling_architecture.png (100%)
 rename docs/{source/contents/architecture => images}/dataflow-inference-graph.png (100%)
 rename docs/{source/contents/architecture => images}/dataflow.png (100%)
 rename docs/{source/contents/architecture => images}/inference_graph.png (100%)
 rename docs/{source/contents/about => images}/intro.png (100%)
 rename docs/{source/contents/architecture => images}/joins.png (100%)
 rename docs/{source/contents/architecture => images}/kafka.png (100%)
 rename docs/{source/contents/about => images}/mesh.png (100%)
 rename docs/{source/contents/about => images}/multimodel1.png (100%)
 rename docs/{source/contents/architecture => images}/orchestrator-inference-graph.png (100%)
 rename docs/{source/contents/architecture => images}/scheduler_autoscaling_state_diagram.png (100%)
 rename docs/{source/contents/about => images}/servers.png (100%)
 rename docs/{source/contents => }/inference/index.md (100%)
 rename docs/{source/contents => }/kubernetes/autoscaling/index.md (100%)
 rename docs/{source/contents => }/kubernetes/index.md (100%)
 rename docs/{source/contents => }/kubernetes/kafka/index.md (100%)
 rename docs/{source/contents => }/kubernetes/metrics/index.md (100%)
 rename docs/{source/contents => }/kubernetes/resources/experiment/index.md (100%)
 rename docs/{source/contents => }/kubernetes/resources/index.md (100%)
 rename docs/{source/contents => }/kubernetes/resources/model/index.md (100%)
 rename docs/{source/contents => }/kubernetes/resources/pipeline/index.md (100%)
 rename docs/{source/contents => }/kubernetes/resources/seldonconfig/index.md (100%)
 rename docs/{source/contents => }/kubernetes/resources/seldonruntime/index.md (100%)
 rename docs/{source/contents => }/kubernetes/resources/server/index.md (100%)
 rename docs/{source/contents => }/kubernetes/resources/serverconfig/index.md (100%)
 rename docs/{source/contents => }/kubernetes/scaling/index.md (100%)
 rename docs/{source/contents => }/kubernetes/service-meshes/ambassador/index.md (100%)
 rename docs/{source/contents => }/kubernetes/service-meshes/index.md (100%)
 rename docs/{source/contents => }/kubernetes/service-meshes/istio/index.md (100%)
 rename docs/{source/contents => }/kubernetes/service-meshes/traefik/index.md (100%)
 rename docs/{source/contents => }/kubernetes/storage-secrets/index.md (100%)
 rename docs/{source/contents => }/kubernetes/tracing/index.md (100%)
 rename docs/{source/contents => }/kubernetes/tracing/jaeger-trace.png (100%)
 delete mode 100644 docs/make.bat
 rename docs/{source/contents => }/metrics/dashboard.png (100%)
 rename docs/{source/contents => }/metrics/hodometer-architecture.png (100%)
 rename docs/{source/contents => }/metrics/index.md (100%)
 rename docs/{source/contents => }/metrics/local-metrics-test.md (100%)
 rename docs/{source/contents => }/metrics/operational.md (100%)
 rename docs/{source/contents => }/metrics/usage.md (100%)
 rename docs/{source/contents => }/models/index.md (100%)
 rename docs/{source/contents => }/models/inference-artifacts/index.md (100%)
 rename docs/{source/contents => }/models/mms/mms.md (100%)
 rename docs/{source/contents => }/models/mms/mms.png (100%)
 rename docs/{source/contents => }/models/mms/overcommit.png (100%)
 rename docs/{source/contents => }/models/parameterized-models/index.md (100%)
 rename docs/{source/contents => }/models/parameterized-models/pandasquery.md (100%)
 rename docs/{source/contents => }/models/rclone/index.md (100%)
 rename docs/{source/contents => }/outlier/index.md (100%)
 rename docs/{source/contents => }/performance-tests/index.md (100%)
 rename docs/{source/contents => }/pipelines/index.md (100%)
 rename docs/{source/contents => }/pipelines/jaeger-tracing.png (100%)
 delete mode 100644 docs/requirements-docs.txt
 rename docs/{source/contents => }/servers/index.md (100%)
 delete mode 100644 docs/source/_static/versions.json
 delete mode 100644 docs/source/conf.py
 rename docs/{source/contents => }/tutorials/index.md (100%)
 rename docs/{source/contents => }/tutorials/workflow.png (100%)
 rename docs/{source/contents => }/upgrading/index.md (100%)

diff --git a/docs/Makefile b/docs/Makefile
deleted file mode 100644
index c015396571..0000000000
--- a/docs/Makefile
+++ /dev/null
@@ -1,26 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = source
-BUILDDIR      = build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-install-dev:
-	pip install -r requirements-docs.txt
-
-dev_server: clean
-	sphinx-autobuild --host 0.0.0.0 source build/html%
diff --git a/docs/source/contents/about/index.md b/docs/about.md
similarity index 100%
rename from docs/source/contents/about/index.md
rename to docs/about.md
diff --git a/docs/source/contents/apis/index.md b/docs/apis/index.md
similarity index 100%
rename from docs/source/contents/apis/index.md
rename to docs/apis/index.md
diff --git a/docs/source/contents/apis/inference/index.md b/docs/apis/inference/index.md
similarity index 100%
rename from docs/source/contents/apis/inference/index.md
rename to docs/apis/inference/index.md
diff --git a/docs/source/contents/apis/inference/v2.md b/docs/apis/inference/v2.md
similarity index 100%
rename from docs/source/contents/apis/inference/v2.md
rename to docs/apis/inference/v2.md
diff --git a/docs/source/contents/apis/internal/agent.md b/docs/apis/internal/agent.md
similarity index 100%
rename from docs/source/contents/apis/internal/agent.md
rename to docs/apis/internal/agent.md
diff --git a/docs/source/contents/apis/internal/chainer.md b/docs/apis/internal/chainer.md
similarity index 100%
rename from docs/source/contents/apis/internal/chainer.md
rename to docs/apis/internal/chainer.md
diff --git a/docs/source/contents/apis/internal/index.md b/docs/apis/internal/index.md
similarity index 100%
rename from docs/source/contents/apis/internal/index.md
rename to docs/apis/internal/index.md
diff --git a/docs/source/contents/apis/scheduler/index.md b/docs/apis/scheduler/index.md
similarity index 100%
rename from docs/source/contents/apis/scheduler/index.md
rename to docs/apis/scheduler/index.md
diff --git a/docs/source/contents/architecture/index.md b/docs/architecture/README.md
similarity index 94%
rename from docs/source/contents/architecture/index.md
rename to docs/architecture/README.md
index e049bbf8c1..c806de4abd 100644
--- a/docs/source/contents/architecture/index.md
+++ b/docs/architecture/README.md
@@ -2,7 +2,7 @@
 
 The current set of components used in Seldon Core V2 is shown below:
 
-![architecture](architecture.png)
+![architecture](../images/architecture.png)
 
 The core components are:
 
@@ -23,17 +23,17 @@ Kafka is used as the backbone for Pipelines allowing a decentralized, synchronou
 
 Kafka is used as the backbone for allowing Pipelines of Models to be connected together into arbitrary directed acyclic graphs. Models can be reused in different Pipelines. The flow of data between models is handled by the dataflow engine using [KStreams](https://docs.confluent.io/platform/current/streams/concepts.html).
 
-![kafka](kafka.png)
+![kafka](../images/kafka.png)
 
 ## Dataflow Architecture
 
 Seldon V2 follows a dataflow design paradigm and it's part of the current movement for data centric machine learning. By taking a decentralized route that focuses on the flow of data users can have more flexibility and insight in building complex applications containing machine learning and traditional components. This contrasts with a more centralized orchestration more traditional in service orientated architectures.
 
-![dataflow](dataflow.png)
+![dataflow](../images/dataflow.png)
 
 By focusing on the data we allow users to join various flows together using stream joining concepts as shown below.
 
-![joins](joins.png)
+![joins](../images/joins.png)
 
 We support several types of joins:
  * _inner joins_, where all inputs need to be present for a transaction to join the tensors passed through the Pipeline;
diff --git a/docs/source/contents/architecture/dataflow.md b/docs/architecture/dataflow.md
similarity index 97%
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index b17443cb89..cb715515b2 100644
--- a/docs/source/contents/architecture/dataflow.md
+++ b/docs/architecture/dataflow.md
@@ -6,11 +6,11 @@ Seldon Core v2 is designed around data flow paradigm. Here we will explain what
 
 Initial release of Seldon Core introduced a concept of an _inference graph_, which can be thought of as a sequence of operations that happen to the inference request. Here is how it may look like:
 
-![inference_graph](inference_graph.png)
+![inference_graph](../images/inference_graph.png)
 
 In reality though this was not how Seldon Core v1 is implemented. Instead, Seldon deployment consists of a range of independent services that host models, transformations, detectors and explainers, and a central orchestrator that knows the inference graph topology and makes service calls in the correct order, passing data between requests and responses as necessary. Here is how the picture looks under the hood:
 
-![orchestrator](orchestrator-inference-graph.png)
+![orchestrator](../images/orchestrator-inference-graph.png)
 
 While this is a convenient way of implementing evaluation graph with microservices, it has a few problems. Orchestrator becomes a bottleneck and a single point of failure. It also hides all the data transformations that need to happen to translate one service's response to another service's request. Data tracing and lineage becomes difficult. All in all, while Seldon platform is all about processing data, under-the-hood implementation was still focused on order of operations and not on data itself.
 
@@ -24,7 +24,7 @@ Data flow design makes data in software the top priority. That is one of the key
 
 In the context of Seldon Core application of FBP design approach means that the evaluation implementation is done the same way inferece graph. So instead of routing everything through a centralized orchestrator the evaluation happens in the same graph-like manner:
 
-![dataflow](dataflow-inference-graph.png)
+![dataflow](../images/dataflow-inference-graph.png)
 
 As far as implementation goes, Seldon Core v2 runs on Kafka. Inference request is put onto a pipeline input topic, which triggers an evaluation. Each part of the inference graph is a service running in its own container fronted by a model gateway. Model gateway listens to a corresponding input Kafka topic, reads data from it, calls the service and puts the received response to an output Kafka topic. There is also a pipeline gateway that allows to interact with Seldon Core in synchronous manner.
 
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diff --git a/docs/conversational_memory.ipynb b/docs/conversational_memory.ipynb
new file mode 100644
index 0000000000..5fea8d0edd
--- /dev/null
+++ b/docs/conversational_memory.ipynb
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conversational Memory Example\n",
+    "\n",
+    "This example demonstrates how to run locally a Conversational Memory Runtime MLServer instance. It \n",
+    "also illustrates the different ways it can be used. (Note: this example only showcases the Conversational \n",
+    "Memory as a stand-alone component, for a more integrated example check out [chatbot](/examples/chat_bot/chat_bot.md).)\n",
+    "\n",
+    "To get up and running we need to install the runtime. To do this create a virtual environment within \n",
+    "the base memory runtime directory:\n",
+    "\n",
+    "```sh\n",
+    "python3 -m venv venv\n",
+    "```\n",
+    "\n",
+    "and then install:\n",
+    "\n",
+    "```sh\n",
+    "pip install .\n",
+    "```\n",
+    "\n",
+    "We are now ready to create the `model-settings.json`. If this kind of file is new to you, make sure to \n",
+    "check out the [MLSerer docs](https://mlserver.readthedocs.io/en/latest/reference/model-settings.html) for \n",
+    "a detailed description on what they are and how they work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"name\": \"conversational_memory\",\n",
+      "  \"implementation\": \"mlserver_memory.ConversationalMemory\",\n",
+      "  \"parameters\": {\n",
+      "    \"extra\": {\n",
+      "      \"database_config\": {\n",
+      "        \"database\": \"filesys\"\n",
+      "      },\n",
+      "      \"memory_config\": {\n",
+      "        \"window_size\": 2,\n",
+      "        \"tensor_names\": [\"content\"]\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat filesys/model-settings.json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See the ConversationalMemory [specification](api-ref-cbms) for an explanation of the `memory_config` \n",
+    "parameters as well as the file system backend [specification](api-ref-fsc). \n",
+    "\n",
+    "\n",
+    "Now that the `model-settings.json` file is ready, we can start the runtime using:\n",
+    "\n",
+    "```sh\n",
+    "mlserver start filesys\n",
+    "```\n",
+    "\n",
+    "There are two intended workflows for using the memory runtime: memory updates and memory retrieval. In \n",
+    "the first one, the developer wishes to update the memory allocated to an LLM with new messages and, \n",
+    "in the second one, the developer wants to get messages from the memory store. When sending a request \n",
+    "to the runtime the developer must always include a `memory_id` tensor that the runtime uses to identify \n",
+    "the relevant part of a conversation needed. Regardless of whether the history of a conversation is updated, \n",
+    "the runtime will return a `window_size` object with the number of messages from the memory.\n",
+    "\n",
+    "![developer updates conversation history](./update_workflow.png)\n",
+    "\n",
+    "For a more concrete example of the first workflow, imagine we want to get the conversational history from a conversation about \n",
+    "some financial documents. A developer can do this by just sending the `memory_id` of a tensor corresponding to\n",
+    "the question `Why did Apple acquire X company?`. In doing so, the runtime will return the last `window_size` \n",
+    "number of messages it has stored, for example, `7` if that is the number of questions previously asked in the \n",
+    "same conversation.\n",
+    "\n",
+    "For the second workflow, suppose we have set the max number of messages in our memory to 10 and that we would like \n",
+    "to update the last one (`window_size - 1`) on behalf of our user. To do so we'd send a `memory_id` tensor as well \n",
+    "as a `content` tensor to the runtime to update the memory with the new message, read the last `window_size` messages \n",
+    "(including the new one) and return these. This is done to make the typical flow of a conversation easy to \n",
+    "implement with the runtime. The next examples will shed some light on how this process works.\n",
+    "\n",
+    "**Note**: If the developer wished they could store more than one tensor by updating the list of `tensor_names` \n",
+    "in the `model-settings.json` file. The only restriction is that if they specify multiple tensors, then they always \n",
+    "send an equal number of each to prevent the store from getting out of sync.\n",
+    "\n",
+    "**Note**: The `window_size` parameter is intended to be used to control the size of requests being sent \n",
+    "along the pipelines in which the memory runtime is deployed. It's not intended to be used to control the \n",
+    "number of tokens sent to the LLM. Token bounds can be configured in the [LLM inference runtime](../api/index.md).\n",
+    "\n",
+    "To get started, let's send a request with a `memory_id` and some content to initialize a conversational \n",
+    "store. We've chosen the `memory_id=6e1f1413` at random."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id': 'dccd1dbd-4ec8-4172-a737-fb6480d20bc4',\n",
+      " 'model_name': 'conversational_memory',\n",
+      " 'outputs': [{'data': ['6e1f1413'],\n",
+      "              'datatype': 'BYTES',\n",
+      "              'name': 'memory_id',\n",
+      "              'parameters': {'content_type': 'str'},\n",
+      "              'shape': [1, 1]},\n",
+      "             {'data': ['Hello, how are you?'],\n",
+      "              'datatype': 'BYTES',\n",
+      "              'name': 'content',\n",
+      "              'parameters': {'content_type': 'str'},\n",
+      "              'shape': [1, 1]}],\n",
+      " 'parameters': {}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import pprint\n",
+    "\n",
+    "\n",
+    "inference_request = {\n",
+    "    \"inputs\": [\n",
+    "        {\n",
+    "            \"name\": \"memory_id\",\n",
+    "            \"shape\": [1],\n",
+    "            \"datatype\": \"BYTES\",\n",
+    "            \"data\": [\"6e1f1413\"],\n",
+    "            \"parameters\": {\"content_type\": \"str\"},\n",
+    "        },\n",
+    "        {\n",
+    "            \"name\": \"content\",\n",
+    "            \"shape\": [1],\n",
+    "            \"datatype\": \"BYTES\",\n",
+    "            \"data\": [\"Hello, how are you?\"],\n",
+    "            \"parameters\": {\"content_type\": \"str\"},\n",
+    "        }\n",
+    "    ]\n",
+    "}\n",
+    "\n",
+    "endpoint = f\"http://localhost:8080/v2/models/conversational_memory/infer\"\n",
+    "response_with_params = requests.post(\n",
+    "    endpoint,\n",
+    "    json=inference_request,\n",
+    ")\n",
+    "\n",
+    "pprint.pprint(response_with_params.json())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, the response returned contains the `memory_id` and the content. The message you've sent \n",
+    "has now been updated in the conversational store. We can now send a second request with the `memory_id` \n",
+    "alone in order to see what's contained in the memory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id': '618a7d1d-1a2b-40d5-9c43-28ab7769f4dc',\n",
+      " 'model_name': 'conversational_memory',\n",
+      " 'outputs': [{'data': ['6e1f1413'],\n",
+      "              'datatype': 'BYTES',\n",
+      "              'name': 'memory_id',\n",
+      "              'parameters': {'content_type': 'str'},\n",
+      "              'shape': [1, 1]},\n",
+      "             {'data': ['Hello, how are you?'],\n",
+      "              'datatype': 'BYTES',\n",
+      "              'name': 'content',\n",
+      "              'parameters': {'content_type': 'str'},\n",
+      "              'shape': [1, 1]}],\n",
+      " 'parameters': {}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "inference_request = {\n",
+    "    \"inputs\": [\n",
+    "        {\n",
+    "            \"name\": \"memory_id\",\n",
+    "            \"shape\": [1],\n",
+    "            \"datatype\": \"BYTES\",\n",
+    "            \"data\": [\"6e1f1413\"],\n",
+    "            \"parameters\": {\"content_type\": \"str\"},\n",
+    "        }\n",
+    "    ]\n",
+    "}\n",
+    "endpoint = f\"http://localhost:8080/v2/models/conversational_memory/infer\"\n",
+    "response_with_params = requests.post(\n",
+    "    endpoint,\n",
+    "    json=inference_request\n",
+    ")\n",
+    "\n",
+    "pprint.pprint(response_with_params.json())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As expected, we got back the previous message we just sent.\n",
+    "\n",
+    "Multiple messages can be sent to the memory runtime at one time. As an example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id': 'fac725c0-0195-408e-a8fe-be4ad2b2ad2a',\n",
+      " 'model_name': 'conversational_memory',\n",
+      " 'outputs': [{'data': ['5P5pB2Lv'],\n",
+      "              'datatype': 'BYTES',\n",
+      "              'name': 'memory_id',\n",
+      "              'parameters': {'content_type': 'str'},\n",
+      "              'shape': [1, 1]},\n",
+      "             {'data': [\"Good, i'd like to buy a car.\",\n",
+      "                       'Really, what kind of car?'],\n",
+      "              'datatype': 'BYTES',\n",
+      "              'name': 'content',\n",
+      "              'parameters': {'content_type': 'str'},\n",
+      "              'shape': [2, 1]}],\n",
+      " 'parameters': {}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "inference_request = {\n",
+    "    \"inputs\": [\n",
+    "        {\n",
+    "            \"name\": \"memory_id\",\n",
+    "            \"shape\": [1],\n",
+    "            \"datatype\": \"BYTES\",\n",
+    "            \"data\": [\"5P5pB2Lv\"],\n",
+    "            \"parameters\": {\"content_type\": \"str\"},\n",
+    "        },\n",
+    "        {\n",
+    "            \"name\": \"content\",\n",
+    "            \"shape\": [1],\n",
+    "            \"datatype\": \"BYTES\",\n",
+    "            \"data\": [\n",
+    "                \"Hello, how are you?\",\n",
+    "                \"I'm well, how are you?\",\n",
+    "                \"Good, i'd like to buy a car.\",\n",
+    "                \"Really, what kind of car?\"\n",
+    "            ],\n",
+    "            \"parameters\": {\"content_type\": \"str\"},\n",
+    "        }\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "endpoint = f\"http://localhost:8080/v2/models/conversational_memory/infer\"\n",
+    "response_with_params = requests.post(\n",
+    "    endpoint,\n",
+    "    json=inference_request,\n",
+    ")\n",
+    "\n",
+    "pprint.pprint(response_with_params.json())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above will write all the content tensors at the same time. This is useful for loading conversations \n",
+    "from a different store into the current conversational memory. Note that, we only received 2 messages back \n",
+    "out of the 4 we sent. This is because we've set the `window_size` parameter in the `model-settings.json` to \n",
+    "2. To change this we can either do so in the model settings themselves and then restart MLServer, or we \n",
+    "can pass a parameter in the inference request that sets the `window_size` value dynamically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'id': '2e970796-4f75-4372-9cef-84c2afca854c',\n",
+      " 'model_name': 'conversational_memory',\n",
+      " 'outputs': [{'data': ['5P5pB2Lv'],\n",
+      "              'datatype': 'BYTES',\n",
+      "              'name': 'memory_id',\n",
+      "              'parameters': {'content_type': 'str'},\n",
+      "              'shape': [1, 1]},\n",
+      "             {'data': ['Hello, how are you?',\n",
+      "                       \"I'm well, how are you?\",\n",
+      "                       \"Good, i'd like to buy a car.\",\n",
+      "                       'Really, what kind of car?'],\n",
+      "              'datatype': 'BYTES',\n",
+      "              'name': 'content',\n",
+      "              'parameters': {'content_type': 'str'},\n",
+      "              'shape': [4, 1]}],\n",
+      " 'parameters': {}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "inference_request = {\n",
+    "    \"inputs\": [\n",
+    "        {\n",
+    "            \"name\": \"memory_id\",\n",
+    "            \"shape\": [1],\n",
+    "            \"datatype\": \"BYTES\",\n",
+    "            \"data\": [\"5P5pB2Lv\"],\n",
+    "            \"parameters\": {\"content_type\": \"str\"},\n",
+    "        },\n",
+    "    ],\n",
+    "    \"parameters\": {\n",
+    "        \"memory_parameters\": {\"window_size\": 5}\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "endpoint = f\"http://localhost:8080/v2/models/conversational_memory/infer\"\n",
+    "response_with_params = requests.post(\n",
+    "    endpoint,\n",
+    "    json=inference_request,\n",
+    ")\n",
+    "\n",
+    "pprint.pprint(response_with_params.json())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above showcases the memory runtime as a stand-alone server. The runtime itself is intended to be used \n",
+    "as a component within a Seldon Core V2 pipeline along with other components such as large language models \n",
+    "and other LLM-based tools. This [chatbot](/examples/chat_bot/chat_bot.md) example illustrates such a usecase."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/docs/make.bat b/docs/make.bat
deleted file mode 100644
index 6247f7e231..0000000000
--- a/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=source
-set BUILDDIR=build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
diff --git a/docs/source/contents/metrics/dashboard.png b/docs/metrics/dashboard.png
similarity index 100%
rename from docs/source/contents/metrics/dashboard.png
rename to docs/metrics/dashboard.png
diff --git a/docs/source/contents/metrics/hodometer-architecture.png b/docs/metrics/hodometer-architecture.png
similarity index 100%
rename from docs/source/contents/metrics/hodometer-architecture.png
rename to docs/metrics/hodometer-architecture.png
diff --git a/docs/source/contents/metrics/index.md b/docs/metrics/index.md
similarity index 100%
rename from docs/source/contents/metrics/index.md
rename to docs/metrics/index.md
diff --git a/docs/source/contents/metrics/local-metrics-test.md b/docs/metrics/local-metrics-test.md
similarity index 100%
rename from docs/source/contents/metrics/local-metrics-test.md
rename to docs/metrics/local-metrics-test.md
diff --git a/docs/source/contents/metrics/operational.md b/docs/metrics/operational.md
similarity index 100%
rename from docs/source/contents/metrics/operational.md
rename to docs/metrics/operational.md
diff --git a/docs/source/contents/metrics/usage.md b/docs/metrics/usage.md
similarity index 100%
rename from docs/source/contents/metrics/usage.md
rename to docs/metrics/usage.md
diff --git a/docs/source/contents/models/index.md b/docs/models/index.md
similarity index 100%
rename from docs/source/contents/models/index.md
rename to docs/models/index.md
diff --git a/docs/source/contents/models/inference-artifacts/index.md b/docs/models/inference-artifacts/index.md
similarity index 100%
rename from docs/source/contents/models/inference-artifacts/index.md
rename to docs/models/inference-artifacts/index.md
diff --git a/docs/source/contents/models/mms/mms.md b/docs/models/mms/mms.md
similarity index 100%
rename from docs/source/contents/models/mms/mms.md
rename to docs/models/mms/mms.md
diff --git a/docs/source/contents/models/mms/mms.png b/docs/models/mms/mms.png
similarity index 100%
rename from docs/source/contents/models/mms/mms.png
rename to docs/models/mms/mms.png
diff --git a/docs/source/contents/models/mms/overcommit.png b/docs/models/mms/overcommit.png
similarity index 100%
rename from docs/source/contents/models/mms/overcommit.png
rename to docs/models/mms/overcommit.png
diff --git a/docs/source/contents/models/parameterized-models/index.md b/docs/models/parameterized-models/index.md
similarity index 100%
rename from docs/source/contents/models/parameterized-models/index.md
rename to docs/models/parameterized-models/index.md
diff --git a/docs/source/contents/models/parameterized-models/pandasquery.md b/docs/models/parameterized-models/pandasquery.md
similarity index 100%
rename from docs/source/contents/models/parameterized-models/pandasquery.md
rename to docs/models/parameterized-models/pandasquery.md
diff --git a/docs/source/contents/models/rclone/index.md b/docs/models/rclone/index.md
similarity index 100%
rename from docs/source/contents/models/rclone/index.md
rename to docs/models/rclone/index.md
diff --git a/docs/source/contents/outlier/index.md b/docs/outlier/index.md
similarity index 100%
rename from docs/source/contents/outlier/index.md
rename to docs/outlier/index.md
diff --git a/docs/source/contents/performance-tests/index.md b/docs/performance-tests/index.md
similarity index 100%
rename from docs/source/contents/performance-tests/index.md
rename to docs/performance-tests/index.md
diff --git a/docs/source/contents/pipelines/index.md b/docs/pipelines/index.md
similarity index 100%
rename from docs/source/contents/pipelines/index.md
rename to docs/pipelines/index.md
diff --git a/docs/source/contents/pipelines/jaeger-tracing.png b/docs/pipelines/jaeger-tracing.png
similarity index 100%
rename from docs/source/contents/pipelines/jaeger-tracing.png
rename to docs/pipelines/jaeger-tracing.png
diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt
deleted file mode 100644
index 90168c646f..0000000000
--- a/docs/requirements-docs.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-jupyter-book==0.6.5
-linkify-it-py==1.0.1
-myst-parser==1.0.0
-Sphinx==6.2.1
-sphinx-autobuild==2021.3.14
-sphinx-copybutton==0.5.0
-sphinx-jinja2-compat==0.2.0
-sphinx-material==0.0.32
-sphinx-notfound-page==0.7.1
-sphinx-panels==0.4.1
-sphinx-tabs==3.4.1
-sphinx-togglebutton==0.3.0
-sphinx-toolbox==3.2.0
-sphinxcontrib-applehelp==1.0.2
-sphinxcontrib-devhelp==1.0.2
-sphinxcontrib-htmlhelp==2.0.1
-sphinxcontrib-jsmath==1.0.1
-sphinxcontrib-mermaid==0.8.1
-sphinxcontrib-qthelp==1.0.3
-sphinxcontrib-serializinghtml==1.1.5
-sphinxcontrib-youtube==1.2.0
\ No newline at end of file
diff --git a/docs/source/contents/servers/index.md b/docs/servers/index.md
similarity index 100%
rename from docs/source/contents/servers/index.md
rename to docs/servers/index.md
diff --git a/docs/source/_static/versions.json b/docs/source/_static/versions.json
deleted file mode 100644
index 25e0f37996..0000000000
--- a/docs/source/_static/versions.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "master": "latest"
-}
diff --git a/docs/source/conf.py b/docs/source/conf.py
deleted file mode 100644
index 21518d0aa1..0000000000
--- a/docs/source/conf.py
+++ /dev/null
@@ -1,184 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-# import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
-
-
-# -- Project information -----------------------------------------------------
-
-project = 'Seldon Core v2'
-copyright = '2024 Seldon Technologies Ltd.'
-author = "Seldon Technologies Ltd"
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    'sphinxcontrib.mermaid',
-    'myst_parser',
-    'sphinx_panels',
-    'sphinx_tabs.tabs',
-    'notfound.extension',
-    'sphinx_toolbox.collapse',
-    'sphinx_copybutton',
-    'sphinx_togglebutton',
-    'sphinxcontrib.youtube'
-]
-
-sphinx_tabs_disable_tab_closing = True
-myst_enable_extensions = [
-    "amsmath",
-    "colon_fence",
-    "deflist",
-    "dollarmath",
-    "html_admonition",
-    "html_image",
-    "linkify",
-    "replacements",
-    "smartquotes",
-    "substitution",
-    "tasklist",
-]
-
-myst_heading_anchors = 6;
-
-source_suffix = ['.rst', '.md']
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = []
-
-notfound_context = {
-    'title': 'Not found',
-    'body': '<h1>Sorry. Seldon Core v2 page was not found!</h1><br><h4>Please use the search bar above to find what you were looking for.</h4>',
-}
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'sphinx_material'
-
-# Set link name generated in the top bar.
-html_title = 'Seldon Core v2'
-
-# Material theme options (see theme.conf for more information)
-html_theme_options = {
-
-    # Set the name of the project to appear in the navigation.
-    'nav_title': 'Seldon Core v2',
-
-    # Set you GA account ID to enable tracking
-    'google_analytics_account': 'UA-54780881-6',
-
-    # Specify a base_url used to generate sitemap.xml. If not
-    # specified, then no sitemap will be built.
-    "base_url": "https://docs.seldon.io/projects/seldon-core/",
-
-    # Set the color and the accent color
-    'color_primary': 'indigo',
-    'color_accent': 'teal',
-
-    # Set the repo location to get a badge with stats
-    "repo_url": "https://github.com/SeldonIO/seldon-core/",
-    'repo_name': 'Seldon Core',
-
-    # Visible levels of the global TOC; -1 means unlimited
-    'globaltoc_depth': 4,
-    # If False, expand all TOC entries
-    'globaltoc_collapse': True,
-    # If True, show hidden TOC entries
-    'globaltoc_includehidden': False,
-
-    # "logo_icon": "&#xe869",
-
-    "nav_links": [
-        {
-            "href": "/",
-            "internal": False,
-            "title": "🚀 Our Other Projects & Products:",
-        },
-        {
-            "href": "https://docs.seldon.io/projects/alibi/en/stable/",
-            "internal": False,
-            "title": "Alibi Explain",
-        },
-        {
-            "href": "https://docs.seldon.io/projects/alibi-detect/en/stable/",
-            "internal": False,
-            "title": "Alibi Detect",
-        },
-        {
-            "href": "https://mlserver.readthedocs.io/en/latest/",
-            "internal": False,
-            "title": "MLServer",
-        },
-        {
-            "href": "https://tempo.readthedocs.io/en/latest/",
-            "internal": False,
-            "title": "Tempo SDK",
-        },
-        {
-            "href": "https://deploy.seldon.io",
-            "internal": False,
-            "title": "Seldon Enterprise Platform",
-        },
-        {
-            "href": "https://github.com/SeldonIO/seldon-deploy-sdk#seldon-deploy-sdk",
-            "internal": False,
-            "title": "Seldon Enterprise Platform SDK",
-        },
-    ],
-
-    "heroes": {
-        "index": "Seldon Core v2 Documentation"
-    },
-
-    "version_dropdown": False,
-    "master_doc": False,
-    "version_json": "_static/versions.json"
-}
-
-html_logo = '_static/images/logo.svg'
-html_favicon = '_static/images/favicon.ico'
-html_show_sourcelink = False
-html_sidebars = {
-    "**": ["globaltoc.html", "localtoc.html", "searchbox.html"],
-    "index": ["searchbox.html"]
-}
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-
-# Hack to make substitutions work in code-blocks https://github.com/sphinx-doc/sphinx/issues/4054#issuecomment-329097229
-
-def ultimateReplace(app, docname, source):
-    result = source[0]
-    for key in app.config.ultimate_replacements:
-        result = result.replace(key, app.config.ultimate_replacements[key])
-    source[0] = result
-
-ultimate_replacements = {
-    # "{{ seldon-core-artifact-version }}": myst_substitutions["seldon-core-artifact-version"],
-}
-
-def setup(app):
-    app.add_config_value('ultimate_replacements', {}, True)
-    app.connect('source-read', ultimateReplace)
diff --git a/docs/source/contents/tutorials/index.md b/docs/tutorials/index.md
similarity index 100%
rename from docs/source/contents/tutorials/index.md
rename to docs/tutorials/index.md
diff --git a/docs/source/contents/tutorials/workflow.png b/docs/tutorials/workflow.png
similarity index 100%
rename from docs/source/contents/tutorials/workflow.png
rename to docs/tutorials/workflow.png
diff --git a/docs/source/contents/upgrading/index.md b/docs/upgrading/index.md
similarity index 100%
rename from docs/source/contents/upgrading/index.md
rename to docs/upgrading/index.md

From 669bbf1af21883f749f488f5c1be9f145f2666c0 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 16 Jul 2024 17:44:04 +0800
Subject: [PATCH 02/32] APIs section completed

---
 docs/apis/README.md                         |   7 +
 docs/apis/index.md                          |  16 -
 docs/apis/inference/{index.md => README.md} |  13 +-
 docs/apis/inference/v2.md                   | 958 ++++++++++----------
 docs/apis/internal/README.md                |   6 +
 docs/apis/internal/agent.md                 | 112 ++-
 docs/apis/internal/chainer.md               |  81 +-
 docs/apis/internal/index.md                 |  14 -
 docs/apis/scheduler/README.md               | 423 +++++++++
 docs/apis/scheduler/index.md                |  12 -
 10 files changed, 1109 insertions(+), 533 deletions(-)
 create mode 100644 docs/apis/README.md
 delete mode 100644 docs/apis/index.md
 rename docs/apis/inference/{index.md => README.md} (62%)
 create mode 100644 docs/apis/internal/README.md
 delete mode 100644 docs/apis/internal/index.md
 create mode 100644 docs/apis/scheduler/README.md
 delete mode 100644 docs/apis/scheduler/index.md

diff --git a/docs/apis/README.md b/docs/apis/README.md
new file mode 100644
index 0000000000..7eb44e82ef
--- /dev/null
+++ b/docs/apis/README.md
@@ -0,0 +1,7 @@
+# APIs
+
+Seldon provides APIs for management and inference.
+
+* [API for inference](./inference/README.md)
+* [Scheduler API for management](./scheduler/README.md) (Advanced)
+* [Internal APIs](./internal/README.md) (Reference)
diff --git a/docs/apis/index.md b/docs/apis/index.md
deleted file mode 100644
index e8d59bc53b..0000000000
--- a/docs/apis/index.md
+++ /dev/null
@@ -1,16 +0,0 @@
-# APIs
-
-Seldon provides APIs for management and inference.
-
- * [API for inference](./inference/index.md)
- * [Scheduler API for management](./scheduler/index.md) (Advanced)
- * [Internal APIs](./internal/index.md) (Reference)
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-inference/index.md
-scheduler/index.md
-internal/index.md
-```
\ No newline at end of file
diff --git a/docs/apis/inference/index.md b/docs/apis/inference/README.md
similarity index 62%
rename from docs/apis/inference/index.md
rename to docs/apis/inference/README.md
index 3489a06c78..e30c5d2eca 100644
--- a/docs/apis/inference/index.md
+++ b/docs/apis/inference/README.md
@@ -2,17 +2,6 @@
 
 Seldon inference servers must respect the following API specification.
 
- * [Seldon, KServe, NVIDIA V2 Inference API Spec](./v2.md)
+* [Seldon, KServe, NVIDIA V2 Inference API Spec](./v2.md)
 
 In future, Seldon may provide extensions for use with Pipelines, Experiments and Explainers.
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-v2.md
-```
-
-
-
-
diff --git a/docs/apis/inference/v2.md b/docs/apis/inference/v2.md
index 6c0c210b85..b4f52c6031 100644
--- a/docs/apis/inference/v2.md
+++ b/docs/apis/inference/v2.md
@@ -39,28 +39,28 @@ the server will choose a version based on its own policies).
 
 **Health:**
 
-```
- GET v2/health/live
- GET v2/health/ready
- GET v2/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]/ready
+```md
+GET v2/health/live
+GET v2/health/ready
+GET v2/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]/ready
 ```
 
 **Server Metadata:**
 
-```
- GET v2
+```md
+GET v2
 ```
 
 **Model Metadata:**
 
-```
- GET v2/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]
+```md
+GET v2/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]
 ```
 
 **Inference:**
 
-```
- POST v2/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]/infer
+```md
+POST v2/models/${MODEL_NAME}[/versions/${MODEL_VERSION}]/infer
 ```
 
 ### Health
@@ -106,13 +106,13 @@ A successful server metadata request is indicated by a 200 HTTP status
 code. The server metadata response object, identified as
 *$metadata_server_response*, is returned in the HTTP body.
 
-```
-    $metadata_server_response =
-    {
-      "name" : $string,
-      "version" : $string,
-      "extensions" : [ $string, ... ]
-    }
+```md
+$metadata_server_response =
+{
+  "name" : $string,
+  "version" : $string,
+  "extensions" : [ $string, ... ]
+}
 ```
 
 * “name” : A descriptive name for the server.
@@ -127,11 +127,11 @@ A failed server metadata request must be indicated by an HTTP error
 status (typically 400). The HTTP body must contain the
 *$metadata_server_error_response* object.
 
-```
-    $metadata_server_error_response =
-    {
-      "error": $string
-    }
+```md
+$metadata_server_error_response =
+{
+  "error": $string
+}
 ```
 
 * “error” : The descriptive message for the error.
@@ -155,15 +155,15 @@ code. The metadata response object, identified as
 *$metadata_model_response*, is returned in the HTTP body for every
 successful model metadata request.
 
-```
-    $metadata_model_response =
-    {
-      "name" : $string,
-      "versions" : [ $string, ... ] #optional,
-      "platform" : $string,
-      "inputs" : [ $metadata_tensor, ... ],
-      "outputs" : [ $metadata_tensor, ... ]
-    }
+```md
+$metadata_model_response =
+{
+  "name" : $string,
+  "versions" : [ $string, ... ] #optional,
+  "platform" : $string,
+  "inputs" : [ $metadata_tensor, ... ],
+  "outputs" : [ $metadata_tensor, ... ]
+}
 ```
 
 * “name” : The name of the model.
@@ -179,13 +179,13 @@ successful model metadata request.
 Each model input and output tensors’ metadata is described with a
 *$metadata_tensor object*.
 
-```
-    $metadata_tensor =
-    {
-      "name" : $string,
-      "datatype" : $string,
-      "shape" : [ $number, ... ]
-    }
+```md
+$metadata_tensor =
+{
+  "name" : $string,
+  "datatype" : $string,
+  "shape" : [ $number, ... ]
+}
 ```
 
 * “name” : The name of the tensor.
@@ -200,11 +200,11 @@ A failed model metadata request must be indicated by an HTTP error
 status (typically 400). The HTTP body must contain the
 *$metadata_model_error_response* object.
 
-```
-    $metadata_model_error_response =
-    {
-      "error": $string
-    }
+```md
+$metadata_model_error_response =
+{
+  "error": $string
+}
 ```
 
 * “error” : The descriptive message for the error.
@@ -222,78 +222,78 @@ example HTTP/REST requests and responses.
 
 #### Inference Request JSON Object
 
-The inference request object, identified as *$inference_request*, is
+The inference request object, identified as `$inference_request`, is
 required in the HTTP body of the POST request. The model name and
 (optionally) version must be available in the URL. If a version is not
 provided the server may choose a version based on its own policies or
 return an error.
 
-```
-    $inference_request =
-    {
-      "id" : $string #optional,
-      "parameters" : $parameters #optional,
-      "inputs" : [ $request_input, ... ],
-      "outputs" : [ $request_output, ... ] #optional
-    }
+```md
+$inference_request =
+{
+  "id" : $string #optional,
+  "parameters" : $parameters #optional,
+  "inputs" : [ $request_input, ... ],
+  "outputs" : [ $request_output, ... ] #optional
+}
 ```
 
-* "id" : An identifier for this request. Optional, but if specified
+* `id` : An identifier for this request. Optional, but if specified
   this identifier must be returned in the response.
-* "parameters" : An object containing zero or more parameters for this
+* `parameters` : An object containing zero or more parameters for this
   inference request expressed as key/value pairs. See
   [Parameters](#parameters) for more information.
-* "inputs" : The input tensors. Each input is described using the
+* `inputs` : The input tensors. Each input is described using the
   `$request_input` schema defined in [Request Input](#request-input).
-* "outputs" : The output tensors requested for this inference. Each
+* `outputs` : The output tensors requested for this inference. Each
   requested output is described using the `$request_output` schema
   defined in [Request Output](#request-output). Optional, if not
   specified all outputs produced by the model will be returned using
-  default *$request_output* settings.
+  default `$request_output` settings.
 
 ##### Request Input
 
-The *$request_input* JSON describes an input to the model. If the
+The `$request_input` JSON describes an input to the model. If the
 input is batched, the shape and data must represent the full shape and
 contents of the entire batch.
 
-```
-    $request_input =
-    {
-      "name" : $string,
-      "shape" : [ $number, ... ],
-      "datatype"  : $string,
-      "parameters" : $parameters #optional,
-      "data" : $tensor_data
-    }
+```md
+$request_input =
+{
+  "name" : $string,
+  "shape" : [ $number, ... ],
+  "datatype"  : $string,
+  "parameters" : $parameters #optional,
+  "data" : $tensor_data
+}
 ```
 
-* "name" : The name of the input tensor.
-* "shape" : The shape of the input tensor. Each dimension must be an
+* `"name"`: The name of the input tensor.
+* `"shape"`: The shape of the input tensor. Each dimension must be an
   integer representable as an unsigned 64-bit integer value.
-* "datatype" : The data-type of the input tensor elements as defined
+* `"datatype"`: The data-type of the input tensor elements as defined
   in [Tensor Data Types](#tensor-data-types).
-* "parameters" : An object containing zero or more parameters for this
+* `"parameters"`: An object containing zero or more parameters for this
   input expressed as key/value pairs. See [Parameters](#parameters)
   for more information.
-* “data”: The contents of the tensor. See [Tensor Data](#tensor-data)
+* `“data”`: The contents of the tensor. See [Tensor Data](#tensor-data)
   for more information.
 
 ##### Request Output
 
-The *$request_output* JSON is used to request which output tensors
+The `$request_output` JSON is used to request which output tensors
 should be returned from the model.
 
-```
-    $request_output =
-    {
-      "name" : $string,
-      "parameters" : $parameters #optional,
-    }
+```md
+$request_output =
+{
+  "name" : $string,
+  "parameters" : $parameters #optional,
+}
 ```
 
-* "name" : The name of the output tensor.
-* "parameters" : An object containing zero or more parameters for this
+* `"name"`: The name of the output tensor.
+* `"parameters"`: An object containing zero or more parameters for this
   output expressed as key/value pairs. See [Parameters](#parameters)
   for more information.
 
@@ -303,71 +303,71 @@ A successful inference request is indicated by a 200 HTTP status
 code. The inference response object, identified as
 *$inference_response*, is returned in the HTTP body.
 
-```
-    $inference_response =
-    {
-      "model_name" : $string,
-      "model_version" : $string #optional,
-      "id" : $string,
-      "parameters" : $parameters #optional,
-      "outputs" : [ $response_output, ... ]
-    }
+```md
+$inference_response =
+{
+  "model_name" : $string,
+  "model_version" : $string #optional,
+  "id" : $string,
+  "parameters" : $parameters #optional,
+  "outputs" : [ $response_output, ... ]
+}
 ```
 
-* "model_name" : The name of the model used for inference.
-* "model_version" : The specific model version used for
+* `"model_name"`: The name of the model used for inference.
+* `"model_version"`: The specific model version used for
   inference. Inference servers that do not implement versioning should
   not provide this field in the response.
-* "id" : The "id" identifier given in the request, if any.
-* "parameters" : An object containing zero or more parameters for this
+* `"id"`: The "id" identifier given in the request, if any.
+* `"parameters"`: An object containing zero or more parameters for this
   response expressed as key/value pairs. See [Parameters](#parameters)
   for more information.
-* "outputs" : The output tensors. Each output is described using the
-  $response_output schema defined in
+* `"outputs"`: The output tensors. Each output is described using the
+  `$response_output` schema defined in
   [Response Output](#response-output).
 
 ##### Response Output
 
-The *$response_output* JSON describes an output from the model. If the
+The `$response_output` JSON describes an output from the model. If the
 output is batched, the shape and data represents the full shape of the
 entire batch.
 
-```
-    $response_output =
-    {
-      "name" : $string,
-      "shape" : [ $number, ... ],
-      "datatype"  : $string,
-      "parameters" : $parameters #optional,
-      "data" : $tensor_data
-    }
+```md
+$response_output =
+{
+  "name" : $string,
+  "shape" : [ $number, ... ],
+  "datatype"  : $string,
+  "parameters" : $parameters #optional,
+  "data" : $tensor_data
+}
 ```
 
-* "name" : The name of the output tensor.
-* "shape" : The shape of the output tensor. Each dimension must be an
+* `"name"`: The name of the output tensor.
+* `"shape"`: The shape of the output tensor. Each dimension must be an
   integer representable as an unsigned 64-bit integer value.
-* "datatype" : The data-type of the output tensor elements as defined
+* `"datatype"`: The data-type of the output tensor elements as defined
   in [Tensor Data Types](#tensor-data-types).
-* "parameters" : An object containing zero or more parameters for this
+* `"parameters"`: An object containing zero or more parameters for this
   input expressed as key/value pairs. See [Parameters](#parameters)
   for more information.
-* “data”: The contents of the tensor. See [Tensor Data](#tensor-data)
+* `“data”`: The contents of the tensor. See [Tensor Data](#tensor-data)
   for more information.
 
 #### Inference Response JSON Error Object
 
 A failed inference request must be indicated by an HTTP error status
 (typically 400). The HTTP body must contain the
-*$inference_error_response* object.
+`$inference_error_response` object.
 
-```
-    $inference_error_response =
-    {
-      "error": <error message string>
-    }
+```md
+$inference_error_response =
+{
+  "error": <error message string>
+}
 ```
 
-* “error” : The descriptive message for the error.
+* `“error”`: The descriptive message for the error.
 
 #### Inference Request Examples
 
@@ -375,69 +375,69 @@ The following example shows an inference request to a model with two
 inputs and one output. The HTTP Content-Length header gives the size
 of the JSON object.
 
-```
-    POST /v2/models/mymodel/infer HTTP/1.1
-    Host: localhost:8000
-    Content-Type: application/json
-    Content-Length: <xx>
+```md
+POST /v2/models/mymodel/infer HTTP/1.1
+Host: localhost:8000
+Content-Type: application/json
+Content-Length: <xx>
+{
+  "id" : "42",
+  "inputs" : [
+    {
+      "name" : "input0",
+      "shape" : [ 2, 2 ],
+      "datatype" : "UINT32",
+      "data" : [ 1, 2, 3, 4 ]
+    },
+    {
+      "name" : "input1",
+      "shape" : [ 3 ],
+      "datatype" : "BOOL",
+      "data" : [ true ]
+    }
+  ],
+  "outputs" : [
     {
-      "id" : "42",
-      "inputs" : [
-        {
-          "name" : "input0",
-          "shape" : [ 2, 2 ],
-          "datatype" : "UINT32",
-          "data" : [ 1, 2, 3, 4 ]
-        },
-        {
-          "name" : "input1",
-          "shape" : [ 3 ],
-          "datatype" : "BOOL",
-          "data" : [ true ]
-        }
-      ],
-      "outputs" : [
-        {
-          "name" : "output0"
-        }
-      ]
+      "name" : "output0"
     }
+  ]
+}
 ```
 
 For the above request the inference server must return the “output0”
 output tensor. Assuming the model returns a [ 3, 2 ] tensor of data
 type FP32 the following response would be returned.
 
-```
-    HTTP/1.1 200 OK
-    Content-Type: application/json
-    Content-Length: <yy>
+```md
+HTTP/1.1 200 OK
+Content-Type: application/json
+Content-Length: <yy>
+{
+  "id" : "42"
+  "outputs" : [
     {
-      "id" : "42"
-      "outputs" : [
-        {
-          "name" : "output0",
-          "shape" : [ 3, 2 ],
-          "datatype"  : "FP32",
-          "data" : [ 1.0, 1.1, 2.0, 2.1, 3.0, 3.1 ]
-        }
-      ]
+      "name" : "output0",
+      "shape" : [ 3, 2 ],
+      "datatype"  : "FP32",
+      "data" : [ 1.0, 1.1, 2.0, 2.1, 3.0, 3.1 ]
     }
+  ]
+}
 ```
 
 ### Parameters
 
-The *$parameters* JSON describes zero or more “name”/”value” pairs,
-where the “name” is the name of the parameter and the “value” is a
-$string, $number, or $boolean.
+The `$parameters` JSON describes zero or more `“name”`/`”value”` pairs,
+where the `“name”` is the name of the parameter and the `“value”` is a
+`$string`, `$number`, or `$boolean`.
 
-```
-    $parameters =
-    {
-      $parameter, ...
-    }
+```md
+$parameters =
+{
+  $parameter, ...
+}
 
-    $parameter = $string : $string | $number | $boolean
+$parameter = $string : $string | $number | $boolean
 ```
 
 Currently no parameters are defined. As required a future proposal may
@@ -463,17 +463,20 @@ typically the programmatic support to create the fp16 representation
 for a JSON number.
 
 For example, the 2-dimensional matrix:
-
-    [ 1 2
-      4 5 ]
+```md
+[ 1 2
+  4 5 ]
+```
 
 Can be represented in its natural format as:
-
-    "data" : [ [ 1, 2 ], [ 4, 5 ] ]
+```md
+"data" : [ [ 1, 2 ], [ 4, 5 ] ]
+```
 
 Or in a flattened one-dimensional representation:
-
-    "data" : [ 1, 2, 4, 5 ]
+```md
+"data" : [ 1, 2, 4, 5 ]
+```
 
 ## GRPC
 
@@ -484,30 +487,31 @@ health, metadata, and inference APIs described in this section.
 All strings in all contexts are case-sensitive.
 
 The GRPC definition of the service is:
+```md
+//
+// Inference Server GRPC endpoints.
+//
+service GRPCInferenceService
+{
+  // Check liveness of the inference server.
+  rpc ServerLive(ServerLiveRequest) returns (ServerLiveResponse) {}
 
-    //
-    // Inference Server GRPC endpoints.
-    //
-    service GRPCInferenceService
-    {
-      // Check liveness of the inference server.
-      rpc ServerLive(ServerLiveRequest) returns (ServerLiveResponse) {}
-
-      // Check readiness of the inference server.
-      rpc ServerReady(ServerReadyRequest) returns (ServerReadyResponse) {}
+  // Check readiness of the inference server.
+  rpc ServerReady(ServerReadyRequest) returns (ServerReadyResponse) {}
 
-      // Check readiness of a model in the inference server.
-      rpc ModelReady(ModelReadyRequest) returns (ModelReadyResponse) {}
+  // Check readiness of a model in the inference server.
+  rpc ModelReady(ModelReadyRequest) returns (ModelReadyResponse) {}
 
-      // Get server metadata.
-      rpc ServerMetadata(ServerMetadataRequest) returns (ServerMetadataResponse) {}
+  // Get server metadata.
+  rpc ServerMetadata(ServerMetadataRequest) returns (ServerMetadataResponse) {}
 
-      // Get model metadata.
-      rpc ModelMetadata(ModelMetadataRequest) returns (ModelMetadataResponse) {}
+  // Get model metadata.
+  rpc ModelMetadata(ModelMetadataRequest) returns (ModelMetadataResponse) {}
 
-      // Perform inference using a specific model.
-      rpc ModelInfer(ModelInferRequest) returns (ModelInferResponse) {}
-    }
+  // Perform inference using a specific model.
+  rpc ModelInfer(ModelInferRequest) returns (ModelInferResponse) {}
+}
+```
 
 ### Health
 
@@ -521,48 +525,51 @@ indicates success and other codes indicate failure.
 The ServerLive API indicates if the inference server is able to
 receive and respond to metadata and inference requests. The request
 and response messages for ServerLive are:
+```md
+message ServerLiveRequest {}
 
-    message ServerLiveRequest {}
-
-    message ServerLiveResponse
-    {
-      // True if the inference server is live, false if not live.
-      bool live = 1;
-    }
+message ServerLiveResponse
+{
+  // True if the inference server is live, false if not live.
+  bool live = 1;
+}
+```
 
 #### Server Ready
 
 The ServerReady API indicates if the server is ready for
 inferencing. The request and response messages for ServerReady are:
+```md
+message ServerReadyRequest {}
 
-    message ServerReadyRequest {}
-
-    message ServerReadyResponse
-    {
-      // True if the inference server is ready, false if not ready.
-      bool ready = 1;
-    }
+message ServerReadyResponse
+{
+  // True if the inference server is ready, false if not ready.
+  bool ready = 1;
+}
+```
 
 #### Model Ready
 
 The ModelReady API indicates if a specific model is ready for
 inferencing. The request and response messages for ModelReady are:
+```md
+message ModelReadyRequest
+{
+  // The name of the model to check for readiness.
+  string name = 1;
 
-    message ModelReadyRequest
-    {
-      // The name of the model to check for readiness.
-      string name = 1;
-
-      // The version of the model to check for readiness. If not given the
-      // server will choose a version based on the model and internal policy.
-      string version = 2;
-    }
+  // The version of the model to check for readiness. If not given the
+  // server will choose a version based on the model and internal policy.
+  string version = 2;
+}
 
-    message ModelReadyResponse
-    {
-      // True if the model is ready, false if not ready.
-      bool ready = 1;
-    }
+message ModelReadyResponse
+{
+  // True if the model is ready, false if not ready.
+  bool ready = 1;
+}
+```
 
 ### Server Metadata
 
@@ -570,20 +577,21 @@ The ServerMetadata API provides information about the server. Errors
 are indicated by the google.rpc.Status returned for the request. The
 OK code indicates success and other codes indicate failure. The
 request and response messages for ServerMetadata are:
+```md
+message ServerMetadataRequest {}
 
-    message ServerMetadataRequest {}
-
-    message ServerMetadataResponse
-    {
-      // The server name.
-      string name = 1;
+message ServerMetadataResponse
+{
+  // The server name.
+  string name = 1;
 
-      // The server version.
-      string version = 2;
+  // The server version.
+  string version = 2;
 
-      // The extensions supported by the server.
-      repeated string extensions = 3;
-    }
+  // The extensions supported by the server.
+  repeated string extensions = 3;
+}
+```
 
 ### Model Metadata
 
@@ -592,47 +600,49 @@ are indicated by the google.rpc.Status returned for the request. The
 OK code indicates success and other codes indicate failure. The
 request and response messages for ModelMetadata are:
 
-    message ModelMetadataRequest
-    {
-      // The name of the model.
-      string name = 1;
+```md
+message ModelMetadataRequest
+{
+  // The name of the model.
+  string name = 1;
 
-      // The version of the model to check for readiness. If not given the
-      // server will choose a version based on the model and internal policy.
-      string version = 2;
-    }
+  // The version of the model to check for readiness. If not given the
+  // server will choose a version based on the model and internal policy.
+  string version = 2;
+}
 
-    message ModelMetadataResponse
-    {
-      // Metadata for a tensor.
-      message TensorMetadata
-      {
-        // The tensor name.
-        string name = 1;
+message ModelMetadataResponse
+{
+  // Metadata for a tensor.
+  message TensorMetadata
+  {
+    // The tensor name.
+    string name = 1;
 
-        // The tensor data type.
-        string datatype = 2;
+    // The tensor data type.
+    string datatype = 2;
 
-        // The tensor shape. A variable-size dimension is represented
-        // by a -1 value.
-        repeated int64 shape = 3;
-      }
+    // The tensor shape. A variable-size dimension is represented
+    // by a -1 value.
+    repeated int64 shape = 3;
+  }
 
-      // The model name.
-      string name = 1;
+  // The model name.
+  string name = 1;
 
-      // The versions of the model available on the server.
-      repeated string versions = 2;
+  // The versions of the model available on the server.
+  repeated string versions = 2;
 
-      // The model's platform. See Platforms.
-      string platform = 3;
+  // The model's platform. See Platforms.
+  string platform = 3;
 
-      // The model's inputs.
-      repeated TensorMetadata inputs = 4;
+  // The model's inputs.
+  repeated TensorMetadata inputs = 4;
 
-      // The model's outputs.
-      repeated TensorMetadata outputs = 5;
-    }
+  // The model's outputs.
+  repeated TensorMetadata outputs = 5;
+}
+```
 
 ### Inference
 
@@ -640,163 +650,165 @@ The ModelInfer API performs inference using the specified
 model. Errors are indicated by the google.rpc.Status returned for the
 request. The OK code indicates success and other codes indicate
 failure. The request and response messages for ModelInfer are:
-
-    message ModelInferRequest
-    {
-      // An input tensor for an inference request.
-      message InferInputTensor
-      {
-        // The tensor name.
-        string name = 1;
-
-        // The tensor data type.
-        string datatype = 2;
-
-        // The tensor shape.
-        repeated int64 shape = 3;
-
-        // Optional inference input tensor parameters.
-        map<string, InferParameter> parameters = 4;
-
-        // The tensor contents using a data-type format. This field must
-        // not be specified if "raw" tensor contents are being used for
-        // the inference request.
-        InferTensorContents contents = 5;
-      }
-
-      // An output tensor requested for an inference request.
-      message InferRequestedOutputTensor
-      {
-        // The tensor name.
-        string name = 1;
-
-        // Optional requested output tensor parameters.
-        map<string, InferParameter> parameters = 2;
-      }
-
-      // The name of the model to use for inferencing.
-      string model_name = 1;
-
-      // The version of the model to use for inference. If not given the
-      // server will choose a version based on the model and internal policy.
-      string model_version = 2;
-
-      // Optional identifier for the request. If specified will be
-      // returned in the response.
-      string id = 3;
-
-      // Optional inference parameters.
-      map<string, InferParameter> parameters = 4;
-
-      // The input tensors for the inference.
-      repeated InferInputTensor inputs = 5;
-
-      // The requested output tensors for the inference. Optional, if not
-      // specified all outputs produced by the model will be returned.
-      repeated InferRequestedOutputTensor outputs = 6;
-
-      // The data contained in an input tensor can be represented in "raw"
-      // bytes form or in the repeated type that matches the tensor's data
-      // type. To use the raw representation 'raw_input_contents' must be
-      // initialized with data for each tensor in the same order as
-      // 'inputs'. For each tensor, the size of this content must match
-      // what is expected by the tensor's shape and data type. The raw
-      // data must be the flattened, one-dimensional, row-major order of
-      // the tensor elements without any stride or padding between the
-      // elements. Note that the FP16 data type must be represented as raw
-      // content as there is no specific data type for a 16-bit float
-      // type.
-      //
-      // If this field is specified then InferInputTensor::contents must
-      // not be specified for any input tensor.
-      repeated bytes raw_input_contents = 7;
-    }
-
-    message ModelInferResponse
-    {
-      // An output tensor returned for an inference request.
-      message InferOutputTensor
-      {
-        // The tensor name.
-        string name = 1;
-
-        // The tensor data type.
-        string datatype = 2;
-
-        // The tensor shape.
-        repeated int64 shape = 3;
-
-        // Optional output tensor parameters.
-        map<string, InferParameter> parameters = 4;
-
-        // The tensor contents using a data-type format. This field must
-        // not be specified if "raw" tensor contents are being used for
-        // the inference response.
-        InferTensorContents contents = 5;
-      }
-
-      // The name of the model used for inference.
-      string model_name = 1;
-
-      // The version of the model used for inference.
-      string model_version = 2;
-
-      // The id of the inference request if one was specified.
-      string id = 3;
-
-      // Optional inference response parameters.
-      map<string, InferParameter> parameters = 4;
-
-      // The output tensors holding inference results.
-      repeated InferOutputTensor outputs = 5;
-
-      // The data contained in an output tensor can be represented in
-      // "raw" bytes form or in the repeated type that matches the
-      // tensor's data type. To use the raw representation 'raw_output_contents'
-      // must be initialized with data for each tensor in the same order as
-      // 'outputs'. For each tensor, the size of this content must match
-      // what is expected by the tensor's shape and data type. The raw
-      // data must be the flattened, one-dimensional, row-major order of
-      // the tensor elements without any stride or padding between the
-      // elements. Note that the FP16 data type must be represented as raw
-      // content as there is no specific data type for a 16-bit float
-      // type.
-      //
-      // If this field is specified then InferOutputTensor::contents must
-      // not be specified for any output tensor.
-      repeated bytes raw_output_contents = 6;
-    }
+```md
+message ModelInferRequest
+{
+  // An input tensor for an inference request.
+  message InferInputTensor
+  {
+    // The tensor name.
+    string name = 1;
+
+    // The tensor data type.
+    string datatype = 2;
+
+    // The tensor shape.
+    repeated int64 shape = 3;
+
+    // Optional inference input tensor parameters.
+    map<string, InferParameter> parameters = 4;
+
+    // The tensor contents using a data-type format. This field must
+    // not be specified if "raw" tensor contents are being used for
+    // the inference request.
+    InferTensorContents contents = 5;
+  }
+
+  // An output tensor requested for an inference request.
+  message InferRequestedOutputTensor
+  {
+    // The tensor name.
+    string name = 1;
+
+    // Optional requested output tensor parameters.
+    map<string, InferParameter> parameters = 2;
+  }
+
+  // The name of the model to use for inferencing.
+  string model_name = 1;
+
+  // The version of the model to use for inference. If not given the
+  // server will choose a version based on the model and internal policy.
+  string model_version = 2;
+
+  // Optional identifier for the request. If specified will be
+  // returned in the response.
+  string id = 3;
+
+  // Optional inference parameters.
+  map<string, InferParameter> parameters = 4;
+
+  // The input tensors for the inference.
+  repeated InferInputTensor inputs = 5;
+
+  // The requested output tensors for the inference. Optional, if not
+  // specified all outputs produced by the model will be returned.
+  repeated InferRequestedOutputTensor outputs = 6;
+
+  // The data contained in an input tensor can be represented in "raw"
+  // bytes form or in the repeated type that matches the tensor's data
+  // type. To use the raw representation 'raw_input_contents' must be
+  // initialized with data for each tensor in the same order as
+  // 'inputs'. For each tensor, the size of this content must match
+  // what is expected by the tensor's shape and data type. The raw
+  // data must be the flattened, one-dimensional, row-major order of
+  // the tensor elements without any stride or padding between the
+  // elements. Note that the FP16 data type must be represented as raw
+  // content as there is no specific data type for a 16-bit float
+  // type.
+  //
+  // If this field is specified then InferInputTensor::contents must
+  // not be specified for any input tensor.
+  repeated bytes raw_input_contents = 7;
+}
+
+message ModelInferResponse
+{
+  // An output tensor returned for an inference request.
+  message InferOutputTensor
+  {
+    // The tensor name.
+    string name = 1;
+
+    // The tensor data type.
+    string datatype = 2;
+
+    // The tensor shape.
+    repeated int64 shape = 3;
+
+    // Optional output tensor parameters.
+    map<string, InferParameter> parameters = 4;
+
+    // The tensor contents using a data-type format. This field must
+    // not be specified if "raw" tensor contents are being used for
+    // the inference response.
+    InferTensorContents contents = 5;
+  }
+
+  // The name of the model used for inference.
+  string model_name = 1;
+
+  // The version of the model used for inference.
+  string model_version = 2;
+
+  // The id of the inference request if one was specified.
+  string id = 3;
+
+  // Optional inference response parameters.
+  map<string, InferParameter> parameters = 4;
+
+  // The output tensors holding inference results.
+  repeated InferOutputTensor outputs = 5;
+
+  // The data contained in an output tensor can be represented in
+  // "raw" bytes form or in the repeated type that matches the
+  // tensor's data type. To use the raw representation 'raw_output_contents'
+  // must be initialized with data for each tensor in the same order as
+  // 'outputs'. For each tensor, the size of this content must match
+  // what is expected by the tensor's shape and data type. The raw
+  // data must be the flattened, one-dimensional, row-major order of
+  // the tensor elements without any stride or padding between the
+  // elements. Note that the FP16 data type must be represented as raw
+  // content as there is no specific data type for a 16-bit float
+  // type.
+  //
+  // If this field is specified then InferOutputTensor::contents must
+  // not be specified for any output tensor.
+  repeated bytes raw_output_contents = 6;
+}
+```
 
 ### Parameters
 
-The Parameters message describes a “name”/”value” pair, where the
-“name” is the name of the parameter and the “value” is a boolean,
+The Parameters message describes a `“name”`/`”value”` pair, where the
+`“name”` is the name of the parameter and the `“value”` is a boolean,
 integer, or string corresponding to the parameter.
 
 Currently no parameters are defined. As required a future proposal may
 define one or more standard parameters to allow portable functionality
 across different inference servers. A server can implement
 server-specific parameters to provide non-standard capabilities.
-
-    //
-    // An inference parameter value.
-    //
-    message InferParameter
-    {
-      // The parameter value can be a string, an int64, a boolean
-      // or a message specific to a predefined parameter.
-      oneof parameter_choice
-      {
-        // A boolean parameter value.
-        bool bool_param = 1;
-
-        // An int64 parameter value.
-        int64 int64_param = 2;
-
-        // A string parameter value.
-        string string_param = 3;
-      }
-    }
+```md
+//
+// An inference parameter value.
+//
+message InferParameter
+{
+  // The parameter value can be a string, an int64, a boolean
+  // or a message specific to a predefined parameter.
+  oneof parameter_choice
+  {
+    // A boolean parameter value.
+    bool bool_param = 1;
+
+    // An int64 parameter value.
+    int64 int64_param = 2;
+
+    // A string parameter value.
+    string string_param = 3;
+  }
+}
+```
 
 ### Tensor Data
 
@@ -806,64 +818,65 @@ values must be given in "linear" order without any stride or padding
 between elements.
 
 Using a "raw" representation of tensors with
-ModelInferRequest::raw_input_contents and
-ModelInferResponse::raw_output_contents will typically allow higher
+`ModelInferRequest::raw_input_contents` and
+`ModelInferResponse::raw_output_contents` will typically allow higher
 performance due to the way protobuf allocation and reuse interacts
-with GRPC. For example, see https://github.com/grpc/grpc/issues/23231.
+with GRPC. For example, see [issue here](https://github.com/grpc/grpc/issues/23231).
 
-An alternative to the "raw" representation is to use
+An alternative to the `"raw"` representation is to use
 InferTensorContents to represent the tensor data in a format that
 matches the tensor's data type.
-
-    //
-    // The data contained in a tensor represented by the repeated type
-    // that matches the tensor's data type. Protobuf oneof is not used
-    // because oneofs cannot contain repeated fields.
-    //
-    message InferTensorContents
-    {
-      // Representation for BOOL data type. The size must match what is
-      // expected by the tensor's shape. The contents must be the flattened,
-      // one-dimensional, row-major order of the tensor elements.
-      repeated bool bool_contents = 1;
-
-      // Representation for INT8, INT16, and INT32 data types. The size
-      // must match what is expected by the tensor's shape. The contents
-      // must be the flattened, one-dimensional, row-major order of the
-      // tensor elements.
-      repeated int32 int_contents = 2;
-
-      // Representation for INT64 data types. The size must match what
-      // is expected by the tensor's shape. The contents must be the
-      // flattened, one-dimensional, row-major order of the tensor elements.
-      repeated int64 int64_contents = 3;
-
-      // Representation for UINT8, UINT16, and UINT32 data types. The size
-      // must match what is expected by the tensor's shape. The contents
-      // must be the flattened, one-dimensional, row-major order of the
-      // tensor elements.
-      repeated uint32 uint_contents = 4;
-
-      // Representation for UINT64 data types. The size must match what
-      // is expected by the tensor's shape. The contents must be the
-      // flattened, one-dimensional, row-major order of the tensor elements.
-      repeated uint64 uint64_contents = 5;
-
-      // Representation for FP32 data type. The size must match what is
-      // expected by the tensor's shape. The contents must be the flattened,
-      // one-dimensional, row-major order of the tensor elements.
-      repeated float fp32_contents = 6;
-
-      // Representation for FP64 data type. The size must match what is
-      // expected by the tensor's shape. The contents must be the flattened,
-      // one-dimensional, row-major order of the tensor elements.
-      repeated double fp64_contents = 7;
-
-      // Representation for BYTES data type. The size must match what is
-      // expected by the tensor's shape. The contents must be the flattened,
-      // one-dimensional, row-major order of the tensor elements.
-      repeated bytes bytes_contents = 8;
-    }
+```md
+//
+// The data contained in a tensor represented by the repeated type
+// that matches the tensor's data type. Protobuf oneof is not used
+// because oneofs cannot contain repeated fields.
+//
+message InferTensorContents
+{
+  // Representation for BOOL data type. The size must match what is
+  // expected by the tensor's shape. The contents must be the flattened,
+  // one-dimensional, row-major order of the tensor elements.
+  repeated bool bool_contents = 1;
+
+  // Representation for INT8, INT16, and INT32 data types. The size
+  // must match what is expected by the tensor's shape. The contents
+  // must be the flattened, one-dimensional, row-major order of the
+  // tensor elements.
+  repeated int32 int_contents = 2;
+
+  // Representation for INT64 data types. The size must match what
+  // is expected by the tensor's shape. The contents must be the
+  // flattened, one-dimensional, row-major order of the tensor elements.
+  repeated int64 int64_contents = 3;
+
+  // Representation for UINT8, UINT16, and UINT32 data types. The size
+  // must match what is expected by the tensor's shape. The contents
+  // must be the flattened, one-dimensional, row-major order of the
+  // tensor elements.
+  repeated uint32 uint_contents = 4;
+
+  // Representation for UINT64 data types. The size must match what
+  // is expected by the tensor's shape. The contents must be the
+  // flattened, one-dimensional, row-major order of the tensor elements.
+  repeated uint64 uint64_contents = 5;
+
+  // Representation for FP32 data type. The size must match what is
+  // expected by the tensor's shape. The contents must be the flattened,
+  // one-dimensional, row-major order of the tensor elements.
+  repeated float fp32_contents = 6;
+
+  // Representation for FP64 data type. The size must match what is
+  // expected by the tensor's shape. The contents must be the flattened,
+  // one-dimensional, row-major order of the tensor elements.
+  repeated double fp64_contents = 7;
+
+  // Representation for BYTES data type. The size must match what is
+  // expected by the tensor's shape. The contents must be the flattened,
+  // one-dimensional, row-major order of the tensor elements.
+  repeated bytes bytes_contents = 8;
+}
+```
 
 ## Platforms
 
@@ -873,15 +886,15 @@ backend. Platform is returned as part of the response to a
 proposed inference APIs are generic relative to the DL/ML framework
 used by a model and so a client does not need to know the platform of
 a given model to use the API. Platform names use the format
-“<project>_<format>”. The following platform names are allowed:
+`“<project>_<format>”`. The following platform names are allowed:
 
-* tensorrt_plan : A TensorRT model encoded as a serialized engine or “plan”.
-* tensorflow_graphdef : A TensorFlow model encoded as a GraphDef.
-* tensorflow_savedmodel : A TensorFlow model encoded as a SavedModel.
-* onnx_onnxv1 : A ONNX model encoded for ONNX Runtime.
-* pytorch_torchscript : A PyTorch model encoded as TorchScript.
-* mxnet_mxnet: An MXNet model
-* caffe2_netdef : A Caffe2 model encoded as a NetDef.
+* `tensorrt_plan`: A TensorRT model encoded as a serialized engine or `“plan”`.
+* `tensorflow_graphdef`: A TensorFlow model encoded as a GraphDef.
+* `tensorflow_savedmodel`: A TensorFlow model encoded as a SavedModel.
+* `onnx_onnxv1`: A ONNX model encoded for ONNX Runtime.
+* `pytorch_torchscript`: A PyTorch model encoded as TorchScript.
+* `mxnet_mxnet` An MXNet model
+* `caffe2_netdef`: A Caffe2 model encoded as a NetDef.
 
 ## Tensor Data Types
 
@@ -909,4 +922,3 @@ of each type, in bytes.
 ## References
 
 This document is based on the [KServe original](https://github.com/kserve/kserve/blob/master/docs/predict-api/v2/required_api.md) created during the lifetime of the KFServing project in Kubeflow by its various contributors including Seldon, NVIDIA, IBM, Bloomberg and others.
-
diff --git a/docs/apis/internal/README.md b/docs/apis/internal/README.md
new file mode 100644
index 0000000000..6f266dd688
--- /dev/null
+++ b/docs/apis/internal/README.md
@@ -0,0 +1,6 @@
+# Internal APIs
+
+Seldon provides some internal gRPC services it uses to manage components.
+
+* [Agent API](./agent.md)
+* [Chainer API](./chainer.md)
diff --git a/docs/apis/internal/agent.md b/docs/apis/internal/agent.md
index a5873af106..dc50fb1504 100644
--- a/docs/apis/internal/agent.md
+++ b/docs/apis/internal/agent.md
@@ -4,7 +4,113 @@ This API is for communication between the Seldon Scheduler and the Seldon Agent
 
 ## Proto Definition
 
-```{literalinclude} ../../../../../apis/mlops/agent/agent.proto 
-:language: proto
-```
+```proto
+syntax = "proto3";
+
+package seldon.mlops.agent;
+
+option go_package = "github.com/seldonio/seldon-core/apis/go/v2/mlops/agent";
+
+import "mlops/scheduler/scheduler.proto";
+
+// [START Messages]
+
+message ModelEventMessage {
+  string serverName = 1;
+  uint32 replicaIdx = 2;
+  string modelName = 3;
+  uint32 modelVersion = 4;
+  enum Event {
+      UNKNOWN_EVENT = 0;
+      LOAD_FAIL_MEMORY = 1;
+      LOADED = 2;
+      LOAD_FAILED = 3;
+      UNLOADED = 4;
+      UNLOAD_FAILED = 5;
+      REMOVED = 6; // unloaded and removed from local PVC
+      REMOVE_FAILED = 7;
+      RSYNC = 9; // Ask server for all models that need to be loaded
+      }
+  Event event = 5;
+  string message = 6;
+  uint64 availableMemoryBytes = 7;
+}
+
+message ModelEventResponse {
+
+}
+
+message ModelScalingTriggerMessage {
+  string serverName = 1;
+  uint32 replicaIdx = 2;
+  string modelName = 3;
+  uint32 modelVersion = 4;
+  enum Trigger {
+      SCALE_UP = 0;
+      SCALE_DOWN = 1;
+      }
+  Trigger trigger = 5;
+  uint32 amount = 6;  // number of replicas required
+  map<string,uint32> metrics = 7;  // optional metrics to expose to the scheduler
+}
+
+message ModelScalingTriggerResponse {
+
+}
 
+message AgentDrainRequest {
+  string serverName = 1;
+  uint32 replicaIdx = 2;
+}
+
+message AgentDrainResponse {
+  bool success = 1;
+}
+
+message AgentSubscribeRequest {
+  string serverName = 1;
+  bool shared = 2;
+  uint32 replicaIdx = 3;
+  ReplicaConfig replicaConfig = 4;
+  repeated ModelVersion loadedModels = 5;
+  uint64 availableMemoryBytes = 6;
+}
+
+message ReplicaConfig {
+  string inferenceSvc = 1; // inference DNS service name
+  int32 inferenceHttpPort = 2; // inference HTTP port
+  int32 inferenceGrpcPort = 3; // Inference grpc port
+  uint64 memoryBytes = 4; // The memory capacity of the server replica
+  repeated string capabilities = 5; // The list of capabilities of the server, e.g. sklearn, pytorch, xgboost, mlflow
+  uint32 overCommitPercentage = 6; // The percentage of over commit to allow, set to 0 (%) to disable over commit
+}
+
+message ModelOperationMessage {
+  enum Operation {
+    UNKNOWN_EVENT = 0;
+    LOAD_MODEL = 1;
+    UNLOAD_MODEL = 2;
+  }
+  Operation operation = 1;
+  ModelVersion modelVersion = 2;
+  bool autoscalingEnabled = 3;
+}
+
+message ModelVersion {
+  scheduler.Model model = 1;
+  uint32 version = 2;
+}
+
+// [END Messages]
+
+// [START Services]
+
+service AgentService {
+  rpc AgentEvent(ModelEventMessage) returns (ModelEventResponse) {};
+  rpc Subscribe(AgentSubscribeRequest) returns (stream ModelOperationMessage) {};
+  rpc ModelScalingTrigger(stream ModelScalingTriggerMessage) returns (ModelScalingTriggerResponse) {};
+  rpc AgentDrain(AgentDrainRequest) returns (AgentDrainResponse) {};
+}
+
+// [END Services]
+```
diff --git a/docs/apis/internal/chainer.md b/docs/apis/internal/chainer.md
index 510b26f589..f731e9c26f 100644
--- a/docs/apis/internal/chainer.md
+++ b/docs/apis/internal/chainer.md
@@ -4,7 +4,82 @@ This API is for communication between the Seldon Scheduler and the Seldon datafl
 
 ## Proto Definition
 
-```{literalinclude} ../../../../../apis/mlops/chainer/chainer.proto 
-:language: proto
-```
+```proto
+syntax = "proto3";
+
+package seldon.mlops.chainer;
+
+option go_package = "github.com/seldonio/seldon-core/apis/go/v2/mlops/chainer";
+option java_package = "io.seldon.mlops.chainer";
+
+message PipelineSubscriptionRequest {
+  string name = 1;
+}
+
+message PipelineUpdateMessage {
+  enum PipelineOperation {
+    Unknown = 0;
+    Create = 1;
+    Delete = 2;
+  }
+  PipelineOperation op = 1;
+  string pipeline = 2;
+  uint32 version = 3;
+  string uid = 4;
+  repeated PipelineStepUpdate updates = 5;
+}
+
+message PipelineStepUpdate {
+  enum PipelineJoinType {
+    Unknown = 0;
+    Inner = 1;
+    Outer = 2;
+    Any = 3;
+  }
+  // https://docs.google.com/document/d/1tX-uaOvngx1RpEyWEZ4EbEcU8D0OgYuRWVb2UAi85n4/edit
+  // Pipeline Resource example, e.g. transform.outputs.traffic
+  //    seldon.<namespace>.<model name>.<inputs|outputs>.<tensor name>
+  repeated PipelineTopic sources = 1;
+  repeated PipelineTopic triggers = 2;
+  PipelineTopic sink = 3;
+  PipelineJoinType inputJoinTy = 4;
+  PipelineJoinType triggersJoinTy = 5;
+  bool passEmptyResponses = 6; // Forward empty response to following steps, default false
+  optional uint32 joinWindowMs = 7; // Join window millisecs, some nozero default (TBD)
+  repeated PipelineTensorMapping tensorMap = 8; // optional list of tensor name mappings
+  Batch batch = 9; // Batch settings
+}
 
+message PipelineTensorMapping {
+  string pipelineName = 1;
+  string topicAndTensor = 2;
+  string tensorName = 3;
+}
+
+message PipelineTopic {
+  string pipelineName = 1;
+  string topicName = 2;
+  optional string tensor = 3;
+}
+
+message Batch {
+  optional uint32 size = 1;
+  optional uint32 windowMs = 2;
+  bool rolling = 3;
+}
+
+message PipelineUpdateStatusMessage {
+  // TODO - include `name` to identify transformer message comes from
+  PipelineUpdateMessage update = 1;
+  bool success = 2;
+  string reason = 3;
+}
+
+message PipelineUpdateStatusResponse {
+}
+
+service Chainer {
+  rpc SubscribePipelineUpdates(PipelineSubscriptionRequest) returns (stream PipelineUpdateMessage) {};
+  rpc PipelineUpdateEvent(PipelineUpdateStatusMessage) returns (PipelineUpdateStatusResponse) {};
+}
+```
diff --git a/docs/apis/internal/index.md b/docs/apis/internal/index.md
deleted file mode 100644
index 13af70a6af..0000000000
--- a/docs/apis/internal/index.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# Internal APIs
-
-Seldon provides some internal gRPC services it uses to manage components.
-
- * [Agent API](./agent.md)
- * [Chainer API](./chainer.md) 
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-agent.md
-chainer.md
-```
\ No newline at end of file
diff --git a/docs/apis/scheduler/README.md b/docs/apis/scheduler/README.md
new file mode 100644
index 0000000000..21d017a9ea
--- /dev/null
+++ b/docs/apis/scheduler/README.md
@@ -0,0 +1,423 @@
+# Scheduler API
+
+The Seldon scheduler API provides a gRPC service to allow Models, Servers, Experiments, and Pipelines to be managed. In Kubernetes the manager deployed by Seldon translates Kubernetes custom resource definitions into calls to the Seldon Scheduler.
+
+In non-Kubernetes environments users of Seldon could create a client to directly control Seldon resources using this API.
+
+## Proto Definition
+
+```proto
+syntax = "proto3";
+
+package seldon.mlops.scheduler;
+
+option go_package = "github.com/seldonio/seldon-core/apis/go/v2/mlops/scheduler";
+
+import "google/protobuf/timestamp.proto";
+
+// [START Messages]
+
+message LoadModelRequest {
+  Model model = 1;
+}
+
+message Model {
+  MetaData meta = 1;
+  ModelSpec modelSpec = 2;
+  DeploymentSpec deploymentSpec = 3;
+  StreamSpec streamSpec = 4;
+}
+
+message MetaData {
+  string name = 1;
+  optional string kind = 2;
+  optional string version = 3;
+  optional KubernetesMeta kubernetesMeta = 4; // Kubernetes specific config
+}
+
+message DeploymentSpec {
+  uint32 replicas = 1;
+  uint32 minReplicas = 2;
+  uint32 maxReplicas = 3;
+  bool logPayloads = 4;
+}
+
+/* ModelDetails
+*/
+message ModelSpec {
+  string uri = 1; // storage uri from where to download the artifacts
+  optional uint32 artifactVersion = 2; // Optional v2 version folder to select
+  optional StorageConfig storageConfig = 3; // Storage auth configuration
+  repeated string requirements = 4; // list of capabilities the server must satisfy to run this model
+  optional uint64 memoryBytes = 5; // Requested memory
+  optional string server = 6; // the particular model server to load the model. If unspecified will be chosen.
+  optional ExplainerSpec explainer = 7; // optional black box explainer details
+  repeated ParameterSpec parameters = 8; // parameters to load with model
+}
+
+message ParameterSpec {
+  string name = 1;
+  string value = 2;
+}
+
+message ExplainerSpec {
+  string type = 1;
+  // 1 of semantic either model or pipeline reference
+  optional string modelRef = 2;
+  optional string pipelineRef = 3;
+}
+
+message KubernetesMeta {
+  string namespace = 1;
+  int64 generation = 2;
+}
+
+message StreamSpec {
+  string inputTopic = 2;
+  string outputTopic = 3;
+}
+
+message StorageConfig {
+  oneof config {
+    string storageSecretName = 1;
+    string storageRcloneConfig = 2;
+  }
+}
+
+message LoadModelResponse {
+
+}
+
+/* ModelReference represents a unique model
+*/
+message ModelReference {
+  string name = 1;
+  optional uint32 version = 2;
+}
+
+message UnloadModelRequest {
+  ModelReference model = 1;
+  optional KubernetesMeta kubernetesMeta = 2;
+}
+
+message UnloadModelResponse {
+}
+
+/* ModelStatusResponse provides the current assignment of the model onto a server
+*/
+message ModelStatusResponse {
+  string modelName = 1;
+  repeated ModelVersionStatus versions = 2;
+  bool deleted = 3;
+}
+
+message ModelVersionStatus {
+  uint32 version = 2;
+  string serverName = 3;
+  optional KubernetesMeta kubernetesMeta = 4;
+  map<int32,ModelReplicaStatus> modelReplicaState = 5;
+  ModelStatus state = 6;
+  optional Model modelDefn = 7;
+}
+
+message ModelStatus {
+  enum ModelState {
+      ModelStateUnknown = 0;
+      ModelProgressing = 1;
+      ModelAvailable = 2;
+      ModelFailed = 3;
+      ModelTerminating = 4;
+      ModelTerminated = 5;
+      ModelTerminateFailed = 6;
+      ScheduleFailed = 7;
+  }
+  ModelState state = 1;
+  string reason = 2;
+  uint32 availableReplicas = 3;
+  uint32 unavailableReplicas = 4;
+  google.protobuf.Timestamp lastChangeTimestamp = 5;
+}
+
+message ModelReplicaStatus {
+  enum ModelReplicaState {
+      ModelReplicaStateUnknown = 0;
+      LoadRequested = 1;
+      Loading = 2;
+      Loaded = 3;
+      LoadFailed = 4;
+      UnloadRequested = 5;
+      Unloading = 6;
+      Unloaded = 7;
+      UnloadFailed = 8;
+      Available = 9;
+      LoadedUnavailable = 10;
+      UnloadEnvoyRequested = 11;
+      Draining = 12;
+  }
+  ModelReplicaState state = 1;
+  string reason = 2;
+  google.protobuf.Timestamp lastChangeTimestamp = 3;
+}
+
+message ServerStatusRequest {
+  string subscriberName = 1;
+  optional string name = 2; // Leave empty for all servers
+}
+
+/* ServerStatusResponse provides details of current server status
+*/
+message ServerStatusResponse {
+  string serverName = 1;
+  repeated ServerReplicaResources resources = 2;
+  int32 expectedReplicas = 3;
+  int32 availableReplicas = 4;
+  int32 numLoadedModelReplicas = 5;
+  optional KubernetesMeta kubernetesMeta = 6;
+}
+
+message ServerReplicaResources {
+  uint32 replicaIdx = 1;
+  uint64 totalMemoryBytes = 2;
+  uint64 availableMemoryBytes = 3;
+  int32 numLoadedModels = 4;
+  uint32 overCommitPercentage = 5;
+}
+
+message ModelSubscriptionRequest {
+  string subscriberName = 1; //Name of the subscription caller
+}
+
+message ModelStatusRequest {
+  string subscriberName = 1;
+  optional ModelReference model = 2;
+  bool allVersions = 3;
+}
+
+message ServerNotifyRequest {
+  string name = 1;
+  int32 expectedReplicas = 2;
+  bool shared = 3;
+  optional KubernetesMeta kubernetesMeta = 4;
+}
+
+message ServerNotifyResponse {
+
+}
+
+message ServerSubscriptionRequest {
+  string subscriberName = 1; //Name of the subscription caller
+}
+
+// Experiments
+
+message StartExperimentRequest {
+  Experiment experiment = 1;
+}
+
+enum ResourceType {
+  MODEL = 0;
+  PIPELINE = 1;
+}
+
+message Experiment {
+  string name = 1;
+  optional string default = 2;
+  repeated ExperimentCandidate candidates = 3;
+  optional ExperimentMirror mirror = 4;
+  optional ExperimentConfig config = 5;
+  optional KubernetesMeta kubernetesMeta = 6;
+  ResourceType resourceType = 7;
+}
+
+message ExperimentConfig {
+  bool stickySessions = 1;
+}
+
+message ExperimentCandidate {
+  string name = 1;
+  uint32 weight = 2;
+}
+
+message ExperimentMirror {
+  string name = 1;
+  uint32 percent = 2;
+}
+
+message StartExperimentResponse {
+}
+
+message StopExperimentRequest {
+  string name = 1;
+}
+
+message StopExperimentResponse {
+}
+
+message ExperimentSubscriptionRequest {
+  string subscriberName = 1; //Name of the subscription caller
+}
+
+message ExperimentStatusResponse {
+  string experimentName = 1;
+  bool active = 2;
+  bool candidatesReady = 3;
+  bool mirrorReady = 4;
+  string statusDescription = 5;
+  optional KubernetesMeta kubernetesMeta = 6;
+}
+
+message LoadPipelineRequest {
+  Pipeline pipeline = 1;
+}
+
+message ExperimentStatusRequest {
+  string subscriberName = 1;
+  optional string name = 2; // Leave empty for all experiments
+}
+
+message Pipeline {
+  string name = 1;
+  string uid = 2;
+  uint32 version = 3;
+  repeated PipelineStep steps = 4;
+  optional PipelineOutput output = 5;
+  optional KubernetesMeta kubernetesMeta = 6;
+  optional PipelineInput input = 7;
+}
+
+message PipelineStep {
+  enum JoinOp {
+    INNER = 0;
+    OUTER = 1;
+    ANY = 2;
+  }
+  string name = 1;
+  repeated string inputs = 2;
+  optional uint32 joinWindowMs = 3; // Join window millisecs, some nonzero default (TBD)
+  map<string,string> tensorMap = 4; // optional map of tensor name mappings
+  JoinOp inputsJoin = 5;
+  repeated string triggers = 6;
+  JoinOp triggersJoin = 7;
+  Batch batch = 8;
+}
+
+message Batch {
+  optional uint32 size = 1;
+  optional uint32 windowMs = 2;
+}
+
+message PipelineInput {
+  enum JoinOp {
+    INNER = 0;
+    OUTER = 1;
+    ANY = 2;
+  }
+  repeated string externalInputs = 1;
+  repeated string externalTriggers = 2;
+  optional uint32 joinWindowMs = 3; // Join window millisecs for output, default 0
+  JoinOp joinType = 4;
+  JoinOp triggersJoin = 5;
+  map<string,string> tensorMap = 6; // optional map of tensor name mappings
+}
+
+message PipelineOutput {
+  enum JoinOp {
+    INNER = 0;
+    OUTER = 1;
+    ANY = 2;
+  }
+  repeated string steps = 1;
+  uint32 joinWindowMs = 2; // Join window millisecs for output, default 0
+  JoinOp stepsJoin = 3;
+  map<string,string> tensorMap = 4; // optional map of tensor name mappings
+}
+
+message LoadPipelineResponse {
+
+}
+
+message UnloadPipelineRequest {
+  string name = 1;
+}
+
+message UnloadPipelineResponse {
+
+}
+
+message PipelineStatusRequest {
+  string subscriberName = 1;
+  optional string name = 2; // Leave empty for all pipelines
+  bool allVersions = 3;
+}
+
+message PipelineSubscriptionRequest {
+  string subscriberName = 1; //Name of the subscription caller
+}
+
+message PipelineStatusResponse {
+  string pipelineName = 1;
+  repeated PipelineWithState versions = 2;
+}
+
+message PipelineWithState {
+  Pipeline pipeline = 1;
+  PipelineVersionState state = 2;
+}
+
+message PipelineVersionState {
+  enum PipelineStatus {
+    PipelineStatusUnknown = 0;
+    PipelineCreate = 1;
+    PipelineCreating = 2;
+    PipelineReady = 3;
+    PipelineFailed = 4;
+    PipelineTerminate = 5;
+    PipelineTerminating = 6;
+    PipelineTerminated = 7;
+  }
+  uint32 pipelineVersion = 1;
+  PipelineStatus status = 2;
+  string reason = 3;
+  google.protobuf.Timestamp lastChangeTimestamp = 4;
+  bool modelsReady = 5;
+}
+
+message SchedulerStatusRequest {
+  string subscriberName = 1;
+}
+
+message SchedulerStatusResponse {
+  string applicationVersion = 1;
+}
+
+// [END Messages]
+
+
+// [START Services]
+
+service Scheduler {
+  rpc ServerNotify(ServerNotifyRequest) returns (ServerNotifyResponse) {};
+
+  rpc LoadModel(LoadModelRequest) returns (LoadModelResponse) {};
+  rpc UnloadModel(UnloadModelRequest) returns (UnloadModelResponse) {};
+
+  rpc LoadPipeline(LoadPipelineRequest) returns (LoadPipelineResponse) {};
+  rpc UnloadPipeline(UnloadPipelineRequest) returns (UnloadPipelineResponse) {};
+
+  rpc StartExperiment(StartExperimentRequest) returns (StartExperimentResponse) {};
+  rpc StopExperiment(StopExperimentRequest) returns (StopExperimentResponse) {};
+
+  rpc ServerStatus(ServerStatusRequest) returns (stream ServerStatusResponse) {}
+  rpc ModelStatus(ModelStatusRequest) returns (stream ModelStatusResponse) {}
+  rpc PipelineStatus(PipelineStatusRequest) returns (stream PipelineStatusResponse) {};
+  rpc ExperimentStatus(ExperimentStatusRequest) returns (stream ExperimentStatusResponse) {};
+  rpc SchedulerStatus(SchedulerStatusRequest) returns (SchedulerStatusResponse) {};
+
+  rpc SubscribeServerStatus(ServerSubscriptionRequest) returns (stream ServerStatusResponse) {};
+  rpc SubscribeModelStatus(ModelSubscriptionRequest) returns (stream ModelStatusResponse) {};
+  rpc SubscribeExperimentStatus(ExperimentSubscriptionRequest) returns (stream ExperimentStatusResponse) {};
+  rpc SubscribePipelineStatus(PipelineSubscriptionRequest) returns (stream PipelineStatusResponse) {};
+}
+
+// [END Services]
+```
diff --git a/docs/apis/scheduler/index.md b/docs/apis/scheduler/index.md
deleted file mode 100644
index bbbf8c700b..0000000000
--- a/docs/apis/scheduler/index.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# Scheduler API
-
-The Seldon scheduler API provides a gRPC service to allow Models, Servers, Experiments, and Pipelines to be managed. In Kubernetes the manager deployed by Seldon translates Kubernetes custom resource definitions into calls to the Seldon Scheduler.
-
-In non-Kubernetes environments users of Seldon could create a client to directly control Seldon resources using this API.
-
-## Proto Definition
-
-```{literalinclude} ../../../../../apis/mlops/scheduler/scheduler.proto 
-:language: proto
-```
-

From 05321e8b43de7e8f0b551bc5df3a370e3671ca86 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Thu, 18 Jul 2024 15:37:00 +0800
Subject: [PATCH 03/32] changing the configuration section in the getting
 started guide

---
 docs/getting-started/{configuration/index.md => configuration.md} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename docs/getting-started/{configuration/index.md => configuration.md} (100%)

diff --git a/docs/getting-started/configuration/index.md b/docs/getting-started/configuration.md
similarity index 100%
rename from docs/getting-started/configuration/index.md
rename to docs/getting-started/configuration.md

From 4c0b62857ac738b8ba9fc92b021aa411fe83a48c Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Thu, 8 Aug 2024 15:07:59 +1000
Subject: [PATCH 04/32] getting started sectionc completed

---
 docs/getting-started/README.md                |  54 ++++
 docs/getting-started/cli.md                   |  13 +-
 docs/getting-started/configuration.md         | 134 +++++---
 .../index.md => docker-installation.md}       |  46 +--
 docs/getting-started/index.md                 |  54 ----
 .../kubernetes-installation/README.md         |  44 +++
 .../kubernetes-installation/ansible.md        | 245 +++++++++++++-
 .../kubernetes-installation/helm.md           |  42 ++-
 .../kubernetes-installation/index.md          |  57 ----
 .../security/README.md                        | 127 ++++++++
 .../security/aws-msk-mtls.md                  |  66 ++--
 .../security/aws-msk-sasl.md                  |  33 +-
 .../security/azure-event-hub-sasl.md          |  56 ++--
 .../security/confluent-oauth.md               |  26 +-
 .../security/confluent-sasl.md                |  35 +-
 .../kubernetes-installation/security/index.md | 104 ------
 .../security/networking.jpg                   | Bin 103917 -> 0 bytes
 .../security/reference.md                     | 299 +++++++++---------
 .../security/strimzi-mtls.md                  |  45 ++-
 .../security/strimzi-sasl.md                  |  33 +-
 20 files changed, 992 insertions(+), 521 deletions(-)
 create mode 100644 docs/getting-started/README.md
 rename docs/getting-started/{docker-installation/index.md => docker-installation.md} (59%)
 delete mode 100644 docs/getting-started/index.md
 create mode 100644 docs/getting-started/kubernetes-installation/README.md
 delete mode 100644 docs/getting-started/kubernetes-installation/index.md
 create mode 100644 docs/getting-started/kubernetes-installation/security/README.md
 delete mode 100644 docs/getting-started/kubernetes-installation/security/index.md
 delete mode 100644 docs/getting-started/kubernetes-installation/security/networking.jpg

diff --git a/docs/getting-started/README.md b/docs/getting-started/README.md
new file mode 100644
index 0000000000..6964c0a13e
--- /dev/null
+++ b/docs/getting-started/README.md
@@ -0,0 +1,54 @@
+# Getting Started
+
+{% hint style="info" %}
+Some dependencies may require that the (virtual) machines on which you deploy, support the SSE4.2
+instruction set or x86-64-v2 microarchitecture. If `lscpu | grep sse4_2` does not return anything on
+your machine, your CPU is not compatible, and you may need to update the (virtual) host's CPU.
+{% endhint %}
+
+Seldon Core can be installed either with Docker Compose or with Kubernetes:
+
+* [Install locally with Docker Compose](./docker-installation.md)
+* [Install onto a Kubernetes cluster](./kubernetes-installation/README.md)
+
+Once installed:
+
+* Try the existing [examples](../examples/README.md).
+* Train and deploy your own [model artifact](../models/inference-artifacts.md#saving-model-artifacts).
+
+
+## Core Concepts
+
+There are three core resources you will use:
+
+* [Models](../models/README.md) - for deploying single machine learning models, custom transformation
+logic, drift detectors and outliers detectors.
+* [Pipelines](../pipelines.md) - for connecting together flows of data transformations between Models
+with a synchronous path and multiple asynchronous paths.
+* [Experiments](../experiments.md) - for testing new versions of models
+
+By default the standard installation will deploy MLServer and Triton inference servers which provide
+support for a wide range of machine learning model artifacts including Tensorflow models, PyTorch models,
+SKlearn models, XGBoost models, ONNX models, TensorRT models, custom python models and many more. For
+advanced use, the creation of new inference servers is manged by two resources:
+
+* [Servers](../servers.md) - for deploying sets of replicas of core inference servers (MLServer or Triton by default).
+* [ServerConfigs](../kubernetes/resources/serverconfig.md) - for defining server configurations including custom servers.
+
+## API for Inference
+
+Once deployed models can be called using the [Seldon V2 inference protocol](../apis/inference/v2.md). This
+protocol created by Seldon, NVIDIA and the KServe projects is supported by MLServer and Triton inference
+servers amingst others and allows REST and gRPC calls to your model.
+
+Your model is exposed via our internal Envoy gateway. If you wish to expose your models in Kubernetes
+outside the cluster you are free to use any Service Mesh or Ingress technology. Various examples are provided
+for service mesh integration.
+
+## Inference Metrics
+
+Metrics are exposed for scraping by Prometheus. For Kubernetes we provide example instructions for using kube-prometheus.
+
+## Pipeline requirements
+
+Pipelines are built upon Kafka streaming technology.
diff --git a/docs/getting-started/cli.md b/docs/getting-started/cli.md
index 9f8d2472e9..6b5f92bc6b 100644
--- a/docs/getting-started/cli.md
+++ b/docs/getting-started/cli.md
@@ -8,7 +8,7 @@ Download from a recent release from https://github.com/SeldonIO/seldon-core/rele
 
 It is dynamically linked and will require and *nix architecture and glibc 2.25+.
 
-```
+```sh
 mv seldon-linux-amd64 seldon
 chmod u+x seldon
 ```
@@ -38,8 +38,15 @@ cd seldon-core/operator
 make build-seldon-arm
 ```
 
-Add `<project-root>/operator/bin` to your PATH.
-
+Add `<project-root>/operator/bin` to your PATH. Open your terminal and open up your `.bashrc` or
+`.zshrc` file and add the following line:
 
+```sh
+export PATH=$PATH:<project-root>/operator/bin
+```
 
+## Usage
 
+```sh
+seldon --help
+```
diff --git a/docs/getting-started/configuration.md b/docs/getting-started/configuration.md
index 50720ef7f2..5844d95780 100644
--- a/docs/getting-started/configuration.md
+++ b/docs/getting-started/configuration.md
@@ -6,64 +6,103 @@ Seldon can be configured via various config files.
 
 We allow configuration of the Kafka integration. In general this configuration looks like:
 
-```{literalinclude} ../../../../../scheduler/config/kafka-internal.json
-:language: json
+```json
+# scheduler/config/kafka-internal.json
+{
+    "topicPrefix": "seldon",
+    "bootstrap.servers":"kafka:9093",
+    "consumer":{
+	"session.timeout.ms":6000,
+	"auto.offset.reset":"earliest",
+	"topic.metadata.propagation.max.ms": 300000,
+	"message.max.bytes":1000000000
+    },
+    "producer":{
+	"linger.ms":0,
+	"message.max.bytes":1000000000
+    },
+    "streams":{
+    }
+}
 ```
 
 The top level keys are:
 
- * `topicPrefix` : the prefix to add to kafka topics created by Seldon
- * `consumerGroupIdPrefix` : the prefix to add to Kafka consumer group IDs created by Seldon
- * `bootstrap.servers` : the global bootstrap kafka servers to use
- * `consumer` : consumer settings
- * `producer` : producer settings
- * `streams` : KStreams settings
+* `topicPrefix` : the prefix to add to kafka topics created by Seldon
+* `consumerGroupIdPrefix` : the prefix to add to Kafka consumer group IDs created by Seldon
+* `bootstrap.servers` : the global bootstrap kafka servers to use
+* `consumer` : consumer settings
+* `producer` : producer settings
+* `streams` : KStreams settings
 
-For `topicPrefix` you can use any acceptable kafka topic characters which are `a-z, A-Z, 0-9, . (dot), _ (underscore), and - (dash)`. We use `.` (dot) internally as topic naming separator so we would suggest you don't end your topic prefix with a dot for clarity. For illustration, an example topic could be `seldon.default.model.mymodel.inputs` where `seldon` is the topic prefix.
+For `topicPrefix` you can use any acceptable kafka topic characters which are
+`a-z, A-Z, 0-9, . (dot), _ (underscore), and - (dash)`. We use `.` (dot) internally as topic
+naming separator so we would suggest you don't end your topic prefix with a dot for clarity. For
+illustration, an example topic could be `seldon.default.model.mymodel.inputs` where `seldon` is the topic prefix.
 
 The `consumerGroupIdPrefix` will ensure that all consumer groups created have a given prefix.
 
 ### Kubernetes
 
-For Kubernetes this is controlled via a ConfigMap called `seldon-kafka` whose default values are defined in the `SeldonConfig` custom resource.
-
-```{literalinclude} ../../../../../k8s/yaml/components.yaml
-:language: yaml
-:start-after:     kafkaConfig
-:end-before:     serviceConfig
+For Kubernetes this is controlled via a ConfigMap called `seldon-kafka` whose default values are
+defined in the `SeldonConfig` custom resource.
+
+```yaml
+# k8s/yaml/components.yaml
+kafkaConfig:
+  bootstrap.servers: 'seldon-kafka-bootstrap.seldon-mesh:9092'
+  consumer:
+    auto.offset.reset: 'earliest'
+    message.max.bytes: '1000000000'
+    session.timeout.ms: '6000'
+    topic.metadata.propagation.max.ms: '300000'
+  consumerGroupIdPrefix: ''
+  debug: ''
+  producer:
+    linger.ms: '0'
+    message.max.bytes: '1000000000'
+  topicPrefix: 'seldon'
 ```
 
-When the `SeldonRuntime` is installed in a namespace a configMap will be created with these settings for Kafka configuration.
+When the `SeldonRuntime` is installed in a namespace a configMap will be created with these
+settings for Kafka configuration.
 
-To customize the settings you can add and modify the Kafka configuration via Helm, for example below is a custom Helm values file that add compression for producers:
+To customize the settings you can add and modify the Kafka configuration via Helm, for example
+below is a custom Helm values file that add compression for producers:
 
-```{literalinclude} ../../../../../k8s/samples/values-runtime-kafka-compression.yaml
-:language: yaml
+```yaml
+# k8s/samples/values-runtime-kafka-compression.yaml
+config:
+  kafkaConfig:
+    producer:
+      compression.type: gzip
 ```
 To use this with the SeldonRuntime Helm chart:
 
-```
+```sh
 helm install seldon-v2-runtime k8s/helm-charts/seldon-core-v2-runtime \
     --namespace seldon-mesh \
     --values k8s/samples/values-runtime-kafka-compression.yaml
 ```
 ### Topic and consumer isolation
 
-If you use a shared Kafka cluster with other applications you may want to isolate the topic names and consumer group IDs from other users of the cluster to ensure there is no name clash. For this we provide two settings:
+If you use a shared Kafka cluster with other applications you may want to isolate the topic
+names and consumer group IDs from other users of the cluster to ensure there is no name
+clash. For this we provide two settings:
 
- * `topicPrefix`: set a prefix for all topics
- * `consumerGroupIdPrefix`: set a prefix for all consumer groups
+* `topicPrefix`: set a prefix for all topics
+* `consumerGroupIdPrefix`: set a prefix for all consumer groups
 
 An example to set this in the configuration when using the helm installation is showm below for creating the default `SeldonConfig`:
 
-```
+```sh
 helm upgrade --install seldon-v2 k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh \
     --set controller.clusterwide=true \
     --set kafka.topicPrefix=myorg \
     --set kafka.consumerGroupIdPrefix=myorg
 ```
 
-You can find a worked example [here](../../examples/k8s-clusterwide.md).
+You can find a worked example [here](../examples/k8s-clusterwide.md).
 
 You can create alternate `SeldonConfig`s with different values or override values for particular `SeldonRuntime` installs.
 
@@ -71,19 +110,25 @@ You can create alternate `SeldonConfig`s with different values or override value
 
 We allow configuration of tracing. This file looks like:
 
-```{literalinclude} ../../../../../scheduler/config/tracing-internal.json
-:language: json
+```json
+# scheduler/config/tracing-internal.json
+{
+  "disable": false,
+  "otelExporterEndpoint": "otel-collector:4317",
+  "otelExporterProtocol": "grpc",
+  "ratio": "1"
+}
 ```
 
 The top level keys are:
 
- * `enable` : whether to enable tracing
- * `otelExporterEndpoint` : The host and port for the OTEL exporter
- * `otelExporterProtocol` : The protocol for the OTEL exporter. Currently used for
-     jvm-based components only (such as dataflow-engine), because `opentelemetry-java-instrumentation`
-     requires a http(s) URI for the endpoint but defaults to `http/protobuf` as a protocol.
-     Because of this, gRPC connections (over http) can only be set up by setting this option to `grpc`
- * `ratio` : The ratio of requests to trace. Takes values between 0 and 1 inclusive.
+* `enable` : whether to enable tracing
+* `otelExporterEndpoint` : The host and port for the OTEL exporter
+* `otelExporterProtocol` : The protocol for the OTEL exporter. Currently used for
+    jvm-based components only (such as dataflow-engine), because `opentelemetry-java-instrumentation`
+    requires a http(s) URI for the endpoint but defaults to `http/protobuf` as a protocol.
+    Because of this, gRPC connections (over http) can only be set up by setting this option to `grpc`
+* `ratio` : The ratio of requests to trace. Takes values between 0 and 1 inclusive.
 
 
 
@@ -91,10 +136,25 @@ The top level keys are:
 
 For Kubernetes this is controlled via a ConfigMap call `seldon-tracing` whose default value is shown below:
 
-```{literalinclude} ../../../../../scheduler/k8s/config/tracing.yaml
-:language: yaml
+```yaml
+# scheduler/k8s/config/tracing.yaml
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: tracing
+data:
+  tracing.json: |-
+   {
+     "enable": true,
+     "otelExporterEndpoint": "seldon-collector:4317",
+     "otelExporterProtocol": "grpc",
+     "ratio": "1"
+   }
+  OTEL_JAVAAGENT_ENABLED: "true"
+  OTEL_EXPORTER_OTLP_ENDPOINT: "http://seldon-collector:4317"
+  OTEL_EXPORTER_OTLP_PROTOCOL: "grpc"
 ```
 
-Note, this ConfigMap is created via our Helm charts and there is usually no need to modify it manually.
+Note, this `ConfigMap` is created via our Helm charts and there is usually no need to modify it manually.
 
 At present Java instrumentation (for the dataflow engine) is duplicated via separate keys.
diff --git a/docs/getting-started/docker-installation/index.md b/docs/getting-started/docker-installation.md
similarity index 59%
rename from docs/getting-started/docker-installation/index.md
rename to docs/getting-started/docker-installation.md
index 31be21adfa..e1f7a81554 100644
--- a/docs/getting-started/docker-installation/index.md
+++ b/docs/getting-started/docker-installation.md
@@ -2,29 +2,33 @@
 
 ## Preparation
 
- 1. `git clone https://github.com/SeldonIO/seldon-core --branch=v2`
- 2. Build [Seldon CLI](../cli.md)
- 3. Install [Docker Compose](https://docs.docker.com/compose/install/) (or directly from GitHub [release](https://github.com/docker/compose#linux) if not using Docker Desktop).
- 4. Install `make`. This will depend on your version of Linux, for example on Ubuntu run `sudo apt-get install build-essential`.
+1. `git clone https://github.com/SeldonIO/seldon-core --branch=v2`
+2. Build [Seldon CLI](cli.md)
+3. Install [Docker Compose](https://docs.docker.com/compose/install/) (or directly from GitHub
+[release](https://github.com/docker/compose#linux) if not using Docker Desktop).
+4. Install `make`. This will depend on your version of Linux, for example on Ubuntu run
+`sudo apt-get install build-essential`.
 
 
 ## Deploy
 
 From the project root run:
 
-```
+```sh
 make deploy-local
 ```
 
 This will run with `latest` images for the components.
 
-Note: Triton and MLServer are large images at present (11G and 9G respectively) so will take time to download on first usage.
+Note: Triton and MLServer are large images at present (11G and 9G respectively) so will take time to
+download on first usage.
 
 ### Run a particular version
 
-To run a particular release set the environment variable `CUSTOM_IMAGE_TAG` to the desired version before running the command, e.g.:
+To run a particular release set the environment variable `CUSTOM_IMAGE_TAG` to the desired version
+before running the command, e.g.:
 
-```
+```sh
 export CUSTOM_IMAGE_TAG=0.2.0
 make deploy-local
 ```
@@ -39,17 +43,26 @@ To enable GPU on servers:
 
 ### Local Models
 
-To deploy with a local folder available for loading models set the environment variable `LOCAL_MODEL_FOLDER` to the folder, e.g.:
+To deploy with a local folder available for loading models set the environment variable `LOCAL_MODEL_FOLDER`
+to the folder, e.g.:
 
-```bash
+```sh
 export LOCAL_MODEL_FOLDER=/home/seldon/models
 make deploy-local
 ```
 
 This folder will be mounted at `/mnt/models`. You can then specify models as shown below:
 
-```{literalinclude} ../../../../../samples/models/sklearn-iris-local.yaml
-:language: yaml
+```yaml
+# samples/models/sklearn-iris-local.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "/mnt/models/iris"
+  requirements:
+  - sklearn
 ```
 
 If you have set the local model folder as above then this would be looking at `/home/seldon/models/iris`.
@@ -66,13 +79,6 @@ The default local install will expose Grafana at `http://localhost:3000`.
 
 From the project root run:
 
-```
+```sh
 make undeploy-local
 ```
-
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-```
diff --git a/docs/getting-started/index.md b/docs/getting-started/index.md
deleted file mode 100644
index bf644d9387..0000000000
--- a/docs/getting-started/index.md
+++ /dev/null
@@ -1,54 +0,0 @@
-# Getting Started
-
-```{note}
-Some dependencies may require that the (virtual) machines on which you deploy, support the SSE4.2 instruction set or x86-64-v2 microarchitecture. If `lscpu | grep sse4_2` does not return anything on your machine, your CPU is not compatible, and you may need to update the (virtual) host's CPU.
-```
-
-Seldon Core can be installed either with Docker Compose or with Kubernetes:
-
- * [Install locally with Docker Compose](./docker-installation/index.md)
- * [Install onto a Kubernetes cluster](./kubernetes-installation/index.md)
-
-Once installed:
-
-  * Try the existing [examples](../examples/index.md).
-  * Train and deploy your own [model artifact](../models/inference-artifacts/index.md#saving-model-artifacts).
-
-
-## Core Concepts
-
-There are three core resources you will use:
-
- * [Models](../models/index.md) - for deploying single machine learning models, custom transformation logic, drift detectors and outliers detectors.
- * [Pipelines](../pipelines/index.md) - for connecting together flows of data transformations between Models with a synchronous path and multiple asynchronous paths.
- * [Experiments](../experiments/index.md) - for testing new versions of models
-
-By default the standard installation will deploy MLServer and Triton inference servers which provide support for a wide range of machine learning model artifacts including Tensorflow models, PyTorch models, SKlearn models, XGBoost models, ONNX models, TensorRT models, custom python models and many more. For advanced use, the creation of new inference servers is manged by two resources:
-
- * [Servers](../servers/index.md) - for deploying sets of replicas of core inference servers (MLServer or Triton by default).
- * [ServerConfigs](../kubernetes/resources/serverconfig/index.md) - for defining server configurations including custom servers.
-
-## API for Inference
-
-Once deployed models can be called using the [Seldon V2 inference protocol](../apis/inference/v2.md). This protocol created by Seldon, NVIDIA and the KServe projects is supported by MLServer and Triton inference servers amingst others and allows REST and gRPC calls to your model.
-
-Your model is exposed via our internal Envoy gateway. If you wish to expose your models in Kubernetes outside the cluster you are free to use any Service Mesh or Ingress technology. Various examples are provided for service mesh integration.
-
-## Inference Metrics
-
-Metrics are exposed for scraping by Prometheus. For Kubernetes we provide example instructions for using kube-prometheus.
-
-## Pipeline requirements
-
-Pipelines are built upon Kafka streaming technology.
-
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-docker-installation/index.md
-kubernetes-installation/index.md
-configuration/index.md
-cli.md
-```
diff --git a/docs/getting-started/kubernetes-installation/README.md b/docs/getting-started/kubernetes-installation/README.md
new file mode 100644
index 0000000000..449850e590
--- /dev/null
+++ b/docs/getting-started/kubernetes-installation/README.md
@@ -0,0 +1,44 @@
+# Kubernetes Installation
+
+## Prerequisites
+
+### A running Kubernetes Cluster
+
+You will need a running Kubernetes cluster or can create a local KinD one for testing via [Ansible](ansible.md).
+
+### Install Ecosystem Components
+
+You will also need to install our ecosystem components. For this we provide directions for [Ansible](ansible.md) to install these.
+
+
+## Table
+
+| Component  | Summary |
+| - | - |
+| Kafka | Required for inference Pipeline usage. |
+| Prometheus | (Optional) Exposes metrics. |
+| Grafana | (Optional) UI for metrics. |
+| OpenTelemetry | (Optional) Exposes tracing. |
+| Jaeger | (Optional) UI for traces. |
+
+
+### Install
+
+To install Seldon Core V2 itself you can choose from the following. At present, all require a
+clone of the source repository.
+
+* [Helm Installation](helm.md)(recommended for production systems)
+* [Ansible](ansible.md)(recommended for test / dev / trial purposes)
+
+The Kubernetes operator that is installed runs in namespaced mode so any resources you create
+need to be in the same namespace as you installed into.
+
+### Kustomize
+
+Our recommended and supported way to install is via Helm or Ansible. If you wish to use Kustomize
+then you can base your configuration on the raw yaml we create in the folder `k8s/yaml` or follow
+the steps in `k8s/Makefile` which illustrate how we build this yaml from our own Kustomize bases.
+
+## Operations
+
+* [Security](security/README.md)
diff --git a/docs/getting-started/kubernetes-installation/ansible.md b/docs/getting-started/kubernetes-installation/ansible.md
index 34ada0f103..1490c18e73 100644
--- a/docs/getting-started/kubernetes-installation/ansible.md
+++ b/docs/getting-started/kubernetes-installation/ansible.md
@@ -1,2 +1,245 @@
-```{include} ../../../../../ansible/README.md
+# Ansible Installation
+
+{% hint style="warning" %}
+The Ansible installation of a Seldon Core and associated ecosystem is meant for **dev/testing** purposes.
+For production use cases follow [Helm installation](https://docs.seldon.io/projects/seldon-core/en/v2/contents/getting-started/kubernetes-installation/helm.html).
+{% endhint %}
+
+## Installing Ansible
+
+Provided Ansible playbooks and roles depends on [kubernetes.core](https://github.com/ansible-collections/kubernetes.core)
+Ansible Collection for performing `kubectl` and `helm` operations. Check Ansible [documentation] for further information.
+
+To install Ansible and required collections
+
+```bash
+pip install ansible openshift kubernetes docker
+ansible-galaxy collection install kubernetes.core
 ```
+
+We have tested provided instructions on Python 3.8 - 3.11 with following version of Python libraries
+
+| Python | Ansible | Docker | Kubernetes |
+|--------|---------|--------|------------|
+| 3.8    | 6.7.0   | 6.0.1  | 26.1.0     |
+| 3.9    | 7.2.0   | 6.0.1  | 26.1.0     |
+| 3.10   | 7.2.0   | 6.0.1  | 26.1.0     |
+| 3.11   | 7.2.0   | 6.0.1  | 26.1.0     |
+
+and `kubernetes.core` collection in version `2.4.0`.
+
+
+Once installed you can use the following Playbooks that you will find in
+[Ansible](https://github.com/SeldonIO/seldon-core/tree/v2/ansible) folder of Seldon Core V2 repository.
+
+You also need to have installed [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl-linux/) CLI.
+
+
+## Installing Seldon Core v2 using Ansible
+
+### One-liner local kind install (from scratch)
+
+If you simply want to install into a fresh local [kind](https://kind.sigs.k8s.io/)
+k8s cluster, the `seldon-all` playbook allows you to do so with a single command:
+
+```bash
+ansible-playbook playbooks/seldon-all.yaml
+```
+
+This will create a Kind cluster and install ecosystem dependencies (kafka,
+prometheus, opentelemetry, jager) as well as all the seldon-specific components.
+The seldon components are installed using helm-charts from the current git
+checkout (`../k8s/helm-charts/`).
+
+Internally this runs, in order, the following playbooks (described in more detail
+in the sections below):
+- kind-cluster.yaml
+- setup-ecosystem.yaml
+- setup-seldon.yaml
+
+You may pass any of the additonal variables which are configurable for those playbooks
+to `seldon-all`. See the Customizing Ansible Instalation section for details.
+
+For example:
+
+```bash
+ansible-playbook playbooks/seldon-all.yaml -e seldon_mesh_namespace=my-seldon-mesh -e install_prometheus=no -e @playbooks/vars/set-custom-images.yaml
+```
+
+Running the playbooks individually, as described in the sections below, will give you
+more control over what gets run and when (for example, if you want to install into an
+existing k8s cluster).
+
+
+### Create Kind Cluster
+
+It is recommended to first install Seldon Core v2 inside Kind cluster.
+This allow to test and trial the installation in isolated environment that is easy to remove.
+
+```bash
+ansible-playbook playbooks/kind-cluster.yaml
+```
+
+### Setup Ecosystem
+
+Seldon runs by default in `seldon-mesh` namespace and a Jaeger pod and  and OpenTelemetry
+collector are installed in the chosen namespace. Run the following:
+
+```bash
+ansible-playbook playbooks/setup-ecosystem.yaml
+```
+
+The most common change will be to install in another namespace with:
+
+```bash
+ansible-playbook playbooks/setup-ecosystem.yaml -e seldon_mesh_namespace=<mynamespace>
+```
+
+### Install Seldon Core v2
+
+Run the following from the `ansible/` folder:
+
+```bash
+ansible-playbook playbooks/setup-seldon.yaml
+```
+
+If you have changed the namespace you wish to use you will need to run with:
+
+```bash
+ansible-playbook playbooks/setup-seldon.yaml -e seldon_mesh_namespace=<mynamespace>
+```
+
+
+## Customizing Ansible Installation
+
+### Ecosystem configuration options
+
+The ecosystem setup can be parametrized by providing extra Ansible variables, e.g. using `-e`
+flag to `ansible-playbook` command.
+
+For example run the following from the `ansible/` folder:
+```bash
+ansible-playbook playbooks/setup-ecosystem.yaml -e full_install=no -e install_kafka=yes
+```
+will only install Kafka when setting up the ecosystem.
+
+|                         | type   | default                       | comment                                                  |
+|-------------------------|--------|-------------------------------|----------------------------------------------------------|
+| seldon_mesh_namespace   | string | seldon-mesh                   | namespace to install Seldon Core v2                      |
+| seldon_kafka_namespace  | string | seldon-mesh                   | namespace to install Kafka Cluster for Core v2           |
+| full_install            | bool   | yes                           | enables full ecosystem installation                      |
+| install_kafka           | bool   | `{{ full_install }}`          | installs Strimzi Kafka Operator                          |
+| install_prometheus      | bool   | `{{ full_install }}`          | installs Prometheus Operator                             |
+| install_grafana         | bool   | `{{ full_install }}`          | installs Grafana Operator                                |
+| install_certmanager     | bool   | `{{ full_install }}`          | installs Cert Manager                                    |
+| install_jaeger          | bool   | `{{ full_install }}`          | installs Jaeger                                          |
+| install_opentelemetry   | bool   | `{{ full_install }}`          | installs OpenTelemetry                                   |
+| configure_kafka         | bool   | `{{ install_kafka }}`         | configures Kafka Cluster for Core v2                     |
+| configure_prometheus    | bool   | `{{ install_prometheus }}`    | configure Prometheus using Core v2 specific resources    |
+| configure_jaeger        | bool   | `{{ install_jaeger }}`        | configure Jaeger using Core v2 specific resources        |
+| configure_opentelemetry | bool   | `{{ install_opentelemetry }}` | configure OpenTelemetry using Core v2 specific resources |
+
+
+
+### Seldon Core v2 configuration options
+
+|                         | type   | default                       | comment                                                 |
+|-------------------------|--------|-------------------------------|---------------------------------------------------------|
+| seldon_kafka_namespace  | string | seldon-mesh                   | namespace to install Kafka                              |
+| seldon_mesh_namespace   | string | seldon-mesh                   | namespace to install Seldon                             |
+| seldon_crds_namespace   | string | default                       | namespace to install Seldon CRDs                        |`
+| full_install            | bool   | yes                           | enables full ecosystem installation                     |
+| install_crds            | bool   | `{{ full_install }}`          | installs Seldon CRDs                                    |
+| install_components      | bool   | `{{ full_install }}`          | install Seldon components                               |
+| install_servers         | bool   | `{{ full_install }}`          | install Seldon servers                                  |
+
+
+#### Custom Seldon images and private registries
+
+By default, the container images used in the install are the ones defined by the helm
+charts (referring to images publicly available on dockerhub).
+
+If you need to customize the images (i.e pull from private registry, pull given
+tag), create a custom images config file following the example in
+`playbooks/vars/set-custom-images.yaml` and run with:
+
+```bash
+ansible-playbook playbooks/setup-seldon.yaml -e @<path-to-custom-images-config.yaml>
+```
+
+If, instead of pulling images from an external repository you want to build certain components
+locally, please read README.dev.md
+
+##### Private registries
+
+When using private registries, access needs to be authenticated (typically, via a
+service account key), and the k8s cluster will need to have access to a secret holding
+this key to be able to pull images.
+
+The `setup-seldon.yaml` playbook will create the required k8s secrets inside the
+cluster if it is provided with an auth file in `dockerconfigjson` format. You provide
+the path to this file by cusomizing the `custom_image_pull_secrets.dockerconfigjson`
+variable and define the secret name via `custom_image_pull_secrets.name` in the custom
+images config file (the one passed to the playbook via `-e @file`).
+
+By default, docker creates the `dockerconfigjson` auth file in `~/.docker/config.json`
+after passing the service-account key to `docker login`.
+
+The `docker login` command would look like this (key in json format):
+
+```bash
+cat registry-sa-key.json | docker login -u _json_key --password-stdin <registry-url>
+```
+
+or, for keys in base64 format:
+
+```bash
+cat registry-sa-key | docker login -u _json_key_base64 --password-stdin <registry-url>
+```
+
+##### Saving helm-chart customisations
+
+Because the additional custom images config file (starting from the
+`playbooks/vars/set-custom-images.yaml` example) overrides values in the helm-charts
+available in the repo, there's also a playbook option of saving those overrides as a
+separate values file, which could be used if deploying manually via helm.
+
+This is controlled via two variables:
+
+|                                      | type   | default                        | comment                             |
+|--------------------------------------|--------|--------------------------------|-------------------------------------|
+| save_helm_components_overrides       | bool   | false                          | enable saving helm values overrides |
+| save_helm_components_overrides_file  | string | ~/seldon_helm_comp_values.yaml | path/filename for saving overrides  |
+
+You can either pass those within the custom images config file or directly when running the
+playbook. For example, for just saving the helm-chart overrides (without installing seldon
+components), you would run:
+
+```bash
+ansible-playbook playbooks/setup-seldon.yaml -e full_install=no -e save_helm_components_overrides=yes -e @<path-to-custom-images-config.yaml>
+```
+
+Please note that when deploying outside ansible via helm using this saved overrides file,
+and using private registries, you will have to manually create the service-account key
+secret with the same name as the one defined in your custom image config file under
+`custom_image_pull_secrets.name`.
+
+## Uninstall
+
+To fully remove the Ansible installation delete the created Kind cluster
+
+```bash
+kind delete cluster --name seldon
+```
+
+This will stop and delete the `Kind` cluster freeing all of the resources taken by the dev/trial installation.
+You may want to also remove cache resources used for the installation with
+
+```bash
+rm -rf ~/.cache/seldon/
+```
+
+{% hint style="info" %}
+If you used Ansible to install Seldon Core v2 and its ecosystem into K8s cluster other than Kind you need to manually remove all the components.
+Notes on how to remove Seldon Core v2 Helm installation itself you can find [here](https://docs.seldon.io/projects/seldon-core/en/v2/contents/getting-started/kubernetes-installation/helm.html#uninstall).
+{% endhint %}
diff --git a/docs/getting-started/kubernetes-installation/helm.md b/docs/getting-started/kubernetes-installation/helm.md
index bf73a05290..29d93c539c 100644
--- a/docs/getting-started/kubernetes-installation/helm.md
+++ b/docs/getting-started/kubernetes-installation/helm.md
@@ -2,18 +2,23 @@
 
 We provide several Helm charts.
 
- * `seldon-core-v2-crds` : cluster wide install of custom resources.
- * `seldon-core-v2-setup` : installation of the manager to manage resources in the namespace or clusterwide. This also installs default **SeldonConfig** and **ServerConfig** resources which allow Runtimes and Servers to be installed easily on demand.
- * `seldon-core-v2-runtime` : this installs a **SeldonRuntime** custom resource which creates the core components in a namespace.
- * `seldon-core-v2-servers` : this installs **Server** custom resources which provide example core servers to load models.
- * `seldon-core-v2-certs` : a default set of certificates for TLS.
+* `seldon-core-v2-crds` : cluster wide install of custom resources.
+* `seldon-core-v2-setup` : installation of the manager to manage resources in the namespace
+or clusterwide. This also installs default **SeldonConfig** and **ServerConfig** resources
+which allow Runtimes and Servers to be installed easily on demand.
+* `seldon-core-v2-runtime` : this installs a **SeldonRuntime** custom resource which creates
+the core components in a namespace.
+* `seldon-core-v2-servers` : this installs **Server** custom resources which provide example
+core servers to load models.
+* `seldon-core-v2-certs` : a default set of certificates for TLS.
 
-The Helm charts can be found within the `k8s/helm-charts` folder and they are published [here](https://github.com/SeldonIO/helm-charts)
+The Helm charts can be found within the `k8s/helm-charts` folder and they are published
+[here](https://github.com/SeldonIO/helm-charts)
 
-Assuming you have installed any ecosystem components: Jaeger, Prometheus, Kafka as discussed [here](./index.md) you can follow the
-following steps.
+Assuming you have installed any ecosystem components: Jaeger, Prometheus, Kafka as discussed
+[here](./README.md) you can follow the following steps.
 
-Note that for Kafka follow the steps discussed [here](../../kubernetes/kafka/index)
+Note that for Kafka follow the steps discussed [here](../../kubernetes/kafka.md)
 
 ## Add Seldon Core v2 Charts
 
@@ -30,7 +35,9 @@ helm install seldon-core-v2-crds  seldon-charts/seldon-core-v2-crds
 
 ## Install the Seldon Core V2 Components
 
-You can install into any namespace. For illustration we will use `seldon-mesh`. This will install the core manager which will handle the key [resources](../../kubernetes/resources/index.md)  used by Seldon including the SeldonRuntime and Server resources.
+You can install into any namespace. For illustration we will use `seldon-mesh`. This will install
+the core manager which will handle the key [resources](../../kubernetes/resources/README.md)  used
+by Seldon including the SeldonRuntime and Server resources.
 
 ```bash
 kubectl create namespace seldon-mesh
@@ -40,13 +47,16 @@ kubectl create namespace seldon-mesh
 helm install seldon-core-v2  seldon-charts/seldon-core-v2-setup --namespace seldon-mesh
 ```
 
-This will install the operator namespaced so it will only control resources in the provided namespace. To allow cluster wide usage add the `--set controller.clusterwide=true`, e.g.
+This will install the operator namespaced so it will only control resources in the provided
+namespace. To allow cluster wide usage add the `--set controller.clusterwide=true`, e.g.
 
-```
+```bash
 helm install seldon-core-v2  seldon-charts/seldon-core-v2-setup --namespace seldon-mesh --set controller.clusterwide=true
 ```
 
-Cluster wide operations will require ClusterRoles to be created so when deploying be aware your user will require the required permissions. With cluster wide operations you can create SeldonRuntimes in any namespace.
+Cluster wide operations will require ClusterRoles to be created so when deploying be aware your
+user will require the required permissions. With cluster wide operations you can create `SeldonRuntimes`
+in any namespace.
 
 ## Install the default Seldon Core V2 Runtime
 
@@ -58,13 +68,15 @@ This will install the core components in your desired namespace.
 
 ## Install example servers
 
-To install some MLServer and Triton servers you can either create Server resources yourself or for initial testing you can use our example Helm chart seldon-core-v2-servers:
+To install some MLServer and Triton servers you can either create Server resources yourself or for
+initial testing you can use our example Helm chart seldon-core-v2-servers:
 
 ```bash
 helm install seldon-v2-servers seldon-charts/seldon-core-v2-servers --namespace seldon-mesh
 ```
 
-By default this will install 1 MLServer and 1 Triton in the desired namespace. This namespace should be the same namespace you installed a Seldon Core Runtime.
+By default this will install 1 MLServer and 1 Triton in the desired namespace. This namespace should
+be the same namespace you installed a Seldon Core Runtime.
 
 ## Uninstall
 
diff --git a/docs/getting-started/kubernetes-installation/index.md b/docs/getting-started/kubernetes-installation/index.md
deleted file mode 100644
index 5f247acdf8..0000000000
--- a/docs/getting-started/kubernetes-installation/index.md
+++ /dev/null
@@ -1,57 +0,0 @@
-# Kubernetes Installation
-
-## Prerequisites
-
-### A running Kubernetes Cluster
-
-You will need a running Kubernetes cluster or can create a local KinD one for testing via [Ansible](ansible.md).
-
-### Install Ecosystem Components
-
-You will also need to install our ecosystem components. For this we provide directions for [Ansible](ansible.md) to install these.
-
-```{list-table}
-:header-rows: 1
-
-* - Component
-  - Summary
-* - Kafka
-  - Required for inference Pipeline usage.
-* - Prometheus
-  - (Optional) Exposes metrics.
-* - Grafana
-  - (Optional) UI for metrics.
-* - OpenTelemetry
-  - (Optional) Exposes tracing.
-* - Jaeger
-  - (Optional) UI for traces.
-
-```
-
-### Install
-
-To install Seldon Core V2 itself you can choose from the following. At present, all require a clone of the source repository.
-
- * [Helm Installation](helm.md) (recommended for production systems)
- * [Ansible](ansible.md) (recommended for test / dev / trial purposes)
-
-The Kubernetes operator that is installed runs in namespaced mode so any resources you create need to be in the same namespace as you installed into.
-
-
-
-### Kustomize
-
-Our recommended and supported way to install is via Helm or Ansible. If you wish to use Kustomize then you can base your configuration on the raw yaml we create in the folder `k8s/yaml` or follow the steps in `k8s/Makefile` which illustrate how we build this yaml from our own Kustomize bases.
-
-## Operations
-
- * [Security](security/index.md)
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-helm.md
-ansible.md
-security/index.md
-```
diff --git a/docs/getting-started/kubernetes-installation/security/README.md b/docs/getting-started/kubernetes-installation/security/README.md
new file mode 100644
index 0000000000..1031af3b72
--- /dev/null
+++ b/docs/getting-started/kubernetes-installation/security/README.md
@@ -0,0 +1,127 @@
+# Security
+
+Seldon can be run with secure control plane and data plane operations. There are three areas of concern:
+
+* [Control Plane](#control-plane)
+* [Kafka](#kafka)
+* [Data Plane](#data-plane)
+
+The various communication points between services are shown in the diagram below:
+
+![networking](../../../images/networking.jpg)
+
+## Control Plane
+
+TLS control plane activation is switched on and off via the environment variable:
+`CONTROL_PLANE_SECURITY_PROTOCOL` whose values can be `PLAINTEXT` or `SSL`.
+
+Certificates will be loaded and used for the control plane gRPC services. The secrets or
+folders will be watched for updates (on certificate renewal) and automatically loaded again.
+
+### Helm Control Plane Install
+
+When installing `seldon-core-v2-setup` you can set the secret names for your certificates. If
+using cert-manager example discussed below this would be as follows:
+
+```bash
+helm install seldon-v2 k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh \
+      --set security.controlplane.protocol=SSL
+```
+
+## Kafka
+
+Kafka secure activation is switched on and off via the environment variable:
+`KAFKA_SECURITY_PROTOCOL` whose values can be `PLAINTEXT`, `SSL` or `SASL_SSL`.
+
+Examples are shown below:
+
+* [mTLS Strimzi](strimzi-mtls.md) example
+* [mTLS AWS MSK](aws-msk-mtls.md) example
+* [SASL PLAIN with Confluent Cloud](confluent-sasl.md) example
+* [SASL PLAIN with Azure Event Hub](azure-event-hub-sasl.md) example
+* [SASL SCRAM with Strimzi](strimzi-sasl.md) example
+* [SASL SCRAM with AWS MSK](aws-msk-sasl.md) example
+* [SASL OAUTH with Confluent Cloud](confluent-oauth.md) example
+
+## Data Plane
+
+TLS Data plane activation is switched on and off via the environment variable:
+`ENVOY_SECURITY_PROTOCOL` whose values can be `PLAINTEXT` or `SSL`.
+
+When activated this ensures TLS is used to communicate to Envoy via the xDS server as
+well as using the SDS service to send cretificates to envoy to use for  upstream and
+downstream networking. Downstream is the external access to Seldon and upstream is the
+path from Envoy to the model servers or pipeline gateway.
+
+### Helm Data Plane Install
+
+When installing `seldon-core-v2-setup` you can set data plane operations to TLS as below.
+This assumes the secrets installed by the helm chart at the end of this section.
+
+```bash
+helm install seldon-v2 k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh --set security.envoy.protocol=SSL
+```
+
+The above uses default secret names defined for the certificates installed. You can change
+the names of the required certificate secrets as shown in a longer configuration below (again
+using the default names for illustration).
+
+For this we use the following updated Helm values (`k8s/samples/values-tls-dataplane-example.yaml`):
+
+```yaml
+security:
+  controlplane:
+    protocol: PLAINTEXT
+    ssl:
+      server:
+        secret: seldon-controlplane-server
+        clientValidationSecret: seldon-controlplane-client
+      client:
+        secret: seldon-controlplane-client
+        serverValidationSecret: seldon-controlplane-server
+  envoy:
+    protocol: SSL
+    ssl:
+      upstream:
+        server:
+          secret: seldon-upstream-server
+          clientValidationSecret: seldon-upstream-client
+        client:
+          secret: seldon-upstream-client
+          serverValidationSecret: seldon-upstream-server
+      downstream:
+        client:
+          serverValidationSecret: seldon-downstream-server
+        server:
+          secret: seldon-downstream-server
+```
+
+We use Envoy internally to direct traffic and in Envoy's terminology `upstream` is for internal model servers called from Envoy while the downstream server is the entrypoint server running in Envoy to receive grpc and REST calls. The above settings ensure mTLS for internal "upstream" traffic while provides a standard SSL non-mTLS entrpoint.
+
+To use the above with the seldon CLI you would need a custom config file as follow:
+
+```json
+{
+    "dataplane": {
+	"tls": true,
+	"skipSSLVerify": true
+    }
+}
+
+```
+
+We skip SSL Verify as these are internal self-signed certificates. For production use you
+would change this to the correct DNS name you are exposing the Seldon entrypoint.
+
+## Cetificate Providers
+
+The installer/cluster controller for Seldon needs to provide the certificates. As part of
+Seldon we provide an example set of certificate issuers and certificates using [cert-manager](https://cert-manager.io/).
+
+### Helm
+
+You can install Certificates into the desired namespace, here we use `seldon-mesh` as an example.
+
+```sh
+helm install seldon-v2-certs k8s/helm-charts/seldon-core-v2-certs/ -n seldon-mesh
+```
diff --git a/docs/getting-started/kubernetes-installation/security/aws-msk-mtls.md b/docs/getting-started/kubernetes-installation/security/aws-msk-mtls.md
index 1d16fe7b49..8569b55c89 100644
--- a/docs/getting-started/kubernetes-installation/security/aws-msk-mtls.md
+++ b/docs/getting-started/kubernetes-installation/security/aws-msk-mtls.md
@@ -2,50 +2,59 @@
 
 Seldon will run with [AWS MSK](https://aws.amazon.com/msk/).
 
-At present we support mTLS authentication to MSK which can be run from a Kubernetes cluster inside or outside Amazon. If running outside your MSK cluster must have a public endpoint.
+At present we support mTLS authentication to MSK which can be run from a Kubernetes
+cluster inside or outside Amazon. If running outside your MSK cluster must have a public endpoint.
 
 ## Considerations
 
 ### Public Access to MSK Cluster
 
-If you running your Kubernetes cluster outside AWS you will need to create a [public accessible MSK cluster](https://docs.aws.amazon.com/msk/latest/developerguide/public-access.html).
+If you running your Kubernetes cluster outside AWS you will need to create a
+[public accessible MSK cluster](https://docs.aws.amazon.com/msk/latest/developerguide/public-access.html).
 
-You will need to setup Kafka ACLs for your user where the username is the CommonName of the certificate of the client and allow full topic access. For example, to add a user with CN=myname to have full operations using the kafka-acls script with [mTLS config setup as described in AWS MSK docs](https://docs.aws.amazon.com/msk/latest/developerguide/msk-authentication.html):
+You will need to setup Kafka ACLs for your user where the username is the CommonName
+of the certificate of the client and allow full topic access. For example, to add a
+user with CN=myname to have full operations using the kafka-acls script with
+[mTLS config setup as described in AWS MSK docs](https://docs.aws.amazon.com/msk/latest/developerguide/msk-authentication.html):
 
-```
+```sh
 kafka-acls.sh --bootstrap-server <mTLS endpoint>  --add --allow-principal User:CN=myname --operation All --topic '*' --command-config client.properties
 ```
 
-You will also need to allow the connecting user to be able to perform admin tasks on the cluster so we can create topics on demand.
+You will also need to allow the connecting user to be able to perform admin tasks on the
+cluster so we can create topics on demand.
 
-```
+```sh
 kafka-acls.sh --bootstrap-server <nTLS endpoint>  --add --allow-principal User:CN=myname --operation All --cluster '*' --command-config client.properties
 ```
 
 You will need to allow group access also.
 
-```
+```sh
 kafka-acls.sh --bootstrap-server <mTKS endpoint>  --add --allow-principal User:CN=myname --operation All --group '*' --command-config client.properties
 ```
 
-
 ## Create TLS Kubernetes Secrets
 
-Create a secret for the client certificate you created. If you followed the [AWS MSK mTLS guide](https://docs.aws.amazon.com/msk/latest/developerguide/msk-authentication.html) you will need to export your private key from the JKS keystore. The certificate and chain will be provided in PEM format when you get the certificate signed. You can use these to create a secret with:
-
-  * tls.key : PEM formatted private key
-  * tls.crt : PEM formatted certificate
-  * ca.crt : Certificate chain
+Create a secret for the client certificate you created. If you followed the
+[AWS MSK mTLS guide](https://docs.aws.amazon.com/msk/latest/developerguide/msk-authentication.html)
+you will need to export your private key from the JKS keystore. The certificate and chain will be
+provided in PEM format when you get the certificate signed. You can use these to create a secret with:
+* tls.key : PEM formatted private key
+* tls.crt : PEM formatted certificate
+* ca.crt : Certificate chain
 
-```
+```sh
 kubectl create secret generic aws-msk-client --from-file=./tls.key --from-file=./tls.crt --from-file=./ca.crt -n seldon-mesh
 ```
 
-Create a secret for the broker certificate. If following the [AWS MSK mTLS guide](https://docs.aws.amazon.com/msk/latest/developerguide/msk-authentication.html) you will need to export the trusstore of Amazon into PEM format and save as ca.crt.
+Create a secret for the broker certificate. If following the
+[AWS MSK mTLS guide](https://docs.aws.amazon.com/msk/latest/developerguide/msk-authentication.html)
+you will need to export the trusstore of Amazon into PEM format and save as ca.crt.
 
 To extract certificates from truststore do:
 
-```
+```sh
 keytool -importkeystore -srckeystore truststore.jks    -destkeystore truststore.p12    -srcstoretype jks    -deststoretype pkcs12
 openssl pkcs12 -in truststore.p12  -nodes -out trust.pem
 cat trust.pem | sed  -ne '/-BEGIN CERTIFICATE-/,/-END CERTIFICATE-/p' > ca.crt
@@ -53,23 +62,31 @@ cat trust.pem | sed  -ne '/-BEGIN CERTIFICATE-/,/-END CERTIFICATE-/p' > ca.crt
 
 Add ca.crt to a secret.
 
-```
+```sh
 kubectl create secret generic aws-msk-broker-ca --from-file=./ca.crt -n seldon-mesh
 ```
 
-
-
 ### Example Helm install
 
 We provide a template you can extend in `k8s/samples/values-aws-msk-kafka-mtls.yaml.tmpl`:
 
-```{literalinclude} ../../../../../../k8s/samples/values-aws-msk-kafka-mtls.yaml.tmpl
-:language: yaml
+```yaml
+# k8s/samples/values-aws-msk-kafka-mtls.yaml.tmpl
+kafka:
+  bootstrap: <MSK Broker Endpoints>
+
+security:
+  kafka:
+    protocol: SSL
+    ssl:
+      client:
+        secret: aws-msk-client
+        brokerValidationSecret: aws-msk-broker-ca
 ```
 
 Copy this and modify by adding your broker endpoints.
 
-```
+```sh
 helm install seldon-v2 k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh -f k8s/samples/values-aws-msk-kafka-mtls.yaml --set kafka.bootstrap=<your aws msk broker endpoints>
 ```
 
@@ -81,4 +98,7 @@ First [check AWS MSK troubleshooting](https://docs.aws.amazon.com/msk/latest/dev
 
 Set the kafka config map debug setting to "all". For Helm install you can set `kafka.debug=all`.
 
-If you see an error from the producer in the Pipeline gateway complaining about not enough insync replicas then the replication factor Seldon is using is less than the cluster setting for `min.insync.replicas` which for a default AWS MSK cluster defaults to 2. Ensure this is equal to that of the cluster. This value can be set in the Helm chart with `kafka.topics.replicationFactor`.
+If you see an error from the producer in the Pipeline gateway complaining about not enough
+insync replicas then the replication factor Seldon is using is less than the cluster setting
+for `min.insync.replicas` which for a default AWS MSK cluster defaults to 2. Ensure this is
+equal to that of the cluster. This value can be set in the Helm chart with `kafka.topics.replicationFactor`.
diff --git a/docs/getting-started/kubernetes-installation/security/aws-msk-sasl.md b/docs/getting-started/kubernetes-installation/security/aws-msk-sasl.md
index 435136e316..5b2c4a9bde 100644
--- a/docs/getting-started/kubernetes-installation/security/aws-msk-sasl.md
+++ b/docs/getting-started/kubernetes-installation/security/aws-msk-sasl.md
@@ -4,10 +4,10 @@
 
 Seldon Core v2 can integrate with Amazon managed Apache Kafka (MSK). You can control access to your Amazon MSK clusters using sign-in credentials that are stored and secured using AWS Secrets Manager. Storing user credentials in Secrets Manager reduces the overhead of cluster authentication such as auditing, updating, and rotating credentials. Secrets Manager also lets you share user credentials across clusters.
 
-```{Note}
+{% hint style="info" %}
 Configuration of the AWS MSK instance itself is out of scope for this example.
 Please follow the official [AWS documentation](https://docs.aws.amazon.com/msk/latest/developerguide/what-is-msk.html) on how to enable SASL and public access to the Kafka cluster (if required).
-```
+{% endhint %}
 
 ## Setting up SASL/SCRAM authentication for an Amazon MSK cluster
 
@@ -17,7 +17,8 @@ Do not forget to also copy the `bootstrap.servers` which we will use it in our c
 
 ## Create Kubernetes Secret
 
-Seldon Core v2 expects password to be in form of K8s secret
+Seldon Core v2 expects password to be in form of K8s secret.
+
 ```bash
 kubectl create secret generic aws-msk-kafka-secret -n seldon-mesh --from-literal password="<MSK SASL Password>"
 ```
@@ -26,16 +27,32 @@ kubectl create secret generic aws-msk-kafka-secret -n seldon-mesh --from-literal
 
 Configure Seldon Core v2 by setting following Helm values:
 
-```{literalinclude} ../../../../../../k8s/samples/values-aws-msk-kafka-sasl-scram.yaml.tmpl
-:language: yaml
+```yaml
+# k8s/samples/values-aws-msk-kafka-sasl-scram.yaml.tmpl
+kafka:
+  bootstrap: <msk-bootstrap-server-endpoints>
+  topics:
+    replicationFactor: 3
+    numPartitions: 4
+
+security:
+    kafka:
+      protocol: SASL_SSL
+      sasl:
+        mechanism: SCRAM-SHA-512
+        client:
+          username: < username >
+          secret: aws-msk-kafka-secret
+          passwordPath: password
+      ssl:
+        client:
+          secret:
+          brokerValidationSecret:
 ```
 
 Note you may need to tweak `replicationFactor` and `numPartitions` to your cluster configuration.
 
-
-
 ## Troubleshooting
 
 - Please check Amazon MSK Troubleshooting [documentation](https://docs.aws.amazon.com/msk/latest/developerguide/troubleshooting.html).
-
 - Set the kafka config map debug setting to `all`. For Helm install you can set `kafka.debug=all`.
diff --git a/docs/getting-started/kubernetes-installation/security/azure-event-hub-sasl.md b/docs/getting-started/kubernetes-installation/security/azure-event-hub-sasl.md
index b15e1ba448..bc5e3e8e23 100644
--- a/docs/getting-started/kubernetes-installation/security/azure-event-hub-sasl.md
+++ b/docs/getting-started/kubernetes-installation/security/azure-event-hub-sasl.md
@@ -4,21 +4,22 @@
 
 Seldon Core v2 can integrate with Azure Event Hub via Kafka protocol.
 
-```{warning}
+{% hint style="warning" %}
 You will need at least `Standard` tier for your Event Hub Namespace as `Basic` tier does not support Kafka protocol.
-```
+{% endhint %}
 
-```{warning}
+{% hint style="warning" %}
 Seldon Core v2 creates 2 Kafka topics for each pipeline and model plus one global topic for errors.
-This means that total number of topics will be `2 x (#models + #pipelines) + 1` which will likely exceed the limit of `Standard` tier in Azure Event Hub.
-You can find more information on quotas, like the number of partitions per Event Hub, [here](https://learn.microsoft.com/en-us/azure/event-hubs/event-hubs-quotas#basic-vs-standard-vs-premium-vs-dedicated-tiers).
-```
+This means that total number of topics will be `2 x (#models + #pipelines) + 1` which will likely exceed
+the limit of `Standard` tier in Azure Event Hub. You can find more information on quotas, like the
+number of partitions per Event Hub, [here](https://learn.microsoft.com/en-us/azure/event-hubs/event-hubs-quotas#basic-vs-standard-vs-premium-vs-dedicated-tiers).
+{% endhint %}
 
 ## Prerequisites
 
-To start you will need to have an Azure Event Hub Namespace.
-You can create one following Azure quickstart [docs](https://learn.microsoft.com/en-gb/azure/event-hubs/event-hubs-create).
-Note that you do not need to create an Event Hub (topics) as Core v2 will require all the topics it needs automatically.
+To start you will need to have an Azure Event Hub Namespace. You can create one following Azure
+quickstart [docs](https://learn.microsoft.com/en-gb/azure/event-hubs/event-hubs-create). Note that
+you do not need to create an Event Hub (topics) as Core v2 will require all the topics it needs automatically.
 
 ## Create API Keys
 
@@ -28,18 +29,20 @@ To connect to Azure Event Hub provided Kafka API you need to obtain:
 
 You can obtain both using Azure Portal as documented [here](https://learn.microsoft.com/en-us/azure/event-hubs/event-hubs-get-connection-string?utm_source=pocket_saves#connection-string-for-a-namespace).
 
-```{note}
+{% hint style="warning" %}
 You should get the Connection String for a namespace level as we will need to dynamically create new topics.
-```
+{% endhint %}
 
 The Connection String should be in format of
-```
+
+```sh
 Endpoint=sb://<namespace>.servicebus.windows.net/;SharedAccessKeyName=XXXXXX;SharedAccessKey=XXXXXX
 ```
 
 ## Create Kubernetes Secret
 
-Seldon Core v2 expects password to be in form of K8s secret
+Seldon Core v2 expects password to be in form of K8s secret.
+
 ```bash
 kubectl create secret generic azure-kafka-secret -n seldon-mesh --from-literal password="Endpoint=sb://<namespace>.servicebus.windows.net/;SharedAccessKeyName=XXXXXX;SharedAccessKey=XXXXXX"
 ```
@@ -48,16 +51,33 @@ kubectl create secret generic azure-kafka-secret -n seldon-mesh --from-literal p
 
 Configure Seldon Core v2 by setting following Helm values:
 
-```{literalinclude} ../../../../../../k8s/samples/values-azure-event-hub-sasl.yaml.tmpl
-:language: yaml
+```yaml
+# k8s/samples/values-azure-event-hub-sasl.yaml.tmpl
+kafka:
+  bootstrap: <namespace>.servicebus.windows.net
+  topics:
+    replicationFactor: 3
+    numPartitions: 4
+
+security:
+  kafka:
+    protocol: SASL_SSL
+    sasl:
+      mechanism: "PLAIN"
+      client:
+        username: $ConnectionString
+        secret: azure-kafka-secret
+    ssl:
+      client:
+        secret:
+        brokerValidationSecret:
 ```
 
-Note you may need to tweak `replicationFactor` and `numPartitions` to your cluster configuration. The username should read `$ConnectionString` and this is not a variable for you to replace.
+Note you may need to tweak `replicationFactor` and `numPartitions` to your cluster configuration. The
+username should read `$ConnectionString` and this is not a variable for you to replace.
 
 ## Troubleshooting
 
 - First check Azure Event Hub [troubleshooting guide](https://learn.microsoft.com/en-us/azure/event-hubs/troubleshooting-guide).
-
 - Set the kafka config map debug setting to `all`. For Helm install you can set `kafka.debug=all`.
-
 - Verify that you did not hit quotas for topics or partitions in your Event Hub namespace.
diff --git a/docs/getting-started/kubernetes-installation/security/confluent-oauth.md b/docs/getting-started/kubernetes-installation/security/confluent-oauth.md
index 9d2e113a03..8d29184a3c 100644
--- a/docs/getting-started/kubernetes-installation/security/confluent-oauth.md
+++ b/docs/getting-started/kubernetes-installation/security/confluent-oauth.md
@@ -19,7 +19,6 @@ In your Confluent Cloud Console go to [Account & Access / Identity providers](ht
 
 See Confluent Cloud [documentation](https://docs.confluent.io/cloud/current/access-management/authenticate/oauth/identity-pools.html) for further details.
 
-
 ## Create Kubernetes Secret
 
 Seldon Core v2 expects oauth credentials to be in form of K8s secret
@@ -50,8 +49,29 @@ Client ID, client secret and token endpoint url should come from identity provid
 
 Configure Seldon Core v2 by setting following Helm values:
 
-```{literalinclude} ../../../../../../k8s/samples/values-confluent-kafka-oauth.yaml.tmpl
-:language: yaml
+```yaml
+# k8s/samples/values-confluent-kafka-oauth.yaml.tmpl
+kafka:
+  bootstrap: < Confluent Cloud Broker Endpoints >
+  topics:
+    replicationFactor: 3
+    numPartitions: 4
+  consumer:
+    messageMaxBytes: 8388608
+  producer:
+    messageMaxBytes: 8388608
+
+security:
+  kafka:
+    protocol: SASL_SSL
+    sasl:
+      mechanism: OAUTHBEARER
+      client:
+          secret: confluent-kafka-oauth
+    ssl:
+      client:
+        secret:
+        brokerValidationSecret:
 ```
 
 Note you may need to tweak `replicationFactor` and `numPartitions` to your cluster configuration.
diff --git a/docs/getting-started/kubernetes-installation/security/confluent-sasl.md b/docs/getting-started/kubernetes-installation/security/confluent-sasl.md
index b904a29b15..852e8216a4 100644
--- a/docs/getting-started/kubernetes-installation/security/confluent-sasl.md
+++ b/docs/getting-started/kubernetes-installation/security/confluent-sasl.md
@@ -9,7 +9,8 @@ In this example we use SASL security mechanism.
 ## Create API Keys
 
 In your Confluent Cloud environment create new API keys.
-The easiest way to obtain all required information is to head to `Clients` -> `New client` (choose e.g. Go) and generate new Kafka cluster API key from there.
+The easiest way to obtain all required information is to head to `Clients` -> `New client`
+(choose e.g. Go) and generate new Kafka cluster API key from there.
 
 This will generate for you:
 - `Key` (we use it as `username`)
@@ -30,15 +31,37 @@ kubectl create secret generic confluent-kafka-sasl -n seldon-mesh --from-literal
 
 Configure Seldon Core v2 by setting following Helm values:
 
-```{literalinclude} ../../../../../../k8s/samples/values-confluent-kafka-sasl.yaml.tmpl
-:language: yaml
+```yaml
+# k8s/samples/values-confluent-kafka-sasl.yaml.tmpl
+kafka:
+  bootstrap: < Confluent Cloud Broker Endpoints >
+  topics:
+    replicationFactor: 3
+    numPartitions: 4
+  consumer:
+    messageMaxBytes: 8388608
+  producer:
+    messageMaxBytes: 8388608
+
+security:
+  kafka:
+    protocol: SASL_SSL
+    sasl:
+      mechanism: "PLAIN"
+      client:
+        username: < username >
+        secret: confluent-kafka-sasl
+    ssl:
+      client:
+        secret:
+        brokerValidationSecret:
 ```
 
-Note you may need to tweak `replicationFactor` and `numPartitions` to your cluster configuration.
-
+{% hint style="info" %}
+You may need to tweak `replicationFactor` and `numPartitions` to your cluster configuration.
+{% endhint %}
 
 ## Troubleshooting
 
 - First check Confluent Cloud [documentation](https://docs.confluent.io/cloud/current/overview.html).
-
 - Set the kafka config map debug setting to `all`. For Helm install you can set `kafka.debug=all`.
diff --git a/docs/getting-started/kubernetes-installation/security/index.md b/docs/getting-started/kubernetes-installation/security/index.md
deleted file mode 100644
index 350f10ad1b..0000000000
--- a/docs/getting-started/kubernetes-installation/security/index.md
+++ /dev/null
@@ -1,104 +0,0 @@
-# Security
-
-Seldon can be run with secure control plane and data plane operations. There are three areas of concern:
-
- * [Control Plane](#control-plane)
- * [Kafka](#kafka)
- * [Data Plane](#data-plane)
-
-The various communication points between services are shown in the diagram below:
-
-![networking](networking.jpg)
-
-## Control Plane
-
-TLS control plane activation is switched on and off via the environment variable: `CONTROL_PLANE_SECURITY_PROTOCOL` whose values can be `PLAINTEXT` or `SSL`.
-
-Certificates will be loaded and used for the control plane gRPC services. The secrets or folders will be watched for updates (on certificate renewal) and automatically loaded again.
-
-### Helm Control Plane Install
-
-When installing `seldon-core-v2-setup` you can set the secret names for your certificates. If using cert-manager example discussed below this would be as follows:
-
-```bash
-helm install seldon-v2 k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh \
-      --set security.controlplane.protocol=SSL
-```
-
-## Kafka
-
-Kafka secure activation is switched on and off via the environment variable: `KAFKA_SECURITY_PROTOCOL` whose values can be `PLAINTEXT`, `SSL` or `SASL_SSL`.
-
-Examples are shown below:
-
- * [mTLS Strimzi](strimzi-mtls.md) example
- * [mTLS AWS MSK](aws-msk-mtls.md) example
- * [SASL PLAIN with Confluent Cloud](confluent-sasl.md) example
- * [SASL PLAIN with Azure Event Hub](azure-event-hub-sasl.md) example
- * [SASL SCRAM with Strimzi](strimzi-sasl.md) example
- * [SASL SCRAM with AWS MSK](aws-msk-sasl.md) example
- * [SASL OAUTH with Confluent Cloud](confluent-oauth.md) example
-
-## Data Plane
-
-TLS Data plane activation is switched on and off via the environment variable: `ENVOY_SECURITY_PROTOCOL` whose values can be `PLAINTEXT` or `SSL`.
-
-When activated this ensures TLS is used to communicate to Envoy via the xDS server as well as using the SDS service to send cretificates to envoy to use for  upstream and downstream networking. Downstream is the external access to Seldon and upstream is the path from Envoy to the model servers or pipeline gateway.
-
-### Helm Data Plane Install
-
-When installing `seldon-core-v2-setup` you can set data plane operations to TLS as below. This assumes the secrets installed by the helm chart at the end of this section.
-
-```bash
-helm install seldon-v2 k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh --set security.envoy.protocol=SSL
-```
-
-The above uses default secret names defined for the certificates installed. You can change the names of the required certificate secrets as shown in a longer configuration below (again using the default names for illustration).
-
-For this we use the following updated Helm values (`k8s/samples/values-tls-dataplane-example.yaml`):
-
-```{literalinclude} ../../../../../../k8s/samples/values-tls-dataplane-example.yaml
-:language: yaml
-```
-
-We use Envoy internally to direct traffic and in Envoy's terminology `upstream` is for internal model servers called from Envoy while the downstream server is the entrypoint server running in Envoy to receive grpc and REST calls. The above settings ensure mTLS for internal "upstream" traffic while provides a standard SSL non-mTLS entrpoint.
-
-To use the above with the seldon CLI you would need a custom config file as follow:
-
-```json
-{
-    "dataplane": {
-	"tls": true,
-	"skipSSLVerify": true
-    }
-}
-
-```
-
-We skip SSL Verify as these are internal self-signed certificates. For production use you would change this to the correct DNS name you are exposing the Seldon entrypoint.
-
-## Cetificate Providers
-
-The installer/cluster controller for Seldon needs to provide the certificates. As part of Seldon we provide an example set of certificate issuers and certificates using [cert-manager](https://cert-manager.io/).
-
-### Helm
-
-You can install Certificates into the desired namespace, here we use `seldon-mesh` as an example.
-
-```
-helm install seldon-v2-certs k8s/helm-charts/seldon-core-v2-certs/ -n seldon-mesh
-```
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-strimzi-mtls.md
-strimzi-sasl.md
-confluent-sasl.md
-confluent-oauth.md
-azure-event-hub-sasl.md
-aws-msk-sasl.md
-aws-msk-mtls.md
-reference.md
-```
diff --git a/docs/getting-started/kubernetes-installation/security/networking.jpg b/docs/getting-started/kubernetes-installation/security/networking.jpg
deleted file mode 100644
index 5073ddb83e369bfdb0b631d2363d032e6d8c567e..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/docs/getting-started/kubernetes-installation/security/reference.md b/docs/getting-started/kubernetes-installation/security/reference.md
index 5ebb625521..70c98b82a1 100644
--- a/docs/getting-started/kubernetes-installation/security/reference.md
+++ b/docs/getting-started/kubernetes-installation/security/reference.md
@@ -2,107 +2,134 @@
 
 ## Helm Settings
 
-```{literalinclude} ../../../../../../k8s/helm-charts/seldon-core-v2-setup/values.yaml
-:language: yaml
-   :language: golang
-   :start-after: # Security settings
-   :end-before: opentelemetry
+```yaml
+# k8s/helm-charts/seldon-core-v2-setup/values.yaml
+security:
+  controlplane:
+    protocol: PLAINTEXT
+    ssl:
+      server:
+        secret: seldon-controlplane-server
+        clientValidationSecret: seldon-controlplane-client
+        keyPath: /tmp/certs/cps/tls.key
+        crtPath: /tmp/certs/cps/tls.crt
+        caPath: /tmp/certs/cps/ca.crt
+        clientCaPath: /tmp/certs/cpc/ca.crt
+      client:
+        secret: seldon-controlplane-client
+        serverValidationSecret: seldon-controlplane-server
+        keyPath: /tmp/certs/cpc/tls.key
+        crtPath: /tmp/certs/cpc/tls.crt
+        caPath: /tmp/certs/cpc/ca.crt
+        serverCaPath: /tmp/certs/cps/ca.crt
+  kafka:
+    protocol: PLAINTEXT
+    sasl:
+      mechanism: SCRAM-SHA-512
+      client:
+        username: seldon
+        secret:
+        passwordPath: password
+    ssl:
+      client:
+        secret:
+        brokerValidationSecret:
+        keyPath: /tmp/certs/kafka/client/tls.key
+        crtPath: /tmp/certs/kafka/client/tls.crt
+        caPath: /tmp/certs/kafka/client/ca.crt
+        brokerCaPath: /tmp/certs/kafka/broker/ca.crt
+        endpointIdentificationAlgorithm:
+  envoy:
+    protocol: PLAINTEXT
+    ssl:
+      upstream:
+        server:
+          secret: seldon-upstream-server
+          clientValidationSecret: seldon-upstream-client
+          keyPath: /tmp/certs/dus/tls.key
+          crtPath: /tmp/certs/dus/tls.crt
+          caPath: /tmp/certs/dus/ca.crt
+          clientCaPath: /tmp/certs/duc/ca.crt
+        client:
+          secret: seldon-upstream-client
+          serverValidationSecret: seldon-upstream-server
+          keyPath: /tmp/certs/duc/tls.key
+          crtPath: /tmp/certs/duc/tls.crt
+          caPath: /tmp/certs/duc/ca.crt
+          serverCaPath: /tmp/certs/dus/ca.crt
+      downstream:
+        server:
+          secret: seldon-downstream-server
+          clientValidationSecret:
+          keyPath: /tmp/certs/dds/tls.key
+          crtPath: /tmp/certs/dds/tls.crt
+          caPath: /tmp/certs/dds/ca.crt
+          clientCaPath: /tmp/certs/ddc/ca.crt
+        client:
+          mtls: false
+          secret:
+          serverValidationSecret: seldon-downstream-server
+          keyPath: /tmp/certs/ddc/tls.key
+          crtPath: /tmp/certs/ddc/tls.crt
+          caPath: /tmp/certs/ddc/ca.crt
+          serverCaPath: /tmp/certs/dds/ca.crt
+
+# A list of image pull secrets
+imagePullSecrets:
 ```
 
-
 ## Environment variables
 
 Kubernetes secrets and mounted files can be used to provide the certificates in PEM format. These are controlled by environment variables for server or client depending on the component:
 
 ### Control Plane
 
-```{list-table}
-:header-rows: 1
-
-* - EnvVar
-  - Value
-* - CONTROL_PLANE_SECURITY_PROTOCOL
-  - SSL or PLAINTEXT
-```
+| EnvVar | Value |
+| --- | --- |
+| CONTROL_PLANE_SECURITY_PROTOCOL | SSL or PLAINTEXT |
 
 For a server (scheduler):
 
-```{list-table}
-:header-rows: 1
-
-* - EnvVar
-  - Value
-* - CONTROL_PLANE_SERVER_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the certificates
-* - CONTROL_PLANE_CLIENT_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the validation ca roots to verify the client certificate
-* - CONTROL_PLANE_SERVER_TLS_KEY_LOCATION
-  - the path to the TLS private key
-* - CONTROL_PLANE_SERVER_TLS_CRT_LOCATION
-  - the path to the TLS certificate
-* - CONTROL_PLANE_SERVER_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the server
-* - CONTROL_PLANE_CLIENT_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the client for mTLS verification
-```
+| EnvVar | Value |
+| --- | --- |
+| CONTROL_PLANE_SERVER_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the certificates |
+| CONTROL_PLANE_CLIENT_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the validation ca roots to verify the client certificate |
+| CONTROL_PLANE_SERVER_TLS_KEY_LOCATION | the path to the TLS private key |
+| CONTROL_PLANE_SERVER_TLS_CRT_LOCATION | the path to the TLS certificate |
+| CONTROL_PLANE_SERVER_TLS_CA_LOCATION | the path to the TLS CA chain for the server |
+| CONTROL_PLANE_CLIENT_TLS_CA_LOCATION | the path to the TLS CA chain for the client for mTLS verification |
 
 For a client (agent, modelgateway, hodometer, CRD controller):
 
 
-```{list-table}
-:header-rows: 1
-
-* - EnvVar
-  - Value
-* - CONTROL_PLANE_CLIENT_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the certificates
-* - CONTROL_PLANE_SERVER_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the validation ca roots to verify the server certificate
-* - CONTROL_PLANE_CLIENT_TLS_KEY_LOCATION
-  - the path to the TLS private key
-* - CONTROL_PLANE_CLIENT_TLS_CRT_LOCATION
-  - the path to the TLS certificate
-* - CONTROL_PLANE_CLIENT_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the client
-* - CONTROL_PLANE_SERVER_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the server for mTLS verification
-```
+| EnvVar | Value |
+| --- | --- |
+| CONTROL_PLANE_CLIENT_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the certificates |
+| CONTROL_PLANE_SERVER_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the validation ca roots to verify the server certificate |
+| CONTROL_PLANE_CLIENT_TLS_KEY_LOCATION | the path to the TLS private key |
+| CONTROL_PLANE_CLIENT_TLS_CRT_LOCATION | the path to the TLS certificate |
+| CONTROL_PLANE_CLIENT_TLS_CA_LOCATION | the path to the TLS CA chain for the client |
+| CONTROL_PLANE_SERVER_TLS_CA_LOCATION | the path to the TLS CA chain for the server for mTLS verification |
+
 
 ### Kafka
 
-```{list-table}
-:header-rows: 1
+| EnvVar | Value |
+| --- | --- |
+| KAFKA_SECURITY_PROTOCOL | PLAINTXT, SSL, or SASL_SSL |
 
-* - EnvVar
-  - Value
-* - KAFKA_SECURITY_PROTOCOL
-  - PLAINTXT, SSL, or SASL_SSL
-```
+| EnvVar | Value |
+| --- | --- |
+| KAFKA_CLIENT_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the Kafka client certificate |
+| KAFKA_CLIENT_SERVER_TLS_KEY_LOCATION | the path to the TLS private key |
+| KAFKA_CLIENT_SERVER_TLS_CRT_LOCATION | the path to the TLS certificate |
+| KAFKA_CLIENT_SERVER_TLS_CA_LOCATION | the path to the CA chain for the client |
+| KAFKA_BROKER_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the validation ca roots for the kafka broker |
+| KAFKA_BROKER_TLS_CA_LOCATION | The path to the broker validatiob CA chain |
+| KAFKA_CLIENT_SASL_USERNAME | SASL username |
+| KAFKA_CLIENT_SASL_SECRET_NAME | the name of the namespaced secret which holds the SASL password |
+| KAFKA_CLIENT_SASL_PASSWORD_LOCATION | the path to the file containing the SASL password |
 
-```{list-table}
-:header-rows: 1
-
-* - EnvVar
-  - Value
-* - KAFKA_CLIENT_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the Kafka client certificate
-* - KAFKA_CLIENT_SERVER_TLS_KEY_LOCATION
-  - the path to the TLS private key
-* - KAFKA_CLIENT_SERVER_TLS_CRT_LOCATION
-  - the path to the TLS certificate
-* - KAFKA_CLIENT_SERVER_TLS_CA_LOCATION
-  - the path to the CA chain for the client
-* - KAFKA_BROKER_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the validation ca roots for the kafka broker
-* - KAFKA_BROKER_TLS_CA_LOCATION
-  - The path to the broker validatiob CA chain
-* - KAFKA_CLIENT_SASL_USERNAME
-  - SASL username
-* - KAFKA_CLIENT_SASL_SECRET_NAME
-  - the name of the namespaced secret which holds the SASL password
-* - KAFKA_CLIENT_SASL_PASSWORD_LOCATION
-  - the path to the file containing the SASL password
-```
 
 ### Envoy
 
@@ -110,87 +137,47 @@ Envoy xDS server will use the control plane server and client certificates defin
 
 Downstream server
 
-```{list-table}
-:header-rows: 1
-
-* - EnvVar
-  - Value
-* - ENVOY_DOWNSTREAM_SERVER_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the certificates
-* - ENVOY_DOWNSTREAM_CLIENT_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the validation ca roots to verify the client certificate
-* - ENVOY_DOWNSTREAM_SERVER_TLS_KEY_LOCATION
-  - the path to the TLS private key
-* - ENVOY_DOWNSTREAM_SERVER_TLS_CRT_LOCATION
-  - the path to the TLS certificate
-* - ENVOY_DOWNSTREAM_SERVER_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the server
-* - ENVOY_DOWNSTREAM_CLIENT_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the client for mTLS verification
-```
+| EnvVar | Value |
+| --- | --- |
+| ENVOY_DOWNSTREAM_SERVER_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the certificates |
+| ENVOY_DOWNSTREAM_CLIENT_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the validation ca roots to verify the client certificate |
+| ENVOY_DOWNSTREAM_SERVER_TLS_KEY_LOCATION | the path to the TLS private key |
+| ENVOY_DOWNSTREAM_SERVER_TLS_CRT_LOCATION | the path to the TLS certificate |
+| ENVOY_DOWNSTREAM_SERVER_TLS_CA_LOCATION | the path to the TLS CA chain for the server |
+| ENVOY_DOWNSTREAM_CLIENT_TLS_CA_LOCATION | the path to the TLS CA chain for the client for mTLS verification |
+
 
 Downstream client
 
-```{list-table}
-:header-rows: 1
-
-* - EnvVar
-  - Value
-* - ENVOY_DOWNSTREAM_CLIENT_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the certificates
-* - ENVOY_DOWNSTREAM_SERVER_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the validation ca roots to verify the server certificate
-* - ENVOY_DOWNSTREAM_CLIENT_TLS_KEY_LOCATION
-  - the path to the TLS private key
-* - ENVOY_DOWNSTREAM_CLIENT_TLS_CRT_LOCATION
-  - the path to the TLS certificate
-* - ENVOY_DOWNSTREAM_CLIENT_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the server
-* - ENVOY_DOWNSTREAM_SERVER_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the server for mTLS verification
-```
+| EnvVar | Value |
+| --- | --- |
+| ENVOY_DOWNSTREAM_CLIENT_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the certificates |
+| ENVOY_DOWNSTREAM_SERVER_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the validation ca roots to verify the server certificate |
+| ENVOY_DOWNSTREAM_CLIENT_TLS_KEY_LOCATION | the path to the TLS private key |
+| ENVOY_DOWNSTREAM_CLIENT_TLS_CRT_LOCATION | the path to the TLS certificate |
+| ENVOY_DOWNSTREAM_CLIENT_TLS_CA_LOCATION | the path to the TLS CA chain for the server |
+| ENVOY_DOWNSTREAM_SERVER_TLS_CA_LOCATION | the path to the TLS CA chain for the server for mTLS verification |
 
-Upstream server
-
-```{list-table}
-:header-rows: 1
-
-* - EnvVar
-  - Value
-* - ENVOY_UPSTREAM_SERVER_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the certificates
-* - ENVOY_UPSTREAM_CLIENT_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the validation ca roots to verify the client certificate
-* - ENVOY_UPSTREAM_SERVER_TLS_KEY_LOCATION
-  - the path to the TLS private key
-* - ENVOY_UPSTREAM_SERVER_TLS_CRT_LOCATION
-  - the path to the TLS certificate
-* - ENVOY_UPSTREAM_SERVER_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the server
-* - ENVOY_UPSTREAM_CLIENT_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the client for mTLS verification
-```
 
-Upstream client
+Upstream server
 
-```{list-table}
-:header-rows: 1
-
-* - EnvVar
-  - Value
-* - ENVOY_UPSTREAM_CLIENT_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the certificates
-* - ENVOY_UPSTREAM_SERVER_TLS_SECRET_NAME
-  - (optional) the name of the namespaced secret which holds the validation ca roots to verify the server certificate
-* - ENVOY_UPSTREAM_CLIENT_TLS_KEY_LOCATION
-  - the path to the TLS private key
-* - ENVOY_UPSTREAM_CLIENT_TLS_CRT_LOCATION
-  - the path to the TLS certificate
-* - ENVOY_UPSTREAM_CLIENT_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the server
-* - ENVOY_UPSTREAM_SERVER_TLS_CA_LOCATION
-  - the path to the TLS CA chain for the server for mTLS verification
-```
+| EnvVar | Value |
+| --- | --- |
+| ENVOY_UPSTREAM_SERVER_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the certificates |
+| ENVOY_UPSTREAM_CLIENT_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the validation ca roots to verify the client certificate |
+| ENVOY_UPSTREAM_SERVER_TLS_KEY_LOCATION | the path to the TLS private key |
+| ENVOY_UPSTREAM_SERVER_TLS_CRT_LOCATION | the path to the TLS certificate |
+| ENVOY_UPSTREAM_SERVER_TLS_CA_LOCATION | the path to the TLS CA chain for the server |
+| ENVOY_UPSTREAM_CLIENT_TLS_CA_LOCATION | the path to the TLS CA chain for the client for mTLS verification |
 
 
+Upstream client
 
+| EnvVar | Value |
+| --- | --- |
+| ENVOY_UPSTREAM_CLIENT_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the certificates |
+| ENVOY_UPSTREAM_SERVER_TLS_SECRET_NAME | (optional) the name of the namespaced secret which holds the validation ca roots to verify the server certificate |
+| ENVOY_UPSTREAM_CLIENT_TLS_KEY_LOCATION | the path to the TLS private key |
+| ENVOY_UPSTREAM_CLIENT_TLS_CRT_LOCATION | the path to the TLS certificate |
+| ENVOY_UPSTREAM_CLIENT_TLS_CA_LOCATION | the path to the TLS CA chain for the server |
+| ENVOY_UPSTREAM_SERVER_TLS_CA_LOCATION | the path to the TLS CA chain for the server for mTLS verificatio |n
diff --git a/docs/getting-started/kubernetes-installation/security/strimzi-mtls.md b/docs/getting-started/kubernetes-installation/security/strimzi-mtls.md
index 5d438e92f8..a2cc715861 100644
--- a/docs/getting-started/kubernetes-installation/security/strimzi-mtls.md
+++ b/docs/getting-started/kubernetes-installation/security/strimzi-mtls.md
@@ -4,7 +4,7 @@
 
 If you have installed Strimzi we have an example Helm chart to create a Kafka cluster for seldon and an associated user in `kafka/strimzi` folder. Ensure the `tls` is enabled with:
 
-```
+```yaml
 broker:
   tls:
     enabled: true
@@ -20,31 +20,56 @@ The Ansible `setup-ecosystem` playbook will also install Strimzi and include a m
 
 Create a Kafka User `seldon` in the namespace seldon was installed. This assumes Strimzi Kafka cluster is installed in the same namespace or is running with cluster wide permissions. Our Ansible scripts to setup the ecosystem will also create this user if tls is active.
 
-```{literalinclude} ../../../../../../k8s/samples/strimzi-example-tls-user.yaml
-:language: yaml
+```yaml
+# k8s/samples/strimzi-example-tls-user.yaml
+apiVersion: kafka.strimzi.io/v1beta2
+kind: KafkaUser
+metadata:
+  name: seldon
+  labels:
+    strimzi.io/cluster: seldon
+spec:
+  authentication:
+    type: tls
 ```
 
 If you don't have this user you can install it with in your desired namespace (here `seldon-mesh`):
 
-```
+```sh
 kubectl create -f k8s/samples/strimzi-example-tls-user.yaml -n seldon-mesh
 ```
 
-Install seldon with the Strimzi certificate secrets using a custom values file. This sets the secret created by Strimzi for the user created above (`seldon`) and targets the server certificate authority secret from the name of the cluster created on install of the Kafka cluster (`seldon-cluster-ca-cert`).
+Install seldon with the Strimzi certificate secrets using a custom values file. This sets
+the secret created by Strimzi for the user created above (`seldon`) and targets the server
+certificate authority secret from the name of the cluster created on install of the Kafka
+cluster (`seldon-cluster-ca-cert`).
 
 Configure Seldon Core v2 by setting following Helm values:
 
-```{literalinclude} ../../../../../../k8s/samples/values-strimzi-kafka-mtls.yaml
-:language: yaml
+```yaml
+# k8s/samples/values-strimzi-kafka-mtls.yaml
+kafka:
+  bootstrap: seldon-kafka-bootstrap.seldon-mesh.svc.cluster.local:9093
+
+security:
+  kafka:
+    protocol: SSL
+    ssl:
+      client:
+        secret: seldon
+        brokerValidationSecret: seldon-cluster-ca-cert
+        keyPath: /tmp/certs/kafka/client/user.key
+        crtPath: /tmp/certs/kafka/client/user.crt
+        caPath: /tmp/certs/kafka/client/ca.crt
+        brokerCaPath: /tmp/certs/kafka/broker/ca.crt
 ```
 
-
-```
+```sh
 helm install seldon-v2 k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh -f k8s/samples/values-strimzi-kafka-mtls.yaml
 ```
 
 You can now go ahead and install a SeldonRuntime in your desired install namespace (here `seldon-mesh`), e.g.
 
-```
+```sh
 helm install seldon-v2-runtime ../k8s/helm-charts/seldon-core-v2-runtime  -n seldon-mesh
 ```
diff --git a/docs/getting-started/kubernetes-installation/security/strimzi-sasl.md b/docs/getting-started/kubernetes-installation/security/strimzi-sasl.md
index c7c7a1a071..5a047b71da 100644
--- a/docs/getting-started/kubernetes-installation/security/strimzi-sasl.md
+++ b/docs/getting-started/kubernetes-installation/security/strimzi-sasl.md
@@ -3,13 +3,18 @@
 Create a Strimzi Kafka cluster with SASL_SSL enabled.
 This can be done with our Ansible scripts by running the following from the `ansible/` folder:
 
-```
+```sh
 ansible-playbook playbooks/setup-ecosystem.yaml -e @../k8s/samples/ansible-strimzi-kafka-sasl-scram.yaml -e strimzi_kafka_operator_feature_gates=""
 ```
 
 The referenced SASL/SCRAM YAML file looks like the below:
-```{literalinclude} ../../../../../../k8s/samples/ansible-strimzi-kafka-sasl-scram.yaml
-:language: yaml
+```yaml
+# k8s/samples/ansible-strimzi-kafka-sasl-scram.yaml
+seldon_kafka_cluster_values:
+  broker:
+    tls:
+      authentication:
+        type: scram-sha-512
 ```
 
 This will use the Strimzi Helm chart provided in Core v2.
@@ -20,10 +25,26 @@ This sets the secret created by Strimzi for the user created above (`seldon`) an
 
 Configure Seldon Core v2 by setting following Helm values:
 
-```{literalinclude} ../../../../../../k8s/samples/values-strimzi-kafka-sasl-scram.yaml
-:language: yaml
+```yaml
+# k8s/samples/values-strimzi-kafka-sasl-scram.yaml
+kafka:
+  bootstrap: seldon-kafka-bootstrap.seldon-mesh.svc.cluster.local:9093
+
+security:
+  kafka:
+    protocol: SASL_SSL
+    sasl:
+      mechanism: SCRAM-SHA-512
+      client:
+        username: seldon
+        secret: seldon
+        passwordPath: password
+    ssl:
+      client:
+        brokerValidationSecret: seldon-cluster-ca-cert
+        brokerCaPath: /tmp/certs/kafka/broker/ca.crt
 ```
 
-```
+```sh
 helm install seldon-v2 k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh -f k8s/samples/values-strimzi-kafka-sasl-scram.yaml
 ```

From 5e3f40403876d0ec30f8e7038a933acfc3cd1a8c Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 16:57:05 +1000
Subject: [PATCH 05/32] rearranged models directory and enhanced different docs

---
 docs/models/README.md                         |  74 ++++++++
 docs/models/index.md                          |  61 -------
 docs/models/inference-artifacts.md            |  82 +++++++++
 docs/models/inference-artifacts/index.md      | 162 ------------------
 docs/models/mms.md                            |  79 +++++++++
 docs/models/mms/mms.md                        |  41 -----
 docs/models/mms/mms.png                       | Bin 40700 -> 0 bytes
 docs/models/mms/overcommit.png                | Bin 73855 -> 0 bytes
 docs/models/parameterized-models/README.md    |  27 +++
 docs/models/parameterized-models/index.md     |  21 ---
 .../parameterized-models/pandasquery.md       |  76 ++++++--
 docs/models/rclone.md                         |   9 +
 docs/models/rclone/index.md                   |   7 -
 13 files changed, 333 insertions(+), 306 deletions(-)
 create mode 100644 docs/models/README.md
 delete mode 100644 docs/models/index.md
 create mode 100644 docs/models/inference-artifacts.md
 delete mode 100644 docs/models/inference-artifacts/index.md
 create mode 100644 docs/models/mms.md
 delete mode 100644 docs/models/mms/mms.md
 delete mode 100644 docs/models/mms/mms.png
 delete mode 100644 docs/models/mms/overcommit.png
 create mode 100644 docs/models/parameterized-models/README.md
 delete mode 100644 docs/models/parameterized-models/index.md
 create mode 100644 docs/models/rclone.md
 delete mode 100644 docs/models/rclone/index.md

diff --git a/docs/models/README.md b/docs/models/README.md
new file mode 100644
index 0000000000..b3678bcbf2
--- /dev/null
+++ b/docs/models/README.md
@@ -0,0 +1,74 @@
+# Models
+
+Models provide the atomic building blocks of Seldon. They represents machine learning models,
+drift detectors, outlier detectors, explainers, feature transformations, and more complex routing
+models such as multi-armed bandits.
+
+* Seldon can handle a wide range of [inference artifacts](./inference-artifacts.md)
+* Artifacts can be stored on any of the 40 or more cloud storage technologies as well as from
+local (mounted) folder as discussed [here](./rclone.md).
+
+## Kubernetes Example
+
+A Kubernetes yaml example is shown below for a SKLearn model for iris classification:
+
+```yaml
+# samples/models/sklearn-iris-gs.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
+```
+
+Its Kubernetes `spec` has two core requirements
+
+* A `storageUri` specifying the location of the artifact. This can be any rclone URI specification.
+* A `requirements` list which provides tags that need to be matched by the Server that can run
+this artifact type. By default when you install Seldon we provide a set of Servers that cover a
+range of artifact types.
+
+
+## GRPC Example
+
+You can also load models directly over the scheduler grpc service. An example is shown below use grpcurl tool:
+
+```bash
+!grpcurl -d '{"model":{ \
+              "meta":{"name":"iris"},\
+              "modelSpec":{"uri":"gs://seldon-models/mlserver/iris",\
+                           "requirements":["sklearn"],\
+                           "memoryBytes":500},\
+              "deploymentSpec":{"replicas":1}}}' \
+         -plaintext \
+         -import-path ../../apis \
+         -proto apis/mlops/scheduler/scheduler.proto  0.0.0.0:9004 seldon.mlops.scheduler.Scheduler/LoadModel
+```
+
+The proto buffer definitions for the scheduler are outlined [here](../apis/scheduler.md).
+
+## Multi-model Serving with Overcommit
+
+Multi-model serving is an architecture pattern where one ML inference server hosts multiple models
+at the same time. It is a feature provided out of the box by Nvidia Triton and Seldon MLServer.
+Multi-model serving reduces infrastructure hardware requirements (e.g. expensive GPUs) which enables
+the deployment of a large number of models while making it efficient to operate the system at scale.
+
+Seldon Core v2 leverages multi-model serving by design and it is the default option for deploying
+models. The system will find an appropriate server to load the model onto based on requirements that
+the user defines in the `Model` deployment definition.
+
+Moreover, in many cases demand patterns allow for further Overcommit of resources. Seldon Core v2
+is able to register more models than what can be served by the provisioned (memory) infrastructure
+and will swap models dynamically according to least used without adding significant latency overheads
+to inference workload.
+
+See [Multi-model serving](mms.md) for more information.
+
+## Autoscaling of Models
+
+See [here](../kubernetes/autoscaling.md) for discussion of autoscaling of models.
diff --git a/docs/models/index.md b/docs/models/index.md
deleted file mode 100644
index 1e3ca2f06a..0000000000
--- a/docs/models/index.md
+++ /dev/null
@@ -1,61 +0,0 @@
-# Models
-
-Models provide the atomic building blocks of Seldon. They represents machine learning models, drift detectors, outlier detectors, explainers, feature transformations, and more complex routing models such as multi-armed bandits.
-
- * Seldon can handle a wide range of [inference artifacts](./inference-artifacts/index.md)
- * Artifacts can be stored on any of the 40 or more cloud storage technologies as well as from local (mounted) folder as discussed [here](./rclone/index.md).
-
-## Kubernetes Example
-
-A Kubernetes yaml example is shown below for a SKLearn model for iris classification:
-
-```{literalinclude} ../../../../samples/models/sklearn-iris-gs.yaml 
-:language: yaml
-```
-
-Its Kubernetes `spec` has two core requirements
-
- * A `storageUri` specifying the location of the artifact. This can be any rclone URI specification.
- * A `requirements` list which provides tags that need to be matched by the Server that can run this artifact type. By default when you install Seldon we provide a set of Servers that cover a range of artifact types.
-
-
-## GRPC Example
-
-You can also load models directly over the scheduler grpc service. An example is shown below use grpcurl tool:
-
-```bash
-!grpcurl -d '{"model":{ \
-              "meta":{"name":"iris"},\
-              "modelSpec":{"uri":"gs://seldon-models/mlserver/iris",\
-                           "requirements":["sklearn"],\
-                           "memoryBytes":500},\
-              "deploymentSpec":{"replicas":1}}}' \
-         -plaintext \
-         -import-path ../../apis \
-         -proto apis/mlops/scheduler/scheduler.proto  0.0.0.0:9004 seldon.mlops.scheduler.Scheduler/LoadModel
-```
-
-The proto buffer definitions for the scheduler are outlined [here](../apis/scheduler/index.md).
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-inference-artifacts/index.md
-rclone/index.md
-parameterized-models/index.md
-```
-
-## Multi-model Serving with Overcommit
-
-Multi-model serving is an architecture pattern where one ML inference server hosts multiple models at the same time. It is a feature provided out of the box by Nvidia Triton and Seldon MLServer. Multi-model serving reduces infrastructure hardware requirements (e.g. expensive GPUs) which enables the deployment of a large number of models while making it efficient to operate the system at scale.
-
-Seldon Core v2 leverages multi-model serving by design and it is the default option for deploying models. The system will find an appropriate server to load the model onto based on requirements that the user defines in the `Model` deployment definition.
-
-Moreover, in many cases demand patterns allow for further Overcommit of resources. Seldon Core v2 is able to register more models than what can be served by the provisioned (memory) infrastructure and will swap models dynamically according to least used without adding significant latency overheads to inference workload.
-
-See [Multi-model serving](mms/mms.md) for more information. 
-
-## Autoscaling of Models
-
-See [here](../kubernetes/autoscaling/index.md) for discussion of autoscaling of models.
diff --git a/docs/models/inference-artifacts.md b/docs/models/inference-artifacts.md
new file mode 100644
index 0000000000..2797a66cf5
--- /dev/null
+++ b/docs/models/inference-artifacts.md
@@ -0,0 +1,82 @@
+# Inference Artifacts
+
+To run your model inside Seldon you must supply an inference artifact that can be downloaded and run
+on one of MLServer or Triton inference servers. We list artifacts below by alphabetical order below.
+
+| Type | Server | Tag | Example |
+| ---- | ------ | --- | ------- |
+| Alibi-Detect | MLServer | `alibi-detect` | [example](../examples/cifar10.md) |
+| Alibi-Explain | MLServer | `alibi-explain` | [example](../examples/explainer-examples.md) |
+| DALI | Triton | `dali` | TBC |
+| Huggingface | MLServer | `huggingface` | [example](../examples/huggingface.md) |
+| LightGBM | MLServer | `lightgbm` | [example](../examples/model-zoo.md#lightgbm-model) |
+| MLFlow | MLServer | `mlflow` | [example](../examples/model-zoo.md#mlflow-wine-model) |
+| ONNX | Triton | `onnx` | [example](../examples/model-zoo.md#onnx-mnist-model) |
+| OpenVino | Triton | `openvino` | TBC |
+| Custom Python | MLServer | `python, mlserver` | [example](../examples/pandasquery.md) |
+| Custom Python | Triton | `python, triton` | [example](https://github.com/SeldonIO/triton-python-examples) |
+| PyTorch | Triton | `pytorch` | [example](../examples/model-zoo.md#pytorch-mnist-model)
+| SKLearn | MLServer | `sklearn` | [example](../examples/income.md) |
+| Spark Mlib | MLServer | `spark-mlib` | TBC |
+| Tensorflow | Triton | `tensorflow` | [example](../examples/cifar10.md) |
+| TensorRT | Triton | `tensorrt` | TBC |
+| Triton FIL | Triton | `fil` | TBC |
+| XGBoost | MLServer | `xgboost` | [example](../examples/model-zoo.md#xgboost-model) |
+
+
+## Saving Model artifacts
+
+For many machine learning artifacts you can simply save them to a folder and load them into seldon
+core v2. Details are given below as well as a link to creating a custom model settings file if needed.
+
+
+| Type | Notes | Custom Model Settings |
+| ---- | ----- | --------------------- |
+| Alibi-Detect | [Save model using Alibi-Detect](https://docs.seldon.io/projects/alibi-detect/en/stable/overview/saving.html). | [docs](https://docs.seldon.io/projects/alibi-detect/en/stable/) |
+| Alibi-Explain | [Save model using Alibi-Explain](https://docs.seldon.io/projects/alibi/en/stable/overview/saving.html). | [docs](https://docs.seldon.io/projects/alibi/en/stable/) |
+| DALI | Follow the Triton docs to create a config.pbtxt and model folder with artifact. | [docs](https://github.com/triton-inference-server/dali_backend) |
+| Huggingface | Create an MLServer `model-settings.json` with the Huggingface model required | [docs](https://github.com/SeldonIO/MLServer/blob/master/docs/examples/huggingface/README.md) |
+| LightGBM | Save model to file with extension`.bst`. | [docs](https://github.com/SeldonIO/MLServer/blob/master/docs/examples/lightgbm/README.md) |
+| MLFlow | Use the created `artifacts/model` folder from your training run. | [docs](https://github.com/SeldonIO/MLServer/tree/master/runtimes/mlflow) |
+| ONNX | Save you model with name `model.onnx`. | [docs](https://github.com/triton-inference-server/onnxruntime_backend) |
+| OpenVino | Follow the Triton docs to create your model artifacts. | [docs](https://github.com/triton-inference-server/openvino_backend) |
+| Custom MLServer Python | Create a python file with a class that extends `MLModel`. | [docs](https://github.com/SeldonIO/MLServer/blob/master/docs/examples/custom/README.md) |
+| Custom Triton Python | Follow the Triton docs to create your `config.pbtxt` and associated python files. | [docs](https://github.com/triton-inference-server/python_backend) |
+| PyTorch | Create a Triton `config.pbtxt` describing inputs and outputs and place traced torchscript in folder as `model.pt`. | [docs](https://github.com/triton-inference-server/pytorch_backend) |
+| SKLearn | Save model via joblib to a file with extension `.joblib` or with pickle to a file with extension `.pkl` or `.pickle`. | [docs](https://github.com/SeldonIO/MLServer/tree/master/runtimes/sklearn) |
+| Spark Mlib | Follow the MLServer docs. | [docs](https://github.com/SeldonIO/MLServer/tree/master/runtimes/mllib) |
+| Tensorflow | Save model in "Saved Model" format as `model.savedodel`. If using graphdef format you will need to create Triton config.pbtxt and place your model in a numbered sub folder. HDF5 is not supported. | [docs](https://github.com/triton-inference-server/tensorflow_backend) |
+| TensorRT | Follow the Triton docs to create your model artifacts. | [docs](https://github.com/triton-inference-server/tensorrt_backend) |
+| Triton FIL | Follow the Triton docs to create your model artifacts. | [docs](https://github.com/triton-inference-server/fil_backend) |
+| XGBoost | Save model to file with extension`.bst` or `.json`. | [docs](https://github.com/SeldonIO/MLServer/blob/master/docs/examples/xgboost/README.md) |
+
+
+## Custom MLServer Model Settings
+
+For [MLServer](https://github.com/SeldonIO/MLServer) targeted models you can create a `model-settings.json`
+file to help MLServer load your model and place this alongside your artifact. See the
+[MLServer project](https://mlserver.readthedocs.io/en/latest/reference/model-settings.html) for details.
+
+## Custom Triton Configuration
+
+For [Triton inference server](https://github.com/triton-inference-server/server) models you can create
+[a configuration config.pbtxt file](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md)
+alongside your artifact.
+
+## Notes
+
+The `tag` field represents the tag you need to add to the `requirements` part of the Model spec for
+your artifact to be loaded on a compatible server. e.g. for an sklearn model:
+
+```yaml
+# samples/models/sklearn-iris-gs.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
+```
diff --git a/docs/models/inference-artifacts/index.md b/docs/models/inference-artifacts/index.md
deleted file mode 100644
index 536d77fedb..0000000000
--- a/docs/models/inference-artifacts/index.md
+++ /dev/null
@@ -1,162 +0,0 @@
-# Inference Artifacts
-
-To run your model inside Seldon you must supply an inference artifact that can be downloaded and run on one of MLServer or Triton inference servers. We list artifacts below by alphabetical order below.
-
-
-```{list-table}
-:header-rows: 1
-
-* - Type
-  - Server
-  - Tag
-  - Example
-* - Alibi-Detect
-  - MLServer
-  - `alibi-detect`
-  - [example](../../examples/cifar10.md)
-* - Alibi-Explain
-  - MLServer
-  - `alibi-explain`
-  - [example](../../examples/explainer-examples.md)
-* - DALI
-  - Triton
-  - `dali`
-  - TBC
-* - Huggingface
-  - MLServer
-  - `huggingface`
-  - [example](../../examples/huggingface.md)
-* - LightGBM
-  - MLServer
-  - `lightgbm`
-  - [example](../../examples/model-zoo.md#lightgbm-model)
-* - MLFlow
-  - MLServer
-  - `mlflow`
-  - [example](../../examples/model-zoo.md#mlflow-wine-model)
-* - ONNX
-  - Triton
-  - `onnx`
-  - [example](../../examples/model-zoo.md#onnx-mnist-model)
-* - OpenVino
-  - Triton
-  - `openvino`
-  - TBC
-* - Custom Python
-  - MLServer
-  - `python, mlserver`
-  - [example](../../examples/pandasquery.md)
-* - Custom Python
-  - Triton
-  - `python, triton`
-  - [example](https://github.com/SeldonIO/triton-python-examples)
-* - PyTorch
-  - Triton
-  - `pytorch`
-  - [example](../../examples/model-zoo.md#pytorch-mnist-model)
-* - SKLearn
-  - MLServer
-  - `sklearn`
-  - [example](../../examples/income.md)
-* - Spark Mlib
-  - MLServer
-  - `spark-mlib`
-  - TBC
-* - Tensorflow
-  - Triton
-  - `tensorflow`
-  - [example](../../examples/cifar10.md)
-* - TensorRT
-  - Triton
-  - `tensorrt`
-  - TBC
-* - Triton FIL
-  - Triton
-  - `fil`
-  - TBC
-* - XGBoost
-  - MLServer
-  - `xgboost`
-  - [example](../../examples/model-zoo.md#xgboost-model)
-```
-
-## Saving Model artifacts
-
-For many machine learning artifacts you can simply save them to a folder and load them into seldon core v2. Details are given below as well as a link to creating a custom model settings file if needed.
-
-```{list-table}
-:header-rows: 1
-
-* - Type
-  - Notes
-  - Custom Model Settings
-
-* - Alibi-Detect
-  - [Save model using Alibi-Detect](https://docs.seldon.io/projects/alibi-detect/en/stable/overview/saving.html).
-  - [docs](https://docs.seldon.io/projects/alibi-detect/en/stable/)
-* - Alibi-Explain
-  - [Save model using Alibi-Explain](https://docs.seldon.io/projects/alibi/en/stable/overview/saving.html).
-  - [docs](https://docs.seldon.io/projects/alibi/en/stable/)
-* - DALI
-  - Follow the Triton docs to create a config.pbtxt and model folder with artifact.
-  - [docs](https://github.com/triton-inference-server/dali_backend)
-* - Huggingface
-  - Create an MLServer `model-settings.json` with the Huggingface model required
-  - [docs](https://github.com/SeldonIO/MLServer/blob/master/docs/examples/huggingface/README.md)
-* - LightGBM
-  - Save model to file with extension`.bst`.
-  - [docs](https://github.com/SeldonIO/MLServer/blob/master/docs/examples/lightgbm/README.md)
-* - MLFlow
-  - Use the created `artifacts/model` folder from your training run.
-  - [docs](https://github.com/SeldonIO/MLServer/tree/master/runtimes/mlflow)
-* - ONNX
-  - Save you model with name `model.onnx`.
-  - [docs](https://github.com/triton-inference-server/onnxruntime_backend)
-* - OpenVino
-  - Follow the Triton docs to create your model artifacts.
-  - [docs](https://github.com/triton-inference-server/openvino_backend)
-* - Custom MLServer Python
-  - Create a python file with a class that extends `MLModel`.
-  - [docs](https://github.com/SeldonIO/MLServer/blob/master/docs/examples/custom/README.md)
-* - Custom Triton Python
-  - Follow the Triton docs to create your `config.pbtxt` and associated python files.
-  - [docs](https://github.com/triton-inference-server/python_backend)
-* - PyTorch
-  - Create a Triton `config.pbtxt` describing inputs and outputs and place traced torchscript in folder as `model.pt`.
-  - [docs](https://github.com/triton-inference-server/pytorch_backend)
-* - SKLearn
-  - Save model via joblib to a file with extension `.joblib` or with pickle to a file with extension `.pkl` or `.pickle`.
-  - [docs](https://github.com/SeldonIO/MLServer/tree/master/runtimes/sklearn)
-* - Spark Mlib
-  - Follow the MLServer docs.
-  - [docs](https://github.com/SeldonIO/MLServer/tree/master/runtimes/mllib)
-* - Tensorflow
-  - Save model in "Saved Model" format as `model.savedodel`. If using graphdef format you will need to create Triton config.pbtxt and place your model in a numbered sub folder. HDF5 is not supported.
-  - [docs](https://github.com/triton-inference-server/tensorflow_backend)
-* - TensorRT
-  - Follow the Triton docs to create your model artifacts.
-  - [docs](https://github.com/triton-inference-server/tensorrt_backend)
-* - Triton FIL
-  - Follow the Triton docs to create your model artifacts.
-  - [docs](https://github.com/triton-inference-server/fil_backend)
-* - XGBoost
-  - Save model to file with extension`.bst` or `.json`.
-  - [docs](https://github.com/SeldonIO/MLServer/blob/master/docs/examples/xgboost/README.md)
-```
-
-## Custom MLServer Model Settings
-
-For [MLServer](https://github.com/SeldonIO/MLServer) targeted models you can create a `model-settings.json` file to help MLServer load your model and place this alongside your artifact. See the [MLServer project](https://mlserver.readthedocs.io/en/latest/reference/model-settings.html)  for details.
-
-## Custom Triton Configuration
-
-For [Triton inference server](https://github.com/triton-inference-server/server) models you can create [a configuration config.pbtxt file](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md) alongside your artifact.
-
-## Notes
-
- * The `tag` field represents the tag you need to add to the `requirements` part of the Model spec for your artifact to be loaded on a compatible server. e.g. for an sklearn model:
-   ```{literalinclude} ../../../../../samples/models/sklearn-iris-gs.yaml 
-   :language: yaml
-   ```
-
-
diff --git a/docs/models/mms.md b/docs/models/mms.md
new file mode 100644
index 0000000000..c168f12cbe
--- /dev/null
+++ b/docs/models/mms.md
@@ -0,0 +1,79 @@
+# Multi-model Serving with Overcommit
+
+## Multi-model Serving
+
+Multi-model serving is an architecture pattern where one ML inference server hosts multiple models
+at the same time. This means that, within a single instance of the server, you can serve multiple
+models under different paths. This is a feature provided out of the box by Nvidia Triton and Seldon
+MLServer, currently the two inference servers that are integrated in Seldon Core v2.
+
+This deployment pattern allows the system to handle a large number of deployed models letting them
+share hardware resources allocated to inference servers (e.g GPUs). For example if a single model
+inference server is deployed on a one GPU node, the underlying loaded models on this inference
+server instance are able to effectively share this GPU. This is contrast to a single model per
+server deployment pattern where only one model can use the allocated GPU.
+
+
+![Multi-model vs Single-model serving](../images/mms.png)
+
+Multi-model serving is enabled by design in Seldon Core v2. Based on requirements that are specified
+by the user on a given `Model`, the Scheduler will find an appropriate model inference server instance
+to load the model onto.
+
+In the below example, given that the model is `tensorflow`, the system will deploy the model onto a
+`triton` server instance (matching with the `Server` labels). Additionally as the model `memory`
+requirement is `100Ki`, the system will pick the server instance that has enough (memory) capacity to
+host this model in parallel to other potentially existing models.
+
+
+```yaml
+# samples/models/tfsimple1.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+```
+
+All models are loaded and active on this model server. Inference requests for these models are served
+concurrently and the hardware resources are shared to fulfil these inference requests.
+
+## Overcommit
+
+Overcommit allows shared servers to handle more models than can fit in memory. This is done by keeping
+highly utilized models in memory and evicting other ones to disk using a least-recently-used (LRU)
+cache mechanism. From a user perspective these models are all registered and "ready" to serve
+inference requests. If an inference request comes for a model that is unloaded/evicted to disk, the
+system will reload the model first before forwarding the request to the inference server.
+
+Overcommit is enabled by setting `SELDON_OVERCOMMIT_PERCENTAGE` on shared servers; it is set by
+default at 10%. In other words a given model inference server instance can register models with a
+total memory requirement up to `MEMORY_REQUEST` * ( 1 + `SELDON_OVERCOMMIT_PERCENTAGE` / 100).
+
+The Seldon Agent (a side car next to each model inference server deployment) is keeping track of
+inference requests times on the different models. These models are sorted in time ascending order and
+this data structure is used to evict the least recently used model in order to make room for another
+incoming model. This happens during two scenarios:
+- A new model load request beyond the active memory capacity of the inference server.
+- An incoming inference request to a registered model that is not loaded in-memory (previously evicted).
+
+This is done seamlessly to users and specifically for reloading a model onto the inference server
+to respond to an inference request, the model artifact is cached on disk which allows a faster reload
+(no remote artifact fetch). Therefore we expect that the extra latency to reload a model during an
+inference request is acceptable in many cases (with a lower bound of ~100ms).
+
+Overcommit can be disabled by setting `SELDON_OVERCOMMIT_PERCENTAGE` to 0 for a given shared server.
+
+![Overcommit](../images/overcommit.png)
+
+{% hint style="info" %}
+Currently we are using memory requirement values that are specified by the user on the
+Server and Model side. In the future we are looking at how to make the system automatically handle
+memory management.
+{% endhint %}
+
+Check this [notebook](../examples/local-overcommit-examples.md) for a local example.
diff --git a/docs/models/mms/mms.md b/docs/models/mms/mms.md
deleted file mode 100644
index dabe7fa77b..0000000000
--- a/docs/models/mms/mms.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Multi-model Serving with Overcommit
-
-## Multi-model Serving
-
-Multi-model serving is an architecture pattern where one ML inference server hosts multiple models at the same time. This means that, within a single instance of the server, you can serve multiple models under different paths. This is a feature provided out of the box by Nvidia Triton and Seldon MLServer, currently the two inference servers that are integrated in Seldon Core v2.
-
-This deployment pattern allows the system to handle a large number of deployed models letting them share hardware resources allocated to inference servers (e.g GPUs). For example if a single model inference server is deployed on a one GPU node, the underlying loaded models on this inference server instance are able to effectively share this GPU. This is contrast to a single model per server deployment pattern where only one model can use the allocated GPU. 
-
-
-![Multi-model vs Single-model serving](mms.png)
-
-Multi-model serving is enabled by design in Seldon Core v2. Based on requirements that are specified by the user on a given `Model`, the Scheduler will find an appropriate model inference server instance to load the model onto. 
-
-In the below example, given that the model is `tensorflow`, the system will deploy the model onto a `triton` server instance (matching with the `Server` labels). Additionally as the model `memory` requirement is `100Ki`, the system will pick the server instance that has enough (memory) capacity to host this model in parallel to other potentially existing models. 
-
-
-```{literalinclude} ../../../../../samples/models/tfsimple1.yaml 
-:language: yaml
-```
-
-All models are loaded and active on this model server. Inference requests for these models are served concurrently and the hardware resources are shared to fulfil these inference requests.
-
-## Overcommit
-
-Overcommit allows shared servers to handle more models than can fit in memory. This is done by keeping highly utilized models in memory and evicting other ones to disk using a least-recently-used (LRU) cache mechanism. From a user perspective these models are all registered and "ready" to serve inference requests. If an inference request comes for a model that is unloaded/evicted to disk, the system will reload the model first before forwarding the request to the inference server.
-
-Overcommit is enabled by setting `SELDON_OVERCOMMIT_PERCENTAGE` on shared servers; it is set by default at 10%. In other words a given model inference server instance can register models with a total memory requirement up to `MEMORY_REQUEST` * ( 1 + `SELDON_OVERCOMMIT_PERCENTAGE` / 100).
-
-The Seldon Agent (a side car next to each model inference server deployment) is keeping track of inference requests times on the different models. These models are sorted in time ascending order and this data structure is used to evict the least recently used model in order to make room for another incoming model. This happens during two scenarios:
-- A new model load request beyond the active memory capacity of the inference server.
-- An incoming inference request to a registered model that is not loaded in-memory (previously evicted).
-
-This is done seamlessly to users and specifically for reloading a model onto the inference server to respond to an inference request, the model artifact is cached on disk which allows a faster reload (no remote artifact fetch). Therefore we expect that the extra latency to reload a model during an inference request is acceptable in many cases (with a lower bound of ~100ms).
-
-Overcommit can be disabled by setting `SELDON_OVERCOMMIT_PERCENTAGE` to 0 for a given shared server.
-
-![Overcommit](overcommit.png)
-
-**Note**: currently we are using memory requirement values that are specified by the user on the Server and Model side. In the future we are looking at how to make the system automatically handle memory management.
-
-Check [notebook](../../examples/local-overcommit-examples.md) for a local example.
\ No newline at end of file
diff --git a/docs/models/mms/mms.png b/docs/models/mms/mms.png
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diff --git a/docs/models/parameterized-models/README.md b/docs/models/parameterized-models/README.md
new file mode 100644
index 0000000000..2200a2e46f
--- /dev/null
+++ b/docs/models/parameterized-models/README.md
@@ -0,0 +1,27 @@
+# Parameterized Models
+
+The Model specification allows parameters to be passed to the loaded model to allow customization. For example:
+
+```yaml
+# samples/models/choice1.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: choice-is-one
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/pandasquery"
+  requirements:
+  - mlserver
+  - python
+  parameters:
+  - name: query
+    value: "choice == 1"
+```
+
+This capability is only available for MLServer custom model runtimes. The named keys and
+values will be added to the model-settings.json file for the provided model in the
+`parameters.extra` Dict. MLServer models are able to read these values in their `load` method.
+
+## Example Parameterized Models
+
+* [Pandas Query](./pandasquery.md)
diff --git a/docs/models/parameterized-models/index.md b/docs/models/parameterized-models/index.md
deleted file mode 100644
index 1ae78bedb0..0000000000
--- a/docs/models/parameterized-models/index.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Parameterized Models
-
-The Model specification allows parameters to be passed to the loaded model to allow customization. For example:
-
-```{literalinclude} ../../../../../samples/models/choice1.yaml 
-:language: yaml
-```
-
-This capability is only available for MLServer custom model runtimes. The named keys and values will be added to the model-settings.json file for the provided model in the
-`parameters.extra` Dict. MLServer models are able to read these values in their `load` method.
-
-## Example Parameterized Models
-
- * [Pandas Query](./pandasquery.md)
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-pandasquery.md
-```
diff --git a/docs/models/parameterized-models/pandasquery.md b/docs/models/parameterized-models/pandasquery.md
index e7ff2e2656..e60af416c8 100644
--- a/docs/models/parameterized-models/pandasquery.md
+++ b/docs/models/parameterized-models/pandasquery.md
@@ -2,36 +2,84 @@
 
 This model allows a [Pandas](https://pandas.pydata.org/)  query to be run in the input to select rows. An example is shown below:
 
-```{literalinclude} ../../../../../samples/models/choice1.yaml 
-:language: yaml
+```yaml
+# samples/models/choice1.yaml
+
 ```
 
 This invocation check filters for tensor A having value 1.
 
-  * The model also returns a tensor called `status` which indicates the operation run and whether it was a success. If no rows satisfy the query then just a `status` tensor output will be returned.
-  * Further details on Pandas query can be found [here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html)
+* The model also returns a tensor called `status` which indicates the operation run and whether it
+was a success. If no rows satisfy the query then just a `status` tensor output will be returned.
+* Further details on Pandas query can be found [here](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html)
 
 
 This model can be useful for conditional Pipelines. For example, you could have two invocations of this model:
 
-```{literalinclude} ../../../../../samples/models/choice1.yaml 
-:language: yaml
+```yaml
+# samples/models/choice1.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: choice-is-one
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/pandasquery"
+  requirements:
+  - mlserver
+  - python
+  parameters:
+  - name: query
+    value: "choice == 1"
 ```
 
 and
 
-```{literalinclude} ../../../../../samples/models/choice2.yaml 
-:language: yaml
+```yaml
+# samples/models/choice2.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: choice-is-two
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/pandasquery"
+  requirements:
+  - mlserver
+  - python
+  parameters:
+  - name: query
+    value: "choice == 2"
 ```
 
 By including these in a Pipeline as follows we can define conditional routes:
 
-```{literalinclude} ../../../../../samples/pipelines/choice.yaml 
-:language: yaml
+```yaml
+# samples/pipelines/choice.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: choice
+spec:
+  steps:
+  - name: choice-is-one
+  - name: mul10
+    inputs:
+    - choice.inputs.INPUT
+    triggers:
+    - choice-is-one.outputs.choice
+  - name: choice-is-two
+  - name: add10
+    inputs:
+    - choice.inputs.INPUT
+    triggers:
+    - choice-is-two.outputs.choice
+  output:
+    steps:
+    - mul10
+    - add10
+    stepsJoin: any
 ```
 
-Here the mul10 model will be called if the choice-is-one model succeeds and the add10 model will be called if the choice-is-two model succeeds.
-
-The full notebook can be found [here](../../examples/pandasquery.md)
-
+Here the mul10 model will be called if the choice-is-one model succeeds and the add10 model will
+be called if the choice-is-two model succeeds.
 
+The full notebook can be found [here](../examples/pandasquery.md)
diff --git a/docs/models/rclone.md b/docs/models/rclone.md
new file mode 100644
index 0000000000..f1efd2751e
--- /dev/null
+++ b/docs/models/rclone.md
@@ -0,0 +1,9 @@
+# Rclone
+
+We utilize [Rclone](https://rclone.org/) to copy model artifacts from a storage location
+to the model servers. This allows users to take advantage of Rclones support for over 40
+cloud storage backends including Amazon S3, Google Storage and many others.
+
+For local storage while developing see [here](../getting-started/docker-installation.md#local-models).
+
+For authorization needed for cloud storage when running on Kubernetes see [here](../../kubernetes/storage-secrets.md).
diff --git a/docs/models/rclone/index.md b/docs/models/rclone/index.md
deleted file mode 100644
index 8b1cd7f2ac..0000000000
--- a/docs/models/rclone/index.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Rclone
-
-We utilize [Rclone](https://rclone.org/) to copy model artifacts from a storage location to the model servers. This allows users to take advantage of Rclones support for over 40 cloud storage backends including Amazon S3, Google Storage and many others.
-
-For local storage while developing see [here](../../getting-started/docker-installation/index.md#local-models).
-
-For authorization needed for cloud storage when running on Kubernetes see [here](../../kubernetes/storage-secrets/index).

From d818bf9dde15be9f6c8ea04fc7ad604e80f8d4f4 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 16:57:37 +1000
Subject: [PATCH 06/32] added most images in the docs to the images directory

---
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 docs/images/favicon.ico                 | Bin 0 -> 950 bytes
 docs/images/header-gradient-core-v2.png | Bin 0 -> 983305 bytes
 docs/images/header-gradient.png         | Bin 0 -> 943594 bytes
 docs/images/hodometer-architecture.png  | Bin 0 -> 39418 bytes
 docs/images/illustration-core-v2.png    | Bin 0 -> 460184 bytes
 docs/images/illustration.png            | Bin 0 -> 232666 bytes
 docs/images/img_pipeline1.jpg           | Bin 0 -> 77889 bytes
 docs/images/img_pipeline2.jpg           | Bin 0 -> 84755 bytes
 docs/images/img_pipeline3.jpg           | Bin 0 -> 83299 bytes
 docs/images/img_pipeline4.jpg           | Bin 0 -> 72798 bytes
 docs/images/img_pipeline5.jpg           | Bin 0 -> 84843 bytes
 docs/images/jaeger-trace.png            | Bin 0 -> 206646 bytes
 docs/images/jaeger-tracing.png          | Bin 0 -> 206646 bytes
 docs/images/logo.svg                    |  40 ++++++++++++++++++++++++
 docs/images/mms.png                     | Bin 0 -> 40700 bytes
 docs/images/networking.jpg              | Bin 0 -> 103917 bytes
 docs/images/overcommit.png              | Bin 0 -> 73855 bytes
 docs/images/speech-to-sentiment.jpg     | Bin 0 -> 141524 bytes
 docs/images/workflow.png                | Bin 0 -> 21172 bytes
 20 files changed, 40 insertions(+)
 create mode 100644 docs/images/dashboard.png
 create mode 100644 docs/images/favicon.ico
 create mode 100644 docs/images/header-gradient-core-v2.png
 create mode 100644 docs/images/header-gradient.png
 create mode 100644 docs/images/hodometer-architecture.png
 create mode 100644 docs/images/illustration-core-v2.png
 create mode 100644 docs/images/illustration.png
 create mode 100644 docs/images/img_pipeline1.jpg
 create mode 100644 docs/images/img_pipeline2.jpg
 create mode 100644 docs/images/img_pipeline3.jpg
 create mode 100644 docs/images/img_pipeline4.jpg
 create mode 100644 docs/images/img_pipeline5.jpg
 create mode 100644 docs/images/jaeger-trace.png
 create mode 100644 docs/images/jaeger-tracing.png
 create mode 100644 docs/images/logo.svg
 create mode 100644 docs/images/mms.png
 create mode 100644 docs/images/networking.jpg
 create mode 100644 docs/images/overcommit.png
 create mode 100644 docs/images/speech-to-sentiment.jpg
 create mode 100644 docs/images/workflow.png

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From 7bfe2e66cc877333fd1b72076af84b13a22c1894 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 16:59:27 +1000
Subject: [PATCH 07/32] moved outliers and drift docs to its own file in the
 root directory

---
 docs/drift.md         | 51 +++++++++++++++++++++++++++++++++++++++++++
 docs/drift/index.md   | 22 -------------------
 docs/outlier.md       | 25 +++++++++++++++++++++
 docs/outlier/index.md | 14 ------------
 4 files changed, 76 insertions(+), 36 deletions(-)
 create mode 100644 docs/drift.md
 delete mode 100644 docs/drift/index.md
 create mode 100644 docs/outlier.md
 delete mode 100644 docs/outlier/index.md

diff --git a/docs/drift.md b/docs/drift.md
new file mode 100644
index 0000000000..a18348fd6f
--- /dev/null
+++ b/docs/drift.md
@@ -0,0 +1,51 @@
+# Drift Detection
+
+Drift detection models are treated as any other Model. You can run any saved
+[Alibi-Detect](https://github.com/SeldonIO/alibi-detect) drift detection model by
+adding the requirement `alibi-detect`.
+
+An example drift detection model from the CIFAR10 image classification example is shown below:
+
+```yaml
+# samples/models/cifar10-drift-detect.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: cifar10-drift
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/cifar10/drift-detector"
+  requirements:
+    - mlserver
+    - alibi-detect
+```
+
+Usually you would run these models in an asynchronous part of a Pipeline, i.e. they are not
+connected to the output of the Pipeline which defines the synchronous path. For example, the
+CIFAR-10 image detection example uses a pipeline as shown below:
+
+```yaml
+# samples/pipelines/cifar10.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: cifar10-production
+spec:
+  steps:
+    - name: cifar10
+    - name: cifar10-outlier
+    - name: cifar10-drift
+      batch:
+        size: 20
+  output:
+    steps:
+    - cifar10
+    - cifar10-outlier.outputs.is_outlier
+```
+
+Note how the `cifar10-drift` model is not part of the path to the outputs. Drift alerts can be
+read from the Kafka topic of the model.
+
+## Examples
+
+* [CIFAR10 image classification with drift detector](examples/cifar10.md)
+* [Tabular income classification model with drift detector](examples/income.md)
diff --git a/docs/drift/index.md b/docs/drift/index.md
deleted file mode 100644
index a8abd741b7..0000000000
--- a/docs/drift/index.md
+++ /dev/null
@@ -1,22 +0,0 @@
-# Drift Detection
-
-Drift detection models are treated as any other Model. You can run any saved [Alibi-Detect](https://github.com/SeldonIO/alibi-detect) drift detection model by adding the requirement `alibi-detect`.
-
-An example drift detection model from the CIFAR10 image classification example is shown below:
-
-```{literalinclude} ../../../../samples/models/cifar10-drift-detect.yaml
-:language: yaml
-```
-
-Usually you would run these models in an asynchronous part of a Pipeline, i.e. they are not connected to the output of the Pipeline which defines the synchronous path. For example, the CIFAR-10 image detection example uses a pipeline as shown below:
-
-```{literalinclude} ../../../../samples/pipelines/cifar10.yaml
-:language: yaml
-```
-
-Note how the `cifar10-drift` model is not part of the path to the outputs. Drift alerts can be read from the Kafka topic of the model.
-
-## Examples
-
- * [CIFAR10 image classification with drift detector](../examples/cifar10.md)
- * [Tabular income classification model with drift detector](../examples/income.md)
diff --git a/docs/outlier.md b/docs/outlier.md
new file mode 100644
index 0000000000..1460ec887f
--- /dev/null
+++ b/docs/outlier.md
@@ -0,0 +1,25 @@
+# Outlier Detection
+
+Outlier detection models are treated as any other Model. You can run any saved
+[Alibi-Detect](https://github.com/SeldonIO/alibi-detect) outlier detection model
+by adding the requirement `alibi-detect`.
+
+An example outlier detection model from the CIFAR10 image classification example is shown below:
+
+```yaml
+# samples/models/cifar10-outlier-detect.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: cifar10-outlier
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/cifar10/outlier-detector"
+  requirements:
+    - mlserver
+    - alibi-detect
+```
+
+## Examples
+
+* [CIFAR10 image classification with outlier detector](../examples/cifar10.md)
+* [Tabular income classification model with outlier detector](../examples/income.md)
diff --git a/docs/outlier/index.md b/docs/outlier/index.md
deleted file mode 100644
index 67283bceaf..0000000000
--- a/docs/outlier/index.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# Outlier Detection
-
-Outlier detection models are treated as any other Model. You can run any saved [Alibi-Detect](https://github.com/SeldonIO/alibi-detect) outlier detection model by adding the requirement `alibi-detect`.
-
-An example outlier detection model from the CIFAR10 image classification example is shown below:
-
-```{literalinclude} ../../../../samples/models/cifar10-outlier-detect.yaml
-:language: yaml
-```
-
-## Examples
-
- * [CIFAR10 image classification with outlier detector](../examples/cifar10.md)
- * [Tabular income classification model with outlier detector](../examples/income.md)

From a837d339c342aba9a4fb4b9b2ac26a3a4d5b0cd3 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:00:23 +1000
Subject: [PATCH 08/32] deleted servers directory and moved servers.md to the
 root directory with enhancements

---
 docs/{servers/index.md => servers.md} | 79 ++++++++++++++++++++-------
 1 file changed, 58 insertions(+), 21 deletions(-)
 rename docs/{servers/index.md => servers.md} (54%)

diff --git a/docs/servers/index.md b/docs/servers.md
similarity index 54%
rename from docs/servers/index.md
rename to docs/servers.md
index 8e86ba4a02..38bee68fb4 100644
--- a/docs/servers/index.md
+++ b/docs/servers.md
@@ -2,8 +2,16 @@
 
 By default Seldon installs two server farms using MLServer and Triton with 1 replica each. Models are scheduled onto servers based on the server's resources and whether the capabilities of the server matches the requirements specified in the Model request. For example:
 
-```{literalinclude} ../../../../samples/models/sklearn-iris-gs.yaml
-:language: yaml
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
 ```
 
 This model specifies the requirement `sklearn`
@@ -11,44 +19,75 @@ This model specifies the requirement `sklearn`
 There is a default capabilities for each server as follows:
 
 * MLServer
-  ```{literalinclude} ../../../../operator/config/serverconfigs/mlserver.yaml
-  :language: yaml
-  :start-after: SELDON_SERVER_CAPABILITIES
-  :end-before: SELDON_OVERCOMMIT_PERCENTAGE
+  ```yaml
+  - name: SELDON_SERVER_CAPABILITIES
+    value: "mlserver,alibi-detect,alibi-explain,huggingface,lightgbm,mlflow,python,sklearn,spark-mlib,xgboost"
+  ```
 * Triton
-  ```{literalinclude} ../../../../operator/config/serverconfigs/triton.yaml
-  :language: yaml
-  :start-after: SELDON_SERVER_CAPABILITIES
-  :end-before: SELDON_OVERCOMMIT_PERCENTAGE
+  ```yaml
+  - name: SELDON_SERVER_CAPABILITIES
+    value: "triton,dali,fil,onnx,openvino,python,pytorch,tensorflow,tensorrt"
   ```
 
 ## Custom Capabilities
 Servers can be defined with a `capabilities` field to indicate custom configurations (e.g. Python dependencies). For instance:
 
-```{literalinclude} ../../../../samples/servers/custom-mlserver-capabilities.yaml
-:language: yaml
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Server
+metadata:
+  name: mlserver-134
+spec:
+  serverConfig: mlserver
+  capabilities:
+  - mlserver-1.3.4
+  podSpec:
+    containers:
+    - image: seldonio/mlserver:1.3.4
+      name: mlserver
 ```
 
 These `capabilities` override the ones from the `serverConfig: mlserver`. A model that takes advantage of this is shown below:
 
-```{literalinclude} ../../../../samples/models/iris-custom-requirements.yaml
-:language: yaml
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - mlserver-1.3.4
 ```
 
 This above model will be matched with the previous custom server `mlserver-134`.
 
 Servers can also be set up with the `extraCapabilities` that add to existing capabilities from the referenced ServerConfig. For instance:
 
-```{literalinclude} ../../../../samples/servers/mlserver-extra-capabilities.yaml
-:language: yaml
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Server
+metadata:
+  name: mlserver-extra
+spec:
+  serverConfig: mlserver
+  extraCapabilities:
+  - extra
 ```
 This server, `mlserver-extra`, inherits a default set of capabilities via `serverConfig: mlserver`.
 These defaults are discussed above.
 The `extraCapabilities` are appended to these to create a single list of capabilities for this server.
 
 Models can then specify requirements to select a server that satisfies those requirements as follows.
-```{literalinclude} ../../../../samples/models/extra-model-requirements.yaml
-:language: yaml
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: extra-model-requirements
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - extra
 ```
 
 The `capabilities` field takes precedence over the `extraCapabilities` field.
@@ -58,6 +97,4 @@ For some examples see [here](../examples/custom-servers.md).
 
 ## Autoscaling of Servers
 
-Within docker we don't support this but for Kubernetes see [here](../kubernetes/autoscaling/index.md)
-
-
+Within docker we don't support this but for Kubernetes see [here](../kubernetes/autoscaling/README.md)

From f9c6a958c34af69d16542e83a40ba1d682e56b03 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:01:03 +1000
Subject: [PATCH 09/32] deleted pipelines dir and moved pipelines.md to the
 root directory

---
 docs/pipelines.md                 | 528 ++++++++++++++++++++++++++++++
 docs/pipelines/index.md           | 378 ---------------------
 docs/pipelines/jaeger-tracing.png | Bin 206646 -> 0 bytes
 3 files changed, 528 insertions(+), 378 deletions(-)
 create mode 100644 docs/pipelines.md
 delete mode 100644 docs/pipelines/index.md
 delete mode 100644 docs/pipelines/jaeger-tracing.png

diff --git a/docs/pipelines.md b/docs/pipelines.md
new file mode 100644
index 0000000000..29ff24b8f5
--- /dev/null
+++ b/docs/pipelines.md
@@ -0,0 +1,528 @@
+# Pipelines
+
+Pipelines allow models to be connected into flows of data transformations. This allows more
+complex machine learning pipelines to be created with multiple models, feature transformations
+and monitoring components such as drift and outlier detectors.
+
+## Creating Pipelines
+
+The simplest way to create Pipelines is by defining them with the
+[Pipeline resource we provide for Kubernetes](./kubernetes/resources/pipeline.md). This format
+is accepted by our Kubernetes implementation but also locally via our `seldon` CLI.
+
+Internally in both cases Pipelines are created via our [Scheduler API](./apis/scheduler.md). Advanced
+users could submit Pipelines directly using this gRPC service.
+
+An example that chains two models together is shown below:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: chain
+  namespace: seldon-mesh
+spec:
+  steps:
+    - name: model1
+    - name: model2
+      inputs:
+      - model1
+  output:
+    steps:
+    - model2
+```
+
+* `steps` allow you to specify the models you want to combine into a pipeline. Each step name will
+correspond to a model of the same name. These models will need to have been deployed and available
+for the Pipeline to function, however Pipelines can be deployed before or at the same time you deploy
+the underlying models.
+* `steps.inputs` allow you to specify the inputs to this step.
+* `outputs.steps` allow you to specify the output of the Pipeline. A pipeline can have multiple paths
+include flows of data that do not reach the output, e.g. Drift detection steps. However, if you wish
+to call your Pipeline in a synchronous manner via REST/gRPC then an output must be present so the
+Pipeline can be treated as a function.
+
+## Expressing input data sources
+
+
+Model step inputs are defined with a dot notation of the form:
+
+```sh
+<stepName>|<pipelineName>.<inputs|outputs>.<tensorName>
+```
+
+Inputs with just a step name will be assumed to be `step.outputs`.
+
+The default payloads for Pipelines is the V2 protocol which requires named tensors as inputs and outputs
+from a model. If you require just certain tensors from a model you can reference those in the inputs,
+e.g. `mymodel.outputs.t1` will reference the tensor `t1` from the model `mymodel`.
+
+For the specification of the [V2 protocol](apis/inference/README.md).
+
+## Chain
+
+The simplest Pipeline chains models together: the output of one model goes into the input of the next.
+This will work out of the box if the output tensor names from a model match the input tensor names for
+the one being chained to. If they do not then the `tensorMap` construct presently needs to be used to
+define the mapping explicitly, e.g. see below for a simple chained pipeline of two tfsimple example models:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple
+spec:
+  steps:
+    - name: tfsimple1
+  output:
+    steps:
+    - tfsimple1
+```
+
+```mermaid
+flowchart LR
+    classDef pipeIO fill:#F6E083
+
+    subgraph input
+        INPUT0:::pipeIO
+        INPUT1:::pipeIO
+    end
+
+    INPUT0 --> TF1(tfsimple1)
+    INPUT1 --> TF1
+    TF1 --->|OUTPUT0: INPUT0| TF2(tfsimple2)
+    TF1 --->|OUTPUT1: INPUT1| TF2
+
+    subgraph output
+        OUTPUT0:::pipeIO
+        OUTPUT1:::pipeIO
+    end
+
+    TF2 --> OUTPUT0
+    TF2 --> OUTPUT1
+```
+
+In the above we rename tensor `OUTPUT0` to `INPUT0` and `OUTPUT1` to `INPUT1`. This allows these models to
+be chained together. The shape and data-type of the tensors needs to match as well.
+
+This example can be found in the [pipeline-examples examples](examples/pipeline-examples.md#model-chaining).
+
+## Join
+
+Joining allows us to combine outputs from multiple steps as input to a new step.
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: join
+spec:
+  steps:
+    - name: tfsimple1
+    - name: tfsimple2
+    - name: tfsimple3
+      inputs:
+      - tfsimple1.outputs.OUTPUT0
+      - tfsimple2.outputs.OUTPUT1
+      tensorMap:
+        tfsimple1.outputs.OUTPUT0: INPUT0
+        tfsimple2.outputs.OUTPUT1: INPUT1
+  output:
+    steps:
+    - tfsimple3
+```
+
+```mermaid
+flowchart LR
+    classDef pipeIO fill:#F6E083
+    classDef hidden fill:#ffffff,stroke:#ffffff
+
+    subgraph input
+        INPUT0:::pipeIO
+        INPUT1:::pipeIO
+    end
+
+    INPUT0 --> TF1(tfsimple1)
+    INPUT1 --> TF1
+    INPUT0 --> TF2(tfsimple2)
+    INPUT1 --> TF2
+    TF1 -.-> |OUTPUT1| tf1( ):::hidden
+    TF1 --> |OUTPUT0: INPUT0| TF3(tfsimple3)
+    TF2 -.-> |OUTPUT0| tf2( ):::hidden
+    TF2 --> |OUTPUT1: INPUT1| TF3
+
+    subgraph output
+        OUTPUT0:::pipeIO
+        OUTPUT1:::pipeIO
+    end
+    TF3 --> OUTPUT0
+    TF3 --> OUTPUT1
+```
+Caption: "*Joining the outputs of two models into a third model. The dashed lines signify model outputs that are not captured in the output of the pipeline.*"
+
+Here we pass the pipeline inputs to two models and then take one output tensor from each and pass to the
+final model. We use the same `tensorMap` technique to rename tensors as disucssed in the previous section.
+
+Joins can have a join type which can be specified with `inputsJoinType` and can take the values:
+* `inner` : require all inputs to be available to join.
+* `outer` : wait for `joinWindowMs` to join any inputs. Ignoring any inputs that have not sent any data at that
+point. This will mean this step of the pipeline is guaranteed to have a latency of at least `joinWindowMs`.
+* `any` : Wait for any of the specified data sources.
+
+This example can be found in the [pipeline-examples examples](examples/pipeline-examples.md#model-join).
+
+## Conditional Logic
+
+Pipelines can create conditional flows via various methods. We will discuss each in turn.
+
+### Model routing via tensors
+
+The simplest way is to create a model that outputs different named tensors based on its decision. This way downstream
+steps can be dependant on different expected tensors. An example is shown below:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-conditional
+spec:
+  steps:
+  - name: conditional
+  - name: mul10
+    inputs:
+    - conditional.outputs.OUTPUT0
+    tensorMap:
+      conditional.outputs.OUTPUT0: INPUT
+  - name: add10
+    inputs:
+    - conditional.outputs.OUTPUT1
+    tensorMap:
+      conditional.outputs.OUTPUT1: INPUT
+  output:
+    steps:
+    - mul10
+    - add10
+    stepsJoin: any
+```
+
+```mermaid
+flowchart LR
+    classDef pipeIO fill:#F6E083
+    classDef join fill:#CEE741;
+
+    subgraph input
+    INPUT0:::pipeIO
+        CHOICE:::pipeIO
+        INPUT0:::pipeIO
+        INPUT1:::pipeIO
+    end
+
+    CHOICE --> conditional
+    INPUT0 --> conditional
+    INPUT1 --> conditional
+
+    conditional --> |OUTPUT0: INPUT| add10
+    conditional --> |OUTPUT1: INPUT| mul10
+
+    add10 --> |OUTPUT| any(any):::join
+    mul10 --> |OUTPUT| any
+
+    subgraph output
+        OUTPUT(OUTPUT):::pipeIO
+    end
+
+    any --> OUTPUT
+
+    linkStyle 3 stroke:blue,color:blue;
+    linkStyle 5 stroke:blue,color:blue;
+    linkStyle 4 stroke:red,color:red;
+    linkStyle 6 stroke:red,color:red;
+```
+Caption: "*Pipeline with a conditional output model. The model **conditional** only outputs one of the two tensors, so only one path through the graph (red or blue) is taken by a single request*"
+
+In the above we have a step `conditional` that either outputs a tensor named `OUTPUT0` or a tensor named `OUTPUT1`.
+The `mul10` step depends on an output in `OUTPUT0` while the add10 step depends on an output from `OUTPUT1`.
+
+Note, we also have a final Pipeline output step that does an `any` join on these two models essentially outputting
+fron the pipeline whichever data arrives from either model. This type of Pipeline can be used for Multi-Armed bandit
+solutions where you want to route traffic dynamically.
+
+This example can be found in the [pipeline-examples examples](examples/pipeline-examples.md#conditional).
+
+### Errors
+
+Its also possible to abort pipelines when an error is produced to in effect create a condition. This is illustrated below:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: error
+spec:
+  steps:
+    - name: outlier-error
+  output:
+    steps:
+    - outlier-error
+```
+
+This Pipeline runs normally or throws an error based on whether the input tensors have certain values.
+
+This example can be found in the [pipeline-examples examples](examples/pipeline-examples.md#error).
+
+### Triggers
+
+Sometimes you want to run a step if an output is received from a previous step but not to send the data from
+that step to the model. This is illustrated below:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: joincheck
+spec:
+  steps:
+    - name: tfsimple1
+    - name: tfsimple2
+    - name: check
+      inputs:
+      - tfsimple1.outputs.OUTPUT0
+      tensorMap:
+        tfsimple1.outputs.OUTPUT0: INPUT
+    - name: tfsimple3
+      inputs:
+      - tfsimple1.outputs.OUTPUT0
+      - tfsimple2.outputs.OUTPUT1
+      tensorMap:
+        tfsimple1.outputs.OUTPUT0: INPUT0
+        tfsimple2.outputs.OUTPUT1: INPUT1
+      triggers:
+      - check.outputs.OUTPUT
+  output:
+    steps:
+    - tfsimple3
+```
+
+```mermaid
+flowchart LR
+  classDef pipeIO fill:#F6E083
+  classDef hidden fill:#ffffff,stroke:#ffffff
+
+  subgraph input
+      INPUT0:::pipeIO
+      INPUT1:::pipeIO
+  end
+
+  INPUT0 --> TF1(tfsimple1)
+  INPUT1 --> TF1
+  INPUT0 --> TF2(tfsimple2)
+  INPUT1 --> TF2
+  TF1 -.-> |OUTPUT1| tf1( ):::hidden
+
+  TF1 --> |OUTPUT0: INPUT| check
+  TF1 --> |OUTPUT0: INPUT0| TF3(tfsimple3)
+  TF2 --> |OUTPUT1: INPUT1| TF3
+  TF2 -.-> |OUTPUT0| tf2( ):::hidden
+
+  check --o |OUTPUT| TF3
+  linkStyle 9 stroke:#CEE741,color:black;
+
+  subgraph output
+    OUTPUT0:::pipeIO
+    OUTPUT1:::pipeIO
+  end
+
+  TF3 --> OUTPUT0
+  TF3 --> OUTPUT1
+```
+Caption: "*A pipeline with a single trigger. The model **tfsimple3** only runs if the model **check** returns a tensor named `OUTPUT`. The green edge signifies that this is a trigger and not an additional input to **tfsimple3**. The dashed lines signify model outputs that are not captured in the output of the pipeline.*"
+
+In this example the last step `tfsimple3` runs only if there are outputs from `tfsimple1` and `tfsimple2` but also
+data from the `check` step. However, if the step `tfsimple3` is run it only receives the join of data from `tfsimple1` and `tfsimple2`.
+
+This example can be found in the [pipeline-examples examples](examples/pipeline-examples.md#model-join-with-trigger).
+
+### Trigger Joins
+
+You can also define multiple triggers which need to happen based on a particulr join type. For example:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: trigger-joins
+spec:
+  steps:
+  - name: mul10
+    inputs:
+    - trigger-joins.inputs.INPUT
+    triggers:
+    - trigger-joins.inputs.ok1
+    - trigger-joins.inputs.ok2
+    triggersJoinType: any
+  - name: add10
+    inputs:
+    - trigger-joins.inputs.INPUT
+    triggers:
+    - trigger-joins.inputs.ok3
+  output:
+    steps:
+    - mul10
+    - add10
+    stepsJoin: any
+```
+
+```mermaid
+flowchart LR
+    classDef pipeIO fill:#F6E083
+    classDef pipeIOopt fill:#F6E083,stroke-dasharray: 5 5;
+    classDef trigger fill:#CEE741;
+    classDef hidden fill:#ffffff,stroke:#ffffff
+    classDef join fill:#CEE741;
+
+    subgraph input
+        ok1:::pipeIOopt
+        ok2:::pipeIOopt
+        INPUT:::pipeIO
+        ok3:::pipeIOopt
+    end
+
+    ok1 --o any
+    ok2 --o any
+    any((any)):::trigger --o mul10
+    linkStyle 0 stroke:#CEE741,color:green;
+    linkStyle 1 stroke:#CEE741,color:green;
+    linkStyle 2 stroke:#CEE741,color:green;
+
+    INPUT --> mul10
+    INPUT --> add10
+
+    ok3 --o add10
+    linkStyle 5 stroke:#CEE741,color:green;
+
+    subgraph output
+      OUTPUT:::pipeIO
+    end
+
+    mul10 -->|OUTPUT| anyOut(any):::join
+    add10 --> |OUTPUT| anyOut
+    anyOut --> OUTPUT
+```
+Caption: "*A pipeline with multiple triggers and a trigger join of type `any`. The pipeline has four inputs, but three of these are optional (signified by the dashed borders).*"
+
+Here the `mul10` step is run if data is seen on the pipeline inputs in the `ok1` or `ok2` tensors based on
+the `any` join type. If data is seen on `ok3` then the `add10` step is run.
+
+If we changed the `triggersJoinType` for `mul10` to `inner` then both `ok1` and `ok2` would need to appear
+before `mul10` is run.
+
+### Pipeline Inputs
+
+Pipelines by default can be accessed synchronously via http/grpc or asynchronously via the Kafka topic
+created for them. However, it's also possible to create a pipeline to take input from one or more other
+pipelines by specifying an `input` section. If for example we already have the `tfsimple` pipeline shown below:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple
+spec:
+  steps:
+    - name: tfsimple1
+  output:
+    steps:
+    - tfsimple1
+```
+
+We can create another pipeline which takes its input from this pipeline, as shown below:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-extended
+spec:
+  input:
+    externalInputs:
+      - tfsimple.outputs
+    tensorMap:
+      tfsimple.outputs.OUTPUT0: INPUT0
+      tfsimple.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+```
+
+```mermaid
+flowchart LR
+    classDef pipeIO fill:#F6E083
+
+    subgraph tfsimple.inputs
+        INPUT0:::pipeIO
+        INPUT1:::pipeIO
+    end
+
+    INPUT0 --> TF1(tfsimple1)
+    INPUT1 --> TF1
+
+    subgraph tfsimple.outputs
+        OUTPUT0:::pipeIO
+        OUTPUT1:::pipeIO
+    end
+
+    TF1 --> OUTPUT0
+    TF1 --> OUTPUT1
+
+    subgraph tfsimple-extended.inputs
+      INPUT10(INPUT0):::pipeIO
+      INPUT11(INPUT1):::pipeIO
+    end
+
+  OUTPUT0 --> INPUT10
+  OUTPUT1 --> INPUT11
+
+  INPUT10 --> TF2(tfsimple2)
+  INPUT11 --> TF2
+
+    subgraph tfsimple-extended.outputs
+        OUTPUT10(OUTPUT0):::pipeIO
+        OUTPUT11(OUTPUT1):::pipeIO
+    end
+
+    TF2 --> OUTPUT10
+    TF2 --> OUTPUT11
+```
+Caption: "*A pipeline taking as input the output of another pipeline.*"
+
+In this way pipelines can be built to extend existing running pipelines to allow extensibility and sharing of data flows.
+
+The spec follows the same spec for a step except that references to other pipelines are contained in
+the `externalInputs` section which takes the form of pipeline or pipeline.step references:
+* `<pipelineName>.(inputs|outputs).<tensorName>`
+* `<pipelineName>.(step).<stepName>.<tensorName>`
+
+Tensor names are optional and only needed if you want to take just one tensor from an input or output.
+
+There is also an `externalTriggers` section which allows triggers from other pipelines.
+
+Further examples can be found in the [pipeline-to-pipeline examples](examples/pipeline-to-pipeline.md).
+
+Present caveats:
+* Circular dependencies are not presently detected.
+* Pipeline status is local to each pipeline.
+
+## Data Centric Implementation
+
+Internally Pipelines are implemented using Kafka. Each input and output to a pipeline step has an
+associated Kafka topic. This has many advantages and allows auditing, replay and debugging easier as data
+is preserved from every step in your pipeline.
+
+Tracing allows you to monitor the processing latency of your pipelines.
+
+![tracing](images/jaeger-tracing.png)
+
+As each request to a pipelines moves through the steps its data will appear in input and output topics. This
+allows a full audit of every transformation to be carried out.
diff --git a/docs/pipelines/index.md b/docs/pipelines/index.md
deleted file mode 100644
index 7ca6036b4e..0000000000
--- a/docs/pipelines/index.md
+++ /dev/null
@@ -1,378 +0,0 @@
-# Pipelines
-
-Pipelines allow models to be connected into flows of data transformations. This allows more complex machine learning pipelines to be created with multiple models, feature transformations and monitoring components such as drift and outlier detectors.
-
-## Creating Pipelines
-
-The simplest way to create Pipelines is by defining them with the [Pipeline resource we provide for Kubernetes](../kubernetes/resources/pipeline/index). This format is accepted by our Kubernetes implementation but also locally via our `seldon` CLI.
-
-Internally in both cases Pipelines are created via our [Scheduler API](../apis/scheduler/index). Advanced users could submit Pipelines directly using this gRPC service.
-
-An example that chains two models together is shown below:
-
-```yaml
-apiVersion: mlops.seldon.io/v1alpha1
-kind: Pipeline
-metadata:
-  name: chain
-  namespace: seldon-mesh
-spec:
-  steps:
-    - name: model1
-    - name: model2
-      inputs:
-      - model1
-  output:
-    steps:
-    - model2
-
-```
-
-  * `steps` allow you to specify the models you want to combine into a pipeline. Each step name will correspond to a model of the same name. These models will need to have been deployed and available for the Pipeline to function, however Pipelines can be deployed before or at the same time you deploy the underlying models.
-  * `steps.inputs` allow you to specify the inputs to this step.
-  * `outputs.steps` allow you to specify the output of the Pipeline. A pipeline can have multiple paths include flows of data that do not reach the output, e.g. Drift detection steps. However, if you wish to call your Pipeline in a synchronous manner via REST/gRPC then an output must be present so the Pipeline can be treated as a function.
-
-## Expressing input data sources
-
-
-Model step inputs are defined with a dot notation of the form:
-
-```
-<stepName>|<pipelineName>.<inputs|outputs>.<tensorName>
-```
-
-Inputs with just a step name will be assumed to be `step.outputs`.
-
-The default payloads for Pipelines is the V2 protocol which requires named tensors as inputs and outputs from a model. If you require just certain tensors from a model you can reference those in the inputs, e.g. `mymodel.outputs.t1` will reference the tensor `t1` from the model `mymodel`.
-
-For the specification of the [V2 protocol](../apis/inference/index.md).
-
-## Chain
-
-The simplest Pipeline chains models together: the output of one model goes into the input of the next. This will work out of the box if the output tensor names from a model match the input tensor names for the one being chained to. If they do not then the `tensorMap` construct presently needs to be used to define the mapping explicitly, e.g. see below for a simple chained pipeline of two tfsimple example models:
-
-```{literalinclude} ../../../../samples/pipelines/tfsimples.yaml
-:language: yaml
-```
-```{mermaid}
-  :caption: "*A simple chain of two models.*"
-  :align: center
-
-  flowchart LR
-      classDef pipeIO fill:#F6E083
-
-      subgraph input
-          INPUT0:::pipeIO
-          INPUT1:::pipeIO
-      end
-
-      INPUT0 --> TF1(tfsimple1)
-      INPUT1 --> TF1
-      TF1 --->|OUTPUT0: INPUT0| TF2(tfsimple2)
-      TF1 --->|OUTPUT1: INPUT1| TF2
-
-      subgraph output
-          OUTPUT0:::pipeIO
-          OUTPUT1:::pipeIO
-      end
-
-      TF2 --> OUTPUT0
-      TF2 --> OUTPUT1
-```
-
-In the above we rename tensor `OUTPUT0` to `INPUT0` and `OUTPUT1` to `INPUT1`. This allows these models to be chained together. The shape and data-type of the tensors needs to match as well.
-
-This example can be found in the [pipeline-examples examples](../examples/pipeline-examples.md#model-chaining).
-
-## Join
-
-Joining allows us to combine outputs from multiple steps as input to a new step.
-
-```{literalinclude} ../../../../samples/pipelines/tfsimples-join.yaml
-:language: yaml
-```
-```{mermaid}
-  :caption: "*Joining the outputs of two models into a third model. The dashed lines signify model outputs that are not captured in the output of the pipeline.*"
-  :align: center
-
-  flowchart LR
-      classDef pipeIO fill:#F6E083
-      classDef hidden fill:#ffffff,stroke:#ffffff
-
-      subgraph input
-          INPUT0:::pipeIO
-          INPUT1:::pipeIO
-      end
-
-      INPUT0 --> TF1(tfsimple1)
-      INPUT1 --> TF1
-      INPUT0 --> TF2(tfsimple2)
-      INPUT1 --> TF2
-      TF1 -.-> |OUTPUT1| tf1( ):::hidden
-      TF1 --> |OUTPUT0: INPUT0| TF3(tfsimple3)
-      TF2 -.-> |OUTPUT0| tf2( ):::hidden
-      TF2 --> |OUTPUT1: INPUT1| TF3
-
-      subgraph output
-          OUTPUT0:::pipeIO
-          OUTPUT1:::pipeIO
-      end
-      TF3 --> OUTPUT0
-      TF3 --> OUTPUT1
-```
-Here we pass the pipeline inputs to two models and then take one output tensor from each and pass to the final model. We use the same `tensorMap` technique to rename tensors as disucssed in the previous section.
-
-Joins can have a join type which can be specified with `inputsJoinType` and can take the values:
-  * `inner` : require all inputs to be available to join.
-  * `outer` : wait for `joinWindowMs` to join any inputs. Ignoring any inputs that have not sent any data at that point. This will mean this step of the pipeline is guaranteed to have a latency of at least `joinWindowMs`.
-  * `any` : Wait for any of the specified data sources.
-
-This example can be found in the [pipeline-examples examples](../examples/pipeline-examples.md#model-join).
-
-## Conditional Logic
-
-Pipelines can create conditional flows via various methods. We will discuss each in turn.
-
-### Model routing via tensors
-
-The simplest way is to create a model that outputs different named tensors based on its decision. This way downstream steps can be dependant on different expected tensors. An example is shown below:
-
-```{literalinclude} ../../../../samples/pipelines/conditional.yaml
-:language: yaml
-```
-
-```{mermaid}
-  :caption: "*Pipeline with a conditional output model. The model **conditional** only outputs one of the two tensors, so only one path through the graph (red or blue) is taken by a single request*"
-  :align: center
-
-  flowchart LR
-      classDef pipeIO fill:#F6E083
-      classDef join fill:#CEE741;
-
-      subgraph input
-      INPUT0:::pipeIO
-          CHOICE:::pipeIO
-          INPUT0:::pipeIO
-          INPUT1:::pipeIO
-      end
-
-      CHOICE --> conditional
-      INPUT0 --> conditional
-      INPUT1 --> conditional
-
-      conditional --> |OUTPUT0: INPUT| add10
-      conditional --> |OUTPUT1: INPUT| mul10
-
-      add10 --> |OUTPUT| any(any):::join
-      mul10 --> |OUTPUT| any
-
-      subgraph output
-          OUTPUT(OUTPUT):::pipeIO
-      end
-
-      any --> OUTPUT
-
-      linkStyle 3 stroke:blue,color:blue;
-      linkStyle 5 stroke:blue,color:blue;
-      linkStyle 4 stroke:red,color:red;
-      linkStyle 6 stroke:red,color:red;
-```
-In the above we have a step `conditional` that either outputs a tensor named `OUTPUT0` or a tensor named `OUTPUT1`. The `mul10` step depends on an output in `OUTPUT0` while the add10 step depends on an output from `OUTPUT1`.
-
-Note, we also have a final Pipeline output step that does an `any` join on these two models essentially outputting fron the pipeline whichever data arrives from either model. This type of Pipeline can be used for Multi-Armed bandit solutions where you want to route traffic dynamically.
-
-This example can be found in the [pipeline-examples examples](../examples/pipeline-examples.md#conditional).
-
-### Errors
-
-Its also possible to abort pipelines when an error is produced to in effect create a condition. This is illustrated below:
-
-```{literalinclude} ../../../../samples/pipelines/error.yaml
-:language: yaml
-```
-
-This Pipeline runs normally or throws an error based on whether the input tensors have certain values.
-
-This example can be found in the [pipeline-examples examples](../examples/pipeline-examples.md#error).
-
-### Triggers
-
-Sometimes you want to run a step if an output is received from a previous step but not to send the data from that step to the model. This is illustrated below:
-
-```{literalinclude} ../../../../samples/pipelines/tfsimples-join-outlier.yaml
-:language: yaml
-```
-
-```{mermaid}
-  :caption: "*A pipeline with a single trigger. The model **tfsimple3** only runs if the model **check** returns a tensor named `OUTPUT`. The green edge signifies that this is a trigger and not an additional input to **tfsimple3**. The dashed lines signify model outputs that are not captured in the output of the pipeline.*"
-  :align: center
-
-  flowchart LR
-    classDef pipeIO fill:#F6E083
-    classDef hidden fill:#ffffff,stroke:#ffffff
-
-    subgraph input
-        INPUT0:::pipeIO
-        INPUT1:::pipeIO
-    end
-
-    INPUT0 --> TF1(tfsimple1)
-    INPUT1 --> TF1
-    INPUT0 --> TF2(tfsimple2)
-    INPUT1 --> TF2
-    TF1 -.-> |OUTPUT1| tf1( ):::hidden
-
-    TF1 --> |OUTPUT0: INPUT| check
-    TF1 --> |OUTPUT0: INPUT0| TF3(tfsimple3)
-    TF2 --> |OUTPUT1: INPUT1| TF3
-    TF2 -.-> |OUTPUT0| tf2( ):::hidden
-
-    check --o |OUTPUT| TF3
-    linkStyle 9 stroke:#CEE741,color:black;
-
-    subgraph output
-      OUTPUT0:::pipeIO
-      OUTPUT1:::pipeIO
-    end
-
-    TF3 --> OUTPUT0
-    TF3 --> OUTPUT1
-```
-
-In this example the last step `tfsimple3` runs only if there are outputs from `tfsimple1` and `tfsimple2` but also data from the `check` step. However, if the step `tfsimple3` is run it only receives the join of data from `tfsimple1` and `tfsimple2`.
-
-This example can be found in the [pipeline-examples examples](../examples/pipeline-examples.md#model-join-with-trigger).
-
-### Trigger Joins
-
-You can also define multiple triggers which need to happen based on a particulr join type. For example:
-
-```{literalinclude} ../../../../samples/pipelines/trigger-joins.yaml
-:language: yaml
-```
-```{mermaid}
-  :caption: "*A pipeline with multiple triggers and a trigger join of type `any`. The pipeline has four inputs, but three of these are optional (signified by the dashed borders).*"
-  :align: center
-
-  flowchart LR
-      classDef pipeIO fill:#F6E083
-      classDef pipeIOopt fill:#F6E083,stroke-dasharray: 5 5;
-      classDef trigger fill:#CEE741;
-      classDef hidden fill:#ffffff,stroke:#ffffff
-      classDef join fill:#CEE741;
-
-      subgraph input
-          ok1:::pipeIOopt
-          ok2:::pipeIOopt
-          INPUT:::pipeIO
-          ok3:::pipeIOopt
-      end
-
-      ok1 --o any
-      ok2 --o any
-      any((any)):::trigger --o mul10
-      linkStyle 0 stroke:#CEE741,color:green;
-      linkStyle 1 stroke:#CEE741,color:green;
-      linkStyle 2 stroke:#CEE741,color:green;
-
-      INPUT --> mul10
-      INPUT --> add10
-
-      ok3 --o add10
-      linkStyle 5 stroke:#CEE741,color:green;
-
-      subgraph output
-        OUTPUT:::pipeIO
-      end
-
-      mul10 -->|OUTPUT| anyOut(any):::join
-      add10 --> |OUTPUT| anyOut
-      anyOut --> OUTPUT
-```
-
-Here the `mul10` step is run if data is seen on the pipeline inputs in the `ok1` or `ok2` tensors based on the `any` join type. If data is seen on `ok3` then the `add10` step is run.
-
-If we changed the `triggersJoinType` for `mul10` to `inner` then both `ok1` and `ok2` would need to appear before `mul10` is run.
-
-### Pipeline Inputs
-
-Pipelines by default can be accessed synchronously via http/grpc or asynchronously via the Kafka topic created for them. However, it's also possible to create a pipeline to take input from one or more other pipelines by specifying an `input` section. If for example we already have the `tfsimple` pipeline shown below:
-
-```{literalinclude} ../../../../samples/pipelines/tfsimple.yaml
-:language: yaml
-```
-
-We can create another pipeline which takes its input from this pipeline, as shown below:
-
-```{literalinclude} ../../../../samples/pipelines/tfsimple-extended.yaml
-:language: yaml
-```
-```{mermaid}
-  :caption: "*A pipeline taking as input the output of another pipeline.*"
-  :align: center
-
-  flowchart LR
-      classDef pipeIO fill:#F6E083
-
-      subgraph tfsimple.inputs
-          INPUT0:::pipeIO
-          INPUT1:::pipeIO
-      end
-      
-      INPUT0 --> TF1(tfsimple1)
-      INPUT1 --> TF1
-      
-      subgraph tfsimple.outputs
-          OUTPUT0:::pipeIO
-          OUTPUT1:::pipeIO
-      end
-      
-      TF1 --> OUTPUT0
-      TF1 --> OUTPUT1
-
-      subgraph tfsimple-extended.inputs
-        INPUT10(INPUT0):::pipeIO
-        INPUT11(INPUT1):::pipeIO
-      end
-
-    OUTPUT0 --> INPUT10
-    OUTPUT1 --> INPUT11
-
-    INPUT10 --> TF2(tfsimple2)
-    INPUT11 --> TF2
-
-      subgraph tfsimple-extended.outputs
-          OUTPUT10(OUTPUT0):::pipeIO
-          OUTPUT11(OUTPUT1):::pipeIO
-      end
-      
-      TF2 --> OUTPUT10
-      TF2 --> OUTPUT11
-```
-
-In this way pipelines can be built to extend existing running pipelines to allow extensibility and sharing of data flows.
-
-The spec follows the same spec for a step except that references to other pipelines are contained in the `externalInputs` section which takes the form of pipeline or pipeline.step references:
-  * `<pipelineName>.(inputs|outputs).<tensorName>`
-  * `<pipelineName>.(step).<stepName>.<tensorName>`
-
-Tensor names are optional and only needed if you want to take just one tensor from an input or output.
-
-There is also an `externalTriggers` section which allows triggers from other pipelines.
-
-Further examples can be found in the [pipeline-to-pipeline examples](../examples/pipeline-to-pipeline.md).
-
-Present caveats:
- * Circular dependencies are not presently detected.
- * Pipeline status is local to each pipeline.
-
-## Data Centric Implementation
-
-Internally Pipelines are implemented using Kafka. Each input and output to a pipeline step has an associated Kafka topic. This has many advantages and allows auditing, replay and debugging easier as data is preserved from every step in your pipeline.
-
-Tracing allows you to monitor the processing latency of your pipelines.
-
-![tracing](jaeger-tracing.png)
- 
-As each request to a pipelines moves through the steps its data will appear in input and output topics. This allows a full audit of every transformation to be carried out.
-
diff --git a/docs/pipelines/jaeger-tracing.png b/docs/pipelines/jaeger-tracing.png
deleted file mode 100644
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From acc7d7c1faaa3ac9afad2fd64afa084bf5928b76 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:01:41 +1000
Subject: [PATCH 10/32] deleted inference dir and moved inference.md to the
 root directory

---
 docs/{inference/index.md => inference.md} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename docs/{inference/index.md => inference.md} (100%)

diff --git a/docs/inference/index.md b/docs/inference.md
similarity index 100%
rename from docs/inference/index.md
rename to docs/inference.md

From f502be2b6a1456a8c7a6b28898a8630b654fdee4 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:02:38 +1000
Subject: [PATCH 11/32] deleted explainers dir and moved explainers.md to the
 root directory

---
 docs/explainers.md       | 51 ++++++++++++++++++++++++++++++++++++++++
 docs/explainers/index.md | 31 ------------------------
 2 files changed, 51 insertions(+), 31 deletions(-)
 create mode 100644 docs/explainers.md
 delete mode 100644 docs/explainers/index.md

diff --git a/docs/explainers.md b/docs/explainers.md
new file mode 100644
index 0000000000..327daf0207
--- /dev/null
+++ b/docs/explainers.md
@@ -0,0 +1,51 @@
+# Explainers
+
+Explainers are Model resources with some extra settings. They allow a range of explainers
+from the Alibi-Explain library to be run on MLServer.
+
+An example Anchors explainer definitions is shown below.
+
+```yaml
+# samples/models/income-explainer.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/income-sklearn/anchor-explainer"
+  explainer:
+    type: anchor_tabular
+    modelRef: income
+```
+
+The key additions are:
+
+* `type`: This must be one of the
+[supported Alibi Explainer types](https://github.com/SeldonIO/MLServer/blob/191ee44297712192fed882afe0797d6a2732965e/runtimes/alibi-explain/mlserver_alibi_explain/alibi_dependency_reference.py#L15-L19)
+supported by the Alibi Explain runtime in MLServer.
+* `modelRef`: The model name for black box explainers.
+* `pipelineRef`: The pipeline name for black box explainers.
+
+Only one of modelRef and pipelineRef is allowed.
+
+## Pipeline Explanations
+
+Blackbox explainers can explain a Pipeline as well as a model. An example from the [Huggingface sentiment demo](../examples/speech-to-sentiment.md) is show below.
+
+```yaml
+# samples/models/hf-sentiment-explainer.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/huggingface/speech-sentiment/explainer"
+  explainer:
+    type: anchor_text
+    pipelineRef: sentiment-explain
+```
+
+## Examples
+* [Tabular income classification model with Anchor Tabular black box model explainer](examples/income.md)
+* [Huggingface Sentiment model with Anchor Text black box pipeline explainer](examples/speech-to-sentiment.md)
+* [Anchor Text movies sentiment explainer](examples/explainer-examples.md)
diff --git a/docs/explainers/index.md b/docs/explainers/index.md
deleted file mode 100644
index 1a8bf0d5b8..0000000000
--- a/docs/explainers/index.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# Explainers
-
-Explainers are Model resources with some extra settings. They allow a range of explainers from the Alibi-Explain library to be run on MLServer.
-
-An example Anchors explainer definitions is shown below.
-
-```{literalinclude} ../../../../samples/models/income-explainer.yaml 
-:language: yaml
-```
-
-The key additions are:
-
- * `type` : This must be one of the [supported Alibi Explainer types](https://github.com/SeldonIO/MLServer/blob/191ee44297712192fed882afe0797d6a2732965e/runtimes/alibi-explain/mlserver_alibi_explain/alibi_dependency_reference.py#L15-L19) supported by the Alibi Explain runtime in MLServer.
- * `modelRef` : The model name for black box explainers.
- * `pipelineRef` : The pipeline name for black box explainers.
-
-Only one of modelRef and pipelineRef is allowed.
-
-## Pipeline Explanations
-
-Blackbox explainers can explain a Pipeline as well as a model. An example from the [Huggingface sentiment demo](../examples/speech-to-sentiment.md) is show below.
-
-```{literalinclude} ../../../../samples/models/hf-sentiment-explainer.yaml 
-:language: yaml
-```
-
-## Examples
- * [Tabular income classification model with Anchor Tabular black box model explainer](../examples/income.md)
- * [Huggingface Sentiment model with Anchor Text black box pipeline explainer](../examples/speech-to-sentiment.md)
- * [Anchor Text movies sentiment explainer](../examples/explainer-examples.md)
- 
\ No newline at end of file

From cfdae58d1be7a7c0a76fcd1c2172a6bf44c0b135 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:03:22 +1000
Subject: [PATCH 12/32] deleted performance-tests dir and moved .md to the root
 directory

---
 docs/performance-tests.md       | 182 ++++++++++++++++++++++++++++++++
 docs/performance-tests/index.md |  77 --------------
 2 files changed, 182 insertions(+), 77 deletions(-)
 create mode 100644 docs/performance-tests.md
 delete mode 100644 docs/performance-tests/index.md

diff --git a/docs/performance-tests.md b/docs/performance-tests.md
new file mode 100644
index 0000000000..13bfde3119
--- /dev/null
+++ b/docs/performance-tests.md
@@ -0,0 +1,182 @@
+# Performance tests
+This section describes how a user can run performance tests to understand the limits of a particular SCv2 deployment.
+
+The base directly is `tests/k6`
+
+## Driver
+[k6](https://k6.io/) is used to drive requests for load, unload and infer workloads. It is recommended that the load
+test is run within the same cluster that has SCv2 installed as it requires internal access to some of the services
+that are not automatically exposed to the outside world. Furthermore having the driver withthin the same cluster
+minimises link latency to SCv2 entrypoint; therefore infer latencies are more representatives of actual overheads of the system.
+
+## Tests
+
+* Envoy
+Tests synchronous inference requests via envoy
+
+To run: `make deploy-envoy-test`
+
+* Agent
+Tests inference requests direct to a specific agent, defaults to triton-0 or mlserver-0
+
+To run: `make deploy-rproxy-test` pr `make deploy-rproxy-mlserver-test`
+
+* Server
+Tests inference requests direct to a specific server (bypassing agent), defaults to triton-0 or mlserver-0
+
+to run: `make deploy-server-test` or `deploy-server-mlserver-test`
+
+* Pipeline gateway (HTTP-Kafka gateway)
+Tests inference requests to one-node pipeline HTTP and GPRC requests
+
+To run: `make deploy-kpipeline-test`
+
+* Model gateway (Kafka-HTTP gateway)
+Tests inference requests to a model via kafka
+
+To run: `deploy-kmodel-test`
+
+## Results
+
+One way to look at results is to look at the log of the pod that executed the kubernetes job.
+
+Results can also be persisted to a gs bucket, a service account `k6-sa-key` in the same namespace is required,
+
+Users can also look at the metrics that are exposed in prometheus while the test is underway
+
+## Building k6 image
+
+In the case a user is modifying the actual scenario of the test:
+
+* `export DOCKERHUB_USERNAME=mydockerhubaccount`
+* build the k6 image via `make build-push`
+* in the same shell environment, deploying jobs will use this custome built docker image
+
+## Modifying tests
+
+Users can modify settings of the tests in `tests/k6/configs/k8s/base/k6.yaml`. This will apply to all subsequent tests that are deployed using the above process.
+
+## Settings
+
+Some settings that can be changed
+
+* k6 args
+  ```yaml
+    # tests/k6/configs/k8s/base/k6.yaml
+    args: [
+      "--no-teardown",
+      "--summary-export",
+      "results/base.json",
+      "--out",
+      "csv=results/base.gz",
+      "-u",
+      "5",
+      "-i",
+      "100000",
+      "-d",
+      "120m",
+      "scenarios/infer_constant_vu.js",
+      ]
+    # # infer_constant_rate
+    # args: [
+    #   "--no-teardown",
+    #   "--summary-export",
+    #   "results/base.json",
+    #   "--out",
+    #   "csv=results/base.gz",
+    #   "scenarios/infer_constant_rate.js",
+    #   ]
+    # # k8s-test-script
+    # args: [
+    #   "--summary-export",
+    #   "results/base.json",
+    #   "--out",
+    #   "csv=results/base.gz",
+    #   "scenarios/k8s-test-script.js",
+    #   ]
+    # # core2_qa_control_plane_ops
+    # args: [
+    #   "--no-teardown",
+    #   "--verbose",
+    #   "--summary-export",
+    #   "results/base.json",
+    #   "--out",
+    #   "csv=results/base.gz",
+    #   "-u",
+    #   "5",
+    #   "-i",
+    #   "10000",
+    #   "-d",
+    #   "9h",
+    #   "scenarios/core2_qa_control_plane_ops.js",
+    #   ]
+   ```
+for a full list, check [k6 args](https://k6.io/docs/using-k6/options/)
+* Environment variables
+  ```yaml
+    - name: INFER_HTTP_ITERATIONS
+      value: "1"
+    - name: INFER_GRPC_ITERATIONS
+      value: "1"
+    - name: MODELNAME_PREFIX
+      value: "tfsimplea,pytorch-cifar10a,tfmnista,mlflow-winea,irisa"
+    - name: MODEL_TYPE
+      value: "tfsimple,pytorch_cifar10,tfmnist,mlflow_wine,iris"
+    # Specify MODEL_MEMORY_BYTES using unit-of measure suffixes (k, M, G, T)
+    # rather than numbers without units of measure. If supplying "naked
+    # numbers", the seldon operator will take care of converting the number
+    # for you but also take ownership of the field (as FieldManager), so the
+    # next time you run the scenario creating/updating of the model CR will
+    # fail.
+    - name: MODEL_MEMORY_BYTES
+      value: "400k,8M,43M,200k,3M"
+    - name: MAX_MEM_UPDATE_FRACTION
+      value: "0.1"
+    - name: MAX_NUM_MODELS
+      value: "800,100,25,100,100"
+      # value: "0,0,25,100,100"
+    #
+    # MAX_NUM_MODELS_HEADROOM is a variable used by control-plane tests.
+    # It's the approximate number of models that can be created over
+    # MAX_NUM_MODELS over the experiment. In the worst case scenario
+    # (very unlikely) the HEADROOM values may temporarily exceed the ones
+    # specified here with the number of VUs, because each VU checks the
+    # headroom constraint independently before deciding on the available
+    # operations (no communication/sync between VUs)
+    # - name: MAX_NUM_MODELS_HEADROOM
+    #   value: "20,5,0,20,30"
+    #
+    # MAX_MODEL_REPLICAS is used by control-plane tests. It controls the
+    # maximum number of replicas that may be requested when
+    # creating/updating models of a given type.
+    # - name: MAX_MODEL_REPLICAS
+    #   value: "2,2,0,2,2"
+    #
+    - name: INFER_BATCH_SIZE
+      value: "1,1,1,1,1"
+    # MODEL_CREATE_UPDATE_DELETE_BIAS defines the probability ratios between
+    # the operations, for control-plane tests. For example, "1, 4, 3"
+    # makes an Update four times more likely then a Create, and a Delete 3
+    # times more likely than the Create.
+    # - name: MODEL_CREATE_UPDATE_DELETE_BIAS
+    #   value: "1,3,1"
+    - name: WARMUP
+      value: "false"
+  ```
+    * for `MODEL_TYPE`, choose from:
+  ```js
+  // tests/k6/components/model.js
+    import { dump as yamlDump } from "https://cdn.jsdelivr.net/npm/js-yaml@4.1.0/dist/js-yaml.mjs";
+    import { getConfig } from '../components/settings.js'
+
+    const tfsimple_string = "tfsimple_string"
+    const tfsimple = "tfsimple"
+    const iris = "iris"  // mlserver
+    const pytorch_cifar10 = "pytorch_cifar10"
+    const tfmnist = "tfmnist"
+    const tfresnet152 = "tfresnet152"
+    const onnx_gpt2 = "onnx_gpt2"
+    const mlflow_wine = "mlflow_wine" // mlserver
+    const add10 = "add10" // https://github.com/SeldonIO/triton-python-examples/tree/master/add10
+    const sentiment = "sentiment" // mlserver
+  ```
diff --git a/docs/performance-tests/index.md b/docs/performance-tests/index.md
deleted file mode 100644
index aa8e12c8da..0000000000
--- a/docs/performance-tests/index.md
+++ /dev/null
@@ -1,77 +0,0 @@
-# Performance tests
-This section describes how a user can run performance tests to understand the limits of a particular SCv2 deployment.
-
-The base directly is `tests/k6`
-
-## Driver
-[k6](https://k6.io/) is used to drive requests for load, unload and infer workloads. It is recommended that the load test is run within the same cluster that has SCv2 installed as it requires internal access to some of the services that are not automatically exposed to the outside world. Furthermore having the driver withthin the same cluster minimises link latency to SCv2 entrypoint; therefore infer latencies are more representatives of actual overheads of the system.
-
-## Tests
-
-* Envoy
-Tests synchronous inference requests via envoy
-
-To run: `make deploy-envoy-test`
-
-* Agent
-Tests inference requests direct to a specific agent, defaults to triton-0 or mlserver-0
-
-To run: `make deploy-rproxy-test` pr `make deploy-rproxy-mlserver-test`
-
-* Server
-Tests inference requests direct to a specific server (bypassing agent), defaults to triton-0 or mlserver-0
-
-to run: `make deploy-server-test` or `deploy-server-mlserver-test`
-
-* Pipeline gateway (HTTP-Kafka gateway)
-Tests inference requests to one-node pipeline HTTP and GPRC requests
-
-To run: `make deploy-kpipeline-test`
-
-* Model gateway (Kafka-HTTP gateway)
-Tests inference requests to a model via kafka
-
-To run: `deploy-kmodel-test`
-
-## Results
-
-One way to look at results is to look at the log of the pod that executed the kubernetes job.
-
-Results can also be persisted to a gs bucket, a service account `k6-sa-key` in the same namespace is required,
-
-Users can also look at the metrics that are exposed in prometheus while the test is underway
-
-## Building k6 image
-
-In the case a user is modifying the actual scenario of the test:
-
-* `export DOCKERHUB_USERNAME=mydockerhubaccount`
-* build the k6 image via `make build-push`
-* in the same shell environment, deploying jobs will use this custome built docker image
-
-## Modifying tests
-
-Users can modify settings of the tests in `tests/k6/configs/k8s/base/k6.yaml`. This will apply to all subsequent tests that are deployed using the above process.
-
-## Settings
-
-Some settings that can be changed
-
-* k6 args
-  ```{literalinclude} ../../../../tests/k6/configs/k8s/base/k6.yaml
-   :language: yaml
-   :start-after: args
-   :end-before: env
-   ```
-for a full list, check [k6 args](https://k6.io/docs/using-k6/options/)
-* Environment variables
-  ```{literalinclude} ../../../../tests/k6/configs/k8s/base/k6.yaml
-   :language: yaml
-   :start-after: SCHEDULER_ENDPOINT}:9004
-   :end-before: GOOGLE_APPLICATION_CREDENTIALS 
-   ```
-    * for `MODEL_TYPE`, choose from: 
-  ```{literalinclude} ../../../../tests/k6/components/model.js
-   :language: javascript
-   :end-before: const models
-   ```

From 55620fc835cb66b989be018b90dc487864289a72 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:03:51 +1000
Subject: [PATCH 13/32] deleted experiments dir and moved .md to the root
 directory

---
 docs/experiments.md       | 8 ++++++++
 docs/experiments/index.md | 8 --------
 2 files changed, 8 insertions(+), 8 deletions(-)
 create mode 100644 docs/experiments.md
 delete mode 100644 docs/experiments/index.md

diff --git a/docs/experiments.md b/docs/experiments.md
new file mode 100644
index 0000000000..53ef570c74
--- /dev/null
+++ b/docs/experiments.md
@@ -0,0 +1,8 @@
+# Experiments
+
+An Experiment defines an http traffic split between Models or Pipelines.
+
+Experiments also allow a mirror model or pipeline to be tested where some
+percentage of the traffic to the main model is sent to the mirror but the result is not returned.
+
+Further details are given [here](kubernetes/resources/experiment.md).
diff --git a/docs/experiments/index.md b/docs/experiments/index.md
deleted file mode 100644
index fff54ac9e1..0000000000
--- a/docs/experiments/index.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# Experiments
-
-An Experiment defines an http traffic split between Models or Pipelines.
-
-Experiments also allow a mirror model or pipeline to be tested where some percentage of the traffic to the main model is sent to the mirror but the result is not returned.
-
-Further details are given [here](../kubernetes/resources/experiment/index.md).
-

From 1fbd36632c66ed291ca05cbb2587ad5538e39ec5 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:04:40 +1000
Subject: [PATCH 14/32] updated about section to match gitbook's expected
 format

---
 docs/about.md | 52 ++++++++++++++++++++++++++-------------------------
 1 file changed, 27 insertions(+), 25 deletions(-)

diff --git a/docs/about.md b/docs/about.md
index a147fae0d8..709f5d7321 100644
--- a/docs/about.md
+++ b/docs/about.md
@@ -1,56 +1,58 @@
-# About 
+# About
 
-Seldon V2 APIs provide a state of the art solution for machine learning inference which can be run locally on a laptop as well as on Kubernetes for production.
+Seldon V2 APIs provide a state of the art solution for machine learning inference which
+can be run locally on a laptop as well as on Kubernetes for production.
 
-```{youtube} ar5lSG_idh4
-:width: 100%
-```
+{% embed url="https://www.youtube.com/watch?v=ar5lSG_idh4" %}
 
 ## Features
 
- * A single platform for inference of wide range of standard and custom artifacts.
- * Deploy locally in Docker during development and testing of models.
- * Deploy at scale on Kubernetes for production.
- * Deploy single models to multi-step pipelines.
- * Save infrastructure costs by deploying multiple models transparently in inference servers.
- * Overcommit on resources to deploy more models than available memory.
- * Dynamically extended models with pipelines with a data-centric perspective backed by Kafka.
- * Explain individual models and pipelines with state of the art explanation techniques.
- * Deploy drift and outlier detectors alongside models.
- * Kubernetes Service mesh agnostic - use the service mesh of your choice.
+* A single platform for inference of wide range of standard and custom artifacts.
+* Deploy locally in Docker during development and testing of models.
+* Deploy at scale on Kubernetes for production.
+* Deploy single models to multi-step pipelines.
+* Save infrastructure costs by deploying multiple models transparently in inference servers.
+* Overcommit on resources to deploy more models than available memory.
+* Dynamically extended models with pipelines with a data-centric perspective backed by Kafka.
+* Explain individual models and pipelines with state of the art explanation techniques.
+* Deploy drift and outlier detectors alongside models.
+* Kubernetes Service mesh agnostic - use the service mesh of your choice.
 
 
 ## Core features and comparison to Seldon Core V1 APIs
 
-Our V2 APIs separate out core tasks into separate resources allowing users to get started fast with deploying a Model and the progressing to more complex Pipelines, Explanations and Experiments.
+Our V2 APIs separate out core tasks into separate resources allowing users to get started fast
+with deploying a Model and the progressing to more complex Pipelines, Explanations and Experiments.
 
-![intro](intro.png)
+![intro](images/intro.png)
 
 ## Multi-model serving
 
 Seldon transparently will provision your model onto the correct inference server.
 
-![mms1](multimodel1.png)
+![mms1](images/multimodel1.png)
 
-By packing multiple models onto a smaller set of servers users can save infrastructure costs and efficiently utilize their models.
+By packing multiple models onto a smaller set of servers users can save infrastructure costs and
+efficiently utilize their models.
 
-![mms2](../models/mms/mms.png)
+![mms2](images/mms.png)
 
-By allowing over-commit users can provision model models that available memory resources by allowing Seldon to transparently unload models that are not in use.
+By allowing over-commit users can provision model models that available memory resources by
+allowing Seldon to transparently unload models that are not in use.
 
-![mms3](../models/mms/overcommit.png)
+![mms3](images/overcommit.png)
 
 ## Inference Servers
 
 Seldon V2 supports any V2 protocol inference server. At present we include Seldon's MLServer and NVIDIA's Triton inference server automatically on install. These servers cover a wide range of artifacts including custom python models.
 
-![servers](servers.png)
+![servers](images/servers.png)
 
 ## Service Mesh Agnostic
 
 Seldon core v2 can be integrated with any Kubernetes service mesh. There are current examples with istio, Ambassador and Traefic.
 
-![mesh](mesh.png)
+![mesh](images/mesh.png)
 
 ## Publication
 
@@ -58,4 +60,4 @@ These features are influenced by our position paper on the next generation of ML
 
 *Title*: [Desiderata for next generation of ML model serving](http://arxiv.org/abs/2210.14665)
 
-*Workshop*: Challenges in deploying and monitoring ML systems workshop - NeurIPS 2022
\ No newline at end of file
+*Workshop*: Challenges in deploying and monitoring ML systems workshop - NeurIPS 2022

From 6387791ac8bfd133615e6dc9a179bbcca0032e0a Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:08:17 +1000
Subject: [PATCH 15/32] updated FAQs section to match gitbook's expected format

---
 docs/faqs.md       | 35 +++++++++++++++++++++++++++++++++++
 docs/faqs/index.md | 34 ----------------------------------
 2 files changed, 35 insertions(+), 34 deletions(-)
 create mode 100644 docs/faqs.md
 delete mode 100644 docs/faqs/index.md

diff --git a/docs/faqs.md b/docs/faqs.md
new file mode 100644
index 0000000000..c7d9ba6019
--- /dev/null
+++ b/docs/faqs.md
@@ -0,0 +1,35 @@
+# FAQs
+
+## Can Seldon Core V2 be used with Seldon Core V1
+
+The two projects are able to be run side by side. Existing users of Seldon Core can update to
+deploy their models using Seldon Core V2 as needed to take advantage of the new functionality.
+Both will be supported.
+
+## Should I choose V1 APIs or V2 APIs
+
+This depends on your use case. V2 APIs are not yet at GA so might contain breaking changes in
+future releases.
+
+**Use V1 for:**
+* Tight integration to Seldon V1 protocol
+* Tensorflow Server requirements
+* Need managed istio integration
+
+**Use V2 for:**
+* Multi-model serving
+* More expressive DAG inference pipelines
+* Data-centric (Kafka)
+* Service mesh agnostic
+* Simpler single model usage
+* V2 Protocol
+
+## Can I do payload logging in Seldon Core v2?
+
+By default, the input and output of every step in a pipeline (as well as the pipeline itself)
+is logged in Kafka. From there it's up to you what to do with the data. You could use something
+like [Kafka Connect](https://docs.confluent.io/platform/current/connect/index.html) to stream the
+logs to a datastore of your choice.
+
+Note that there is no automatic request logging for models being accessed directly over REST or gRPC.
+Requests need to be sent via pipelines to be recorded in Kafka.
diff --git a/docs/faqs/index.md b/docs/faqs/index.md
deleted file mode 100644
index 5cb7df9aa2..0000000000
--- a/docs/faqs/index.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# FAQs
-
-## Can Seldon Core V2 be used with Seldon Core V1
-
-The two projects are able to be run side by side. Existing users of Seldon Core can update to deploy their models using Seldon Core V2 as needed to take advantage of the new functionality. Both will be supported.
-
-## Should I choose V1 APIs or V2 APIs
-
-This depends on your use case. V2 APIs are not yet at GA so might contain breaking changes in future releases.
-
- Use V1 for:
-
-  * Tight integration to Seldon V1 protocol
-  * Tensorflow Server requirements
-  * Need managed istio integration
-
- Use V2 for:
-
-  * Multi-model serving
-  * More expressive DAG inference pipelines
-  * Data-centric (Kafka)
-  * Service mesh agnostic
-  * Simpler single model usage
-  * V2 Protocol
-
-## Can I do payload logging in Seldon Core v2?
-
-By default, the input and output of every step in a pipeline (as well as the pipeline itself) is logged in Kafka.
-From there it's up to you what to do with the data.
-You could use something like [Kafka Connect](https://docs.confluent.io/platform/current/connect/index.html) to stream the logs to a datastore of your choice. 
-
-Note that there is no automatic request logging for models being accessed directly over REST or gRPC.
-Requests need to be sent via pipelines to be recorded in Kafka.
-

From ae1d6e3634843f7e146acc145617fef8b6e62737 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:09:18 +1000
Subject: [PATCH 16/32] updated pandas query section with choice1.yaml

---
 docs/models/parameterized-models/pandasquery.md | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/docs/models/parameterized-models/pandasquery.md b/docs/models/parameterized-models/pandasquery.md
index e60af416c8..8e6bf2d48d 100644
--- a/docs/models/parameterized-models/pandasquery.md
+++ b/docs/models/parameterized-models/pandasquery.md
@@ -4,7 +4,18 @@ This model allows a [Pandas](https://pandas.pydata.org/)  query to be run in the
 
 ```yaml
 # samples/models/choice1.yaml
-
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: choice-is-one
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/pandasquery"
+  requirements:
+  - mlserver
+  - python
+  parameters:
+  - name: query
+    value: "choice == 1"
 ```
 
 This invocation check filters for tensor A having value 1.

From c57384a781b8ad89a09cc15b110dab84e9e33206 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:17:07 +1000
Subject: [PATCH 17/32] mostly moved and renamed files and directories

---
 docs/apis/scheduler/README.md                 |  423 ----
 docs/architecture/README.md                   |   25 +-
 docs/cli/docs/seldon.md                       |   18 -
 docs/cli/docs/seldon_config.md                |   27 -
 docs/cli/docs/seldon_config_activate.md       |   22 -
 docs/cli/docs/seldon_config_add.md            |   22 -
 docs/cli/docs/seldon_config_deactivate.md     |   22 -
 docs/cli/docs/seldon_config_list.md           |   22 -
 docs/cli/docs/seldon_config_remove.md         |   22 -
 docs/cli/docs/seldon_experiment.md            |   26 -
 docs/cli/docs/seldon_experiment_list.md       |   24 -
 docs/cli/docs/seldon_experiment_start.md      |   27 -
 docs/cli/docs/seldon_experiment_status.md     |   27 -
 docs/cli/docs/seldon_experiment_stop.md       |   26 -
 docs/cli/docs/seldon_model.md                 |   28 -
 docs/cli/docs/seldon_model_infer.md           |   33 -
 docs/cli/docs/seldon_model_list.md            |   24 -
 docs/cli/docs/seldon_model_load.md            |   27 -
 docs/cli/docs/seldon_model_metadata.md        |   24 -
 docs/cli/docs/seldon_model_status.md          |   27 -
 docs/cli/docs/seldon_model_unload.md          |   26 -
 docs/cli/docs/seldon_pipeline.md              |   28 -
 docs/cli/docs/seldon_pipeline_infer.md        |   33 -
 docs/cli/docs/seldon_pipeline_inspect.md      |   29 -
 docs/cli/docs/seldon_pipeline_list.md         |   24 -
 docs/cli/docs/seldon_pipeline_load.md         |   27 -
 docs/cli/docs/seldon_pipeline_status.md       |   27 -
 docs/cli/docs/seldon_pipeline_unload.md       |   26 -
 docs/cli/docs/seldon_server.md                |   24 -
 docs/cli/docs/seldon_server_list.md           |   24 -
 docs/cli/docs/seldon_server_status.md         |   26 -
 docs/cli/index.md                             |  185 --
 docs/conversational_memory.ipynb              |  386 ----
 docs/examples/index.md                        |   80 -
 docs/examples/pipeline-to-pipeline.md         | 1932 ++++++++++++++++-
 docs/examples/speech-to-sentiment.md          |  523 ++++-
 docs/examples/tritonclient-examples.md        |  515 ++++-
 docs/kubernetes/autoscaling/index.md          |   88 -
 docs/kubernetes/index.md                      |   17 -
 docs/kubernetes/kafka/index.md                |   33 -
 docs/kubernetes/metrics/index.md              |   43 -
 docs/kubernetes/resources/experiment/index.md |   77 -
 docs/kubernetes/resources/index.md            |   31 -
 docs/kubernetes/resources/model/index.md      |   21 -
 docs/kubernetes/resources/pipeline/index.md   |   24 -
 .../resources/seldonconfig/index.md           |   25 -
 .../resources/seldonruntime/index.md          |   53 -
 docs/kubernetes/resources/server/index.md     |   54 -
 .../resources/serverconfig/index.md           |   12 -
 docs/kubernetes/scaling/index.md              |   61 -
 .../service-meshes/ambassador/index.md        |   33 -
 docs/kubernetes/service-meshes/index.md       |   20 -
 docs/kubernetes/service-meshes/istio/index.md |   28 -
 .../service-meshes/traefik/index.md           |   25 -
 docs/kubernetes/storage-secrets/index.md      |  164 --
 docs/kubernetes/tracing/index.md              |   10 -
 docs/kubernetes/tracing/jaeger-trace.png      |  Bin 206646 -> 0 bytes
 docs/metrics/dashboard.png                    |  Bin 134629 -> 0 bytes
 docs/metrics/hodometer-architecture.png       |  Bin 39418 -> 0 bytes
 docs/metrics/index.md                         |   21 -
 docs/source/_static/images/favicon.ico        |  Bin 950 -> 0 bytes
 .../images/header-gradient-core-v2.png        |  Bin 983305 -> 0 bytes
 .../source/_static/images/header-gradient.png |  Bin 943594 -> 0 bytes
 .../_static/images/illustration-core-v2.png   |  Bin 460184 -> 0 bytes
 docs/source/_static/images/illustration.png   |  Bin 232666 -> 0 bytes
 docs/source/_static/images/logo.svg           |   40 -
 docs/source/_templates/custom/index.html      |  123 --
 docs/source/_templates/header.html            |   61 -
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diff --git a/docs/apis/scheduler/README.md b/docs/apis/scheduler/README.md
deleted file mode 100644
index 21d017a9ea..0000000000
--- a/docs/apis/scheduler/README.md
+++ /dev/null
@@ -1,423 +0,0 @@
-# Scheduler API
-
-The Seldon scheduler API provides a gRPC service to allow Models, Servers, Experiments, and Pipelines to be managed. In Kubernetes the manager deployed by Seldon translates Kubernetes custom resource definitions into calls to the Seldon Scheduler.
-
-In non-Kubernetes environments users of Seldon could create a client to directly control Seldon resources using this API.
-
-## Proto Definition
-
-```proto
-syntax = "proto3";
-
-package seldon.mlops.scheduler;
-
-option go_package = "github.com/seldonio/seldon-core/apis/go/v2/mlops/scheduler";
-
-import "google/protobuf/timestamp.proto";
-
-// [START Messages]
-
-message LoadModelRequest {
-  Model model = 1;
-}
-
-message Model {
-  MetaData meta = 1;
-  ModelSpec modelSpec = 2;
-  DeploymentSpec deploymentSpec = 3;
-  StreamSpec streamSpec = 4;
-}
-
-message MetaData {
-  string name = 1;
-  optional string kind = 2;
-  optional string version = 3;
-  optional KubernetesMeta kubernetesMeta = 4; // Kubernetes specific config
-}
-
-message DeploymentSpec {
-  uint32 replicas = 1;
-  uint32 minReplicas = 2;
-  uint32 maxReplicas = 3;
-  bool logPayloads = 4;
-}
-
-/* ModelDetails
-*/
-message ModelSpec {
-  string uri = 1; // storage uri from where to download the artifacts
-  optional uint32 artifactVersion = 2; // Optional v2 version folder to select
-  optional StorageConfig storageConfig = 3; // Storage auth configuration
-  repeated string requirements = 4; // list of capabilities the server must satisfy to run this model
-  optional uint64 memoryBytes = 5; // Requested memory
-  optional string server = 6; // the particular model server to load the model. If unspecified will be chosen.
-  optional ExplainerSpec explainer = 7; // optional black box explainer details
-  repeated ParameterSpec parameters = 8; // parameters to load with model
-}
-
-message ParameterSpec {
-  string name = 1;
-  string value = 2;
-}
-
-message ExplainerSpec {
-  string type = 1;
-  // 1 of semantic either model or pipeline reference
-  optional string modelRef = 2;
-  optional string pipelineRef = 3;
-}
-
-message KubernetesMeta {
-  string namespace = 1;
-  int64 generation = 2;
-}
-
-message StreamSpec {
-  string inputTopic = 2;
-  string outputTopic = 3;
-}
-
-message StorageConfig {
-  oneof config {
-    string storageSecretName = 1;
-    string storageRcloneConfig = 2;
-  }
-}
-
-message LoadModelResponse {
-
-}
-
-/* ModelReference represents a unique model
-*/
-message ModelReference {
-  string name = 1;
-  optional uint32 version = 2;
-}
-
-message UnloadModelRequest {
-  ModelReference model = 1;
-  optional KubernetesMeta kubernetesMeta = 2;
-}
-
-message UnloadModelResponse {
-}
-
-/* ModelStatusResponse provides the current assignment of the model onto a server
-*/
-message ModelStatusResponse {
-  string modelName = 1;
-  repeated ModelVersionStatus versions = 2;
-  bool deleted = 3;
-}
-
-message ModelVersionStatus {
-  uint32 version = 2;
-  string serverName = 3;
-  optional KubernetesMeta kubernetesMeta = 4;
-  map<int32,ModelReplicaStatus> modelReplicaState = 5;
-  ModelStatus state = 6;
-  optional Model modelDefn = 7;
-}
-
-message ModelStatus {
-  enum ModelState {
-      ModelStateUnknown = 0;
-      ModelProgressing = 1;
-      ModelAvailable = 2;
-      ModelFailed = 3;
-      ModelTerminating = 4;
-      ModelTerminated = 5;
-      ModelTerminateFailed = 6;
-      ScheduleFailed = 7;
-  }
-  ModelState state = 1;
-  string reason = 2;
-  uint32 availableReplicas = 3;
-  uint32 unavailableReplicas = 4;
-  google.protobuf.Timestamp lastChangeTimestamp = 5;
-}
-
-message ModelReplicaStatus {
-  enum ModelReplicaState {
-      ModelReplicaStateUnknown = 0;
-      LoadRequested = 1;
-      Loading = 2;
-      Loaded = 3;
-      LoadFailed = 4;
-      UnloadRequested = 5;
-      Unloading = 6;
-      Unloaded = 7;
-      UnloadFailed = 8;
-      Available = 9;
-      LoadedUnavailable = 10;
-      UnloadEnvoyRequested = 11;
-      Draining = 12;
-  }
-  ModelReplicaState state = 1;
-  string reason = 2;
-  google.protobuf.Timestamp lastChangeTimestamp = 3;
-}
-
-message ServerStatusRequest {
-  string subscriberName = 1;
-  optional string name = 2; // Leave empty for all servers
-}
-
-/* ServerStatusResponse provides details of current server status
-*/
-message ServerStatusResponse {
-  string serverName = 1;
-  repeated ServerReplicaResources resources = 2;
-  int32 expectedReplicas = 3;
-  int32 availableReplicas = 4;
-  int32 numLoadedModelReplicas = 5;
-  optional KubernetesMeta kubernetesMeta = 6;
-}
-
-message ServerReplicaResources {
-  uint32 replicaIdx = 1;
-  uint64 totalMemoryBytes = 2;
-  uint64 availableMemoryBytes = 3;
-  int32 numLoadedModels = 4;
-  uint32 overCommitPercentage = 5;
-}
-
-message ModelSubscriptionRequest {
-  string subscriberName = 1; //Name of the subscription caller
-}
-
-message ModelStatusRequest {
-  string subscriberName = 1;
-  optional ModelReference model = 2;
-  bool allVersions = 3;
-}
-
-message ServerNotifyRequest {
-  string name = 1;
-  int32 expectedReplicas = 2;
-  bool shared = 3;
-  optional KubernetesMeta kubernetesMeta = 4;
-}
-
-message ServerNotifyResponse {
-
-}
-
-message ServerSubscriptionRequest {
-  string subscriberName = 1; //Name of the subscription caller
-}
-
-// Experiments
-
-message StartExperimentRequest {
-  Experiment experiment = 1;
-}
-
-enum ResourceType {
-  MODEL = 0;
-  PIPELINE = 1;
-}
-
-message Experiment {
-  string name = 1;
-  optional string default = 2;
-  repeated ExperimentCandidate candidates = 3;
-  optional ExperimentMirror mirror = 4;
-  optional ExperimentConfig config = 5;
-  optional KubernetesMeta kubernetesMeta = 6;
-  ResourceType resourceType = 7;
-}
-
-message ExperimentConfig {
-  bool stickySessions = 1;
-}
-
-message ExperimentCandidate {
-  string name = 1;
-  uint32 weight = 2;
-}
-
-message ExperimentMirror {
-  string name = 1;
-  uint32 percent = 2;
-}
-
-message StartExperimentResponse {
-}
-
-message StopExperimentRequest {
-  string name = 1;
-}
-
-message StopExperimentResponse {
-}
-
-message ExperimentSubscriptionRequest {
-  string subscriberName = 1; //Name of the subscription caller
-}
-
-message ExperimentStatusResponse {
-  string experimentName = 1;
-  bool active = 2;
-  bool candidatesReady = 3;
-  bool mirrorReady = 4;
-  string statusDescription = 5;
-  optional KubernetesMeta kubernetesMeta = 6;
-}
-
-message LoadPipelineRequest {
-  Pipeline pipeline = 1;
-}
-
-message ExperimentStatusRequest {
-  string subscriberName = 1;
-  optional string name = 2; // Leave empty for all experiments
-}
-
-message Pipeline {
-  string name = 1;
-  string uid = 2;
-  uint32 version = 3;
-  repeated PipelineStep steps = 4;
-  optional PipelineOutput output = 5;
-  optional KubernetesMeta kubernetesMeta = 6;
-  optional PipelineInput input = 7;
-}
-
-message PipelineStep {
-  enum JoinOp {
-    INNER = 0;
-    OUTER = 1;
-    ANY = 2;
-  }
-  string name = 1;
-  repeated string inputs = 2;
-  optional uint32 joinWindowMs = 3; // Join window millisecs, some nonzero default (TBD)
-  map<string,string> tensorMap = 4; // optional map of tensor name mappings
-  JoinOp inputsJoin = 5;
-  repeated string triggers = 6;
-  JoinOp triggersJoin = 7;
-  Batch batch = 8;
-}
-
-message Batch {
-  optional uint32 size = 1;
-  optional uint32 windowMs = 2;
-}
-
-message PipelineInput {
-  enum JoinOp {
-    INNER = 0;
-    OUTER = 1;
-    ANY = 2;
-  }
-  repeated string externalInputs = 1;
-  repeated string externalTriggers = 2;
-  optional uint32 joinWindowMs = 3; // Join window millisecs for output, default 0
-  JoinOp joinType = 4;
-  JoinOp triggersJoin = 5;
-  map<string,string> tensorMap = 6; // optional map of tensor name mappings
-}
-
-message PipelineOutput {
-  enum JoinOp {
-    INNER = 0;
-    OUTER = 1;
-    ANY = 2;
-  }
-  repeated string steps = 1;
-  uint32 joinWindowMs = 2; // Join window millisecs for output, default 0
-  JoinOp stepsJoin = 3;
-  map<string,string> tensorMap = 4; // optional map of tensor name mappings
-}
-
-message LoadPipelineResponse {
-
-}
-
-message UnloadPipelineRequest {
-  string name = 1;
-}
-
-message UnloadPipelineResponse {
-
-}
-
-message PipelineStatusRequest {
-  string subscriberName = 1;
-  optional string name = 2; // Leave empty for all pipelines
-  bool allVersions = 3;
-}
-
-message PipelineSubscriptionRequest {
-  string subscriberName = 1; //Name of the subscription caller
-}
-
-message PipelineStatusResponse {
-  string pipelineName = 1;
-  repeated PipelineWithState versions = 2;
-}
-
-message PipelineWithState {
-  Pipeline pipeline = 1;
-  PipelineVersionState state = 2;
-}
-
-message PipelineVersionState {
-  enum PipelineStatus {
-    PipelineStatusUnknown = 0;
-    PipelineCreate = 1;
-    PipelineCreating = 2;
-    PipelineReady = 3;
-    PipelineFailed = 4;
-    PipelineTerminate = 5;
-    PipelineTerminating = 6;
-    PipelineTerminated = 7;
-  }
-  uint32 pipelineVersion = 1;
-  PipelineStatus status = 2;
-  string reason = 3;
-  google.protobuf.Timestamp lastChangeTimestamp = 4;
-  bool modelsReady = 5;
-}
-
-message SchedulerStatusRequest {
-  string subscriberName = 1;
-}
-
-message SchedulerStatusResponse {
-  string applicationVersion = 1;
-}
-
-// [END Messages]
-
-
-// [START Services]
-
-service Scheduler {
-  rpc ServerNotify(ServerNotifyRequest) returns (ServerNotifyResponse) {};
-
-  rpc LoadModel(LoadModelRequest) returns (LoadModelResponse) {};
-  rpc UnloadModel(UnloadModelRequest) returns (UnloadModelResponse) {};
-
-  rpc LoadPipeline(LoadPipelineRequest) returns (LoadPipelineResponse) {};
-  rpc UnloadPipeline(UnloadPipelineRequest) returns (UnloadPipelineResponse) {};
-
-  rpc StartExperiment(StartExperimentRequest) returns (StartExperimentResponse) {};
-  rpc StopExperiment(StopExperimentRequest) returns (StopExperimentResponse) {};
-
-  rpc ServerStatus(ServerStatusRequest) returns (stream ServerStatusResponse) {}
-  rpc ModelStatus(ModelStatusRequest) returns (stream ModelStatusResponse) {}
-  rpc PipelineStatus(PipelineStatusRequest) returns (stream PipelineStatusResponse) {};
-  rpc ExperimentStatus(ExperimentStatusRequest) returns (stream ExperimentStatusResponse) {};
-  rpc SchedulerStatus(SchedulerStatusRequest) returns (SchedulerStatusResponse) {};
-
-  rpc SubscribeServerStatus(ServerSubscriptionRequest) returns (stream ServerStatusResponse) {};
-  rpc SubscribeModelStatus(ModelSubscriptionRequest) returns (stream ModelStatusResponse) {};
-  rpc SubscribeExperimentStatus(ExperimentSubscriptionRequest) returns (stream ExperimentStatusResponse) {};
-  rpc SubscribePipelineStatus(PipelineSubscriptionRequest) returns (stream PipelineStatusResponse) {};
-}
-
-// [END Services]
-```
diff --git a/docs/architecture/README.md b/docs/architecture/README.md
index c806de4abd..da3b8669fe 100644
--- a/docs/architecture/README.md
+++ b/docs/architecture/README.md
@@ -6,12 +6,12 @@ The current set of components used in Seldon Core V2 is shown below:
 
 The core components are:
 
- * Scheduler : manages the load and unload of models, pipelines, explainers and experiments.
- * Pipeline gateway : handles REST/gRPC calls to pipelines.
- * Dataflow engine : handles the flow of data between components in a pipeline.
- * Model gateway : handles the flow of data from models to inference requests on servers and passes on the responses.
- * Agent : manages the loading and unloading of models on a server and access to the server over REST/gRPC.
- * Envoy : manages the proxying of requests to the correct servers including load balancing.
+* Scheduler : manages the load and unload of models, pipelines, explainers and experiments.
+* Pipeline gateway : handles REST/gRPC calls to pipelines.
+* Dataflow engine : handles the flow of data between components in a pipeline.
+* Model gateway : handles the flow of data from models to inference requests on servers and passes on the responses.
+* Agent : manages the loading and unloading of models on a server and access to the server over REST/gRPC.
+* Envoy : manages the proxying of requests to the correct servers including load balancing.
 
 All the above are Kubernetes agnostic and can run locally, e.g. on Docker Compose.
 
@@ -36,17 +36,10 @@ By focusing on the data we allow users to join various flows together using stre
 ![joins](../images/joins.png)
 
 We support several types of joins:
- * _inner joins_, where all inputs need to be present for a transaction to join the tensors passed through the Pipeline;
- * _outer joins_, where only a subset needs to be available during the join window
- * _triggers_, in which data flows need to wait until records on one or more trigger data flows appear. The data in these triggers is not passed onwards from the join.
+* _inner joins_, where all inputs need to be present for a transaction to join the tensors passed through the Pipeline;
+* _outer joins_, where only a subset needs to be available during the join window
+* _triggers_, in which data flows need to wait until records on one or more trigger data flows appear. The data in these triggers is not passed onwards from the join.
 
 These techniques allow users to create complex pipeline flows of data between machine learning components.
 
 More discussion on the data flow view of machine learning and its effect on v2 design can be found [here](dataflow.md).
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-dataflow.md
-```
\ No newline at end of file
diff --git a/docs/cli/docs/seldon.md b/docs/cli/docs/seldon.md
deleted file mode 100644
index 3cecdf8d54..0000000000
--- a/docs/cli/docs/seldon.md
+++ /dev/null
@@ -1,18 +0,0 @@
-## seldon
-
-
-
-### Options
-
-```
-  -h, --help   help for seldon
-```
-
-### SEE ALSO
-
-* [seldon config](seldon_config.md)	 - manage configs
-* [seldon experiment](seldon_experiment.md)	 - manage experiments
-* [seldon model](seldon_model.md)	 - manage models
-* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
-* [seldon server](seldon_server.md)	 - manage servers
-
diff --git a/docs/cli/docs/seldon_config.md b/docs/cli/docs/seldon_config.md
deleted file mode 100644
index 2501fd7c66..0000000000
--- a/docs/cli/docs/seldon_config.md
+++ /dev/null
@@ -1,27 +0,0 @@
-## seldon config
-
-manage configs
-
-### Synopsis
-
-Manage and activate configuration files for the CLI
-
-```
-seldon config <subcomand> [flags]
-```
-
-### Options
-
-```
-  -h, --help   help for config
-```
-
-### SEE ALSO
-
-* [seldon](seldon.md)	 - 
-* [seldon config activate](seldon_config_activate.md)	 - activate config
-* [seldon config add](seldon_config_add.md)	 - add config
-* [seldon config deactivate](seldon_config_deactivate.md)	 - deactivate config
-* [seldon config list](seldon_config_list.md)	 - list configs
-* [seldon config remove](seldon_config_remove.md)	 - remove config
-
diff --git a/docs/cli/docs/seldon_config_activate.md b/docs/cli/docs/seldon_config_activate.md
deleted file mode 100644
index fd911fe2cb..0000000000
--- a/docs/cli/docs/seldon_config_activate.md
+++ /dev/null
@@ -1,22 +0,0 @@
-## seldon config activate
-
-activate config
-
-### Synopsis
-
-activate config
-
-```
-seldon config activate [flags]
-```
-
-### Options
-
-```
-  -h, --help   help for activate
-```
-
-### SEE ALSO
-
-* [seldon config](seldon_config.md)	 - manage configs
-
diff --git a/docs/cli/docs/seldon_config_add.md b/docs/cli/docs/seldon_config_add.md
deleted file mode 100644
index 3ea6f7036b..0000000000
--- a/docs/cli/docs/seldon_config_add.md
+++ /dev/null
@@ -1,22 +0,0 @@
-## seldon config add
-
-add config
-
-### Synopsis
-
-add config
-
-```
-seldon config add [flags]
-```
-
-### Options
-
-```
-  -h, --help   help for add
-```
-
-### SEE ALSO
-
-* [seldon config](seldon_config.md)	 - manage configs
-
diff --git a/docs/cli/docs/seldon_config_deactivate.md b/docs/cli/docs/seldon_config_deactivate.md
deleted file mode 100644
index 55639bd4ae..0000000000
--- a/docs/cli/docs/seldon_config_deactivate.md
+++ /dev/null
@@ -1,22 +0,0 @@
-## seldon config deactivate
-
-deactivate config
-
-### Synopsis
-
-deactivate config
-
-```
-seldon config deactivate [flags]
-```
-
-### Options
-
-```
-  -h, --help   help for deactivate
-```
-
-### SEE ALSO
-
-* [seldon config](seldon_config.md)	 - manage configs
-
diff --git a/docs/cli/docs/seldon_config_list.md b/docs/cli/docs/seldon_config_list.md
deleted file mode 100644
index 30b17fe285..0000000000
--- a/docs/cli/docs/seldon_config_list.md
+++ /dev/null
@@ -1,22 +0,0 @@
-## seldon config list
-
-list configs
-
-### Synopsis
-
-list configs
-
-```
-seldon config list [flags]
-```
-
-### Options
-
-```
-  -h, --help   help for list
-```
-
-### SEE ALSO
-
-* [seldon config](seldon_config.md)	 - manage configs
-
diff --git a/docs/cli/docs/seldon_config_remove.md b/docs/cli/docs/seldon_config_remove.md
deleted file mode 100644
index 0ada8f04b8..0000000000
--- a/docs/cli/docs/seldon_config_remove.md
+++ /dev/null
@@ -1,22 +0,0 @@
-## seldon config remove
-
-remove config
-
-### Synopsis
-
-remove config
-
-```
-seldon config remove [flags]
-```
-
-### Options
-
-```
-  -h, --help   help for remove
-```
-
-### SEE ALSO
-
-* [seldon config](seldon_config.md)	 - manage configs
-
diff --git a/docs/cli/docs/seldon_experiment.md b/docs/cli/docs/seldon_experiment.md
deleted file mode 100644
index d56201c12e..0000000000
--- a/docs/cli/docs/seldon_experiment.md
+++ /dev/null
@@ -1,26 +0,0 @@
-## seldon experiment
-
-manage experiments
-
-### Synopsis
-
-experiments allow you to test models by splitting traffic between candidates.
-
-```
-seldon experiment <subcomand> [flags]
-```
-
-### Options
-
-```
-  -h, --help   help for experiment
-```
-
-### SEE ALSO
-
-* [seldon](seldon.md)	 - 
-* [seldon experiment list](seldon_experiment_list.md)	 - get list of experiments
-* [seldon experiment start](seldon_experiment_start.md)	 - start an experiment
-* [seldon experiment status](seldon_experiment_status.md)	 - get status for experiment
-* [seldon experiment stop](seldon_experiment_stop.md)	 - stop an experiment
-
diff --git a/docs/cli/docs/seldon_experiment_list.md b/docs/cli/docs/seldon_experiment_list.md
deleted file mode 100644
index bed319a9fe..0000000000
--- a/docs/cli/docs/seldon_experiment_list.md
+++ /dev/null
@@ -1,24 +0,0 @@
-## seldon experiment list
-
-get list of experiments
-
-### Synopsis
-
-get list of experiments and whether they are active
-
-```
-seldon experiment list [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for list
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-```
-
-### SEE ALSO
-
-* [seldon experiment](seldon_experiment.md)	 - manage experiments
-
diff --git a/docs/cli/docs/seldon_experiment_start.md b/docs/cli/docs/seldon_experiment_start.md
deleted file mode 100644
index 2cb1eedd93..0000000000
--- a/docs/cli/docs/seldon_experiment_start.md
+++ /dev/null
@@ -1,27 +0,0 @@
-## seldon experiment start
-
-start an experiment
-
-### Synopsis
-
-start an experiment
-
-```
-seldon experiment start [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -f, --file-path string        experiment manifest file (YAML)
-  -h, --help                    help for start
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-```
-
-### SEE ALSO
-
-* [seldon experiment](seldon_experiment.md)	 - manage experiments
-
diff --git a/docs/cli/docs/seldon_experiment_status.md b/docs/cli/docs/seldon_experiment_status.md
deleted file mode 100644
index c58e58575c..0000000000
--- a/docs/cli/docs/seldon_experiment_status.md
+++ /dev/null
@@ -1,27 +0,0 @@
-## seldon experiment status
-
-get status for experiment
-
-### Synopsis
-
-get status for experiment
-
-```
-seldon experiment status <experimentName> [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for status
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-  -w, --wait                    wait for experiment to be active
-```
-
-### SEE ALSO
-
-* [seldon experiment](seldon_experiment.md)	 - manage experiments
-
diff --git a/docs/cli/docs/seldon_experiment_stop.md b/docs/cli/docs/seldon_experiment_stop.md
deleted file mode 100644
index 6b0110221c..0000000000
--- a/docs/cli/docs/seldon_experiment_stop.md
+++ /dev/null
@@ -1,26 +0,0 @@
-## seldon experiment stop
-
-stop an experiment
-
-### Synopsis
-
-stop an experiment
-
-```
-seldon experiment stop <experimentName> [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for stop
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-```
-
-### SEE ALSO
-
-* [seldon experiment](seldon_experiment.md)	 - manage experiments
-
diff --git a/docs/cli/docs/seldon_model.md b/docs/cli/docs/seldon_model.md
deleted file mode 100644
index 669c4ea560..0000000000
--- a/docs/cli/docs/seldon_model.md
+++ /dev/null
@@ -1,28 +0,0 @@
-## seldon model
-
-manage models
-
-### Synopsis
-
-load and unload and get status for models
-
-```
-seldon model <subcomand> [flags]
-```
-
-### Options
-
-```
-  -h, --help   help for model
-```
-
-### SEE ALSO
-
-* [seldon](seldon.md)	 - 
-* [seldon model infer](seldon_model_infer.md)	 - run inference on a model
-* [seldon model list](seldon_model_list.md)	 - get list of models
-* [seldon model load](seldon_model_load.md)	 - load a model
-* [seldon model metadata](seldon_model_metadata.md)	 - get model metadata
-* [seldon model status](seldon_model_status.md)	 - get status for model
-* [seldon model unload](seldon_model_unload.md)	 - unload a model
-
diff --git a/docs/cli/docs/seldon_model_infer.md b/docs/cli/docs/seldon_model_infer.md
deleted file mode 100644
index 345546d522..0000000000
--- a/docs/cli/docs/seldon_model_infer.md
+++ /dev/null
@@ -1,33 +0,0 @@
-## seldon model infer
-
-run inference on a model
-
-### Synopsis
-
-call a model with a given input and get a prediction
-
-```
-seldon model infer <modelName> (data) [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -f, --file-path string        inference payload file
-      --header stringArray      add a header, e.g. key=value; use the flag multiple times to add more than one header
-  -h, --help                    help for infer
-      --inference-host string   seldon inference host (default "0.0.0.0:9000")
-      --inference-mode string   inference mode (rest or grpc) (default "rest")
-  -i, --iterations int          how many times to run inference (default 1)
-  -t, --seconds int             number of secs to run inference
-      --show-headers            show request and response headers
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-  -s, --sticky-session          use sticky session from last inference (only works with experiments)
-```
-
-### SEE ALSO
-
-* [seldon model](seldon_model.md)	 - manage models
-
diff --git a/docs/cli/docs/seldon_model_list.md b/docs/cli/docs/seldon_model_list.md
deleted file mode 100644
index fce34d5e92..0000000000
--- a/docs/cli/docs/seldon_model_list.md
+++ /dev/null
@@ -1,24 +0,0 @@
-## seldon model list
-
-get list of models
-
-### Synopsis
-
-get the list of all models with their status
-
-```
-seldon model list [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for list
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-```
-
-### SEE ALSO
-
-* [seldon model](seldon_model.md)	 - manage models
-
diff --git a/docs/cli/docs/seldon_model_load.md b/docs/cli/docs/seldon_model_load.md
deleted file mode 100644
index 6a2beaeea5..0000000000
--- a/docs/cli/docs/seldon_model_load.md
+++ /dev/null
@@ -1,27 +0,0 @@
-## seldon model load
-
-load a model
-
-### Synopsis
-
-load a model
-
-```
-seldon model load [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -f, --file-path string        model manifest file (YAML)
-  -h, --help                    help for load
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-```
-
-### SEE ALSO
-
-* [seldon model](seldon_model.md)	 - manage models
-
diff --git a/docs/cli/docs/seldon_model_metadata.md b/docs/cli/docs/seldon_model_metadata.md
deleted file mode 100644
index b96688089e..0000000000
--- a/docs/cli/docs/seldon_model_metadata.md
+++ /dev/null
@@ -1,24 +0,0 @@
-## seldon model metadata
-
-get model metadata
-
-### Synopsis
-
-get model metadata
-
-```
-seldon model metadata <modelName> [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for metadata
-      --inference-host string   seldon inference host (default "0.0.0.0:9000")
-```
-
-### SEE ALSO
-
-* [seldon model](seldon_model.md)	 - manage models
-
diff --git a/docs/cli/docs/seldon_model_status.md b/docs/cli/docs/seldon_model_status.md
deleted file mode 100644
index abf47c1342..0000000000
--- a/docs/cli/docs/seldon_model_status.md
+++ /dev/null
@@ -1,27 +0,0 @@
-## seldon model status
-
-get status for model
-
-### Synopsis
-
-get the status for a model
-
-```
-seldon model status <modelName> [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for status
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-  -w, --wait string             model wait condition
-```
-
-### SEE ALSO
-
-* [seldon model](seldon_model.md)	 - manage models
-
diff --git a/docs/cli/docs/seldon_model_unload.md b/docs/cli/docs/seldon_model_unload.md
deleted file mode 100644
index 0add751f8a..0000000000
--- a/docs/cli/docs/seldon_model_unload.md
+++ /dev/null
@@ -1,26 +0,0 @@
-## seldon model unload
-
-unload a model
-
-### Synopsis
-
-unload a model
-
-```
-seldon model unload <modelName> [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for unload
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-```
-
-### SEE ALSO
-
-* [seldon model](seldon_model.md)	 - manage models
-
diff --git a/docs/cli/docs/seldon_pipeline.md b/docs/cli/docs/seldon_pipeline.md
deleted file mode 100644
index cbe5c96125..0000000000
--- a/docs/cli/docs/seldon_pipeline.md
+++ /dev/null
@@ -1,28 +0,0 @@
-## seldon pipeline
-
-manage pipelines
-
-### Synopsis
-
-pipelines allow you to join models together into inference graphs.
-
-```
-seldon pipeline <subcomand> [flags]
-```
-
-### Options
-
-```
-  -h, --help   help for pipeline
-```
-
-### SEE ALSO
-
-* [seldon](seldon.md)	 - 
-* [seldon pipeline infer](seldon_pipeline_infer.md)	 - run inference on a pipeline
-* [seldon pipeline inspect](seldon_pipeline_inspect.md)	 - inspect data in a pipeline
-* [seldon pipeline list](seldon_pipeline_list.md)	 - list pipelines
-* [seldon pipeline load](seldon_pipeline_load.md)	 - load a pipeline
-* [seldon pipeline status](seldon_pipeline_status.md)	 - status of a pipeline
-* [seldon pipeline unload](seldon_pipeline_unload.md)	 - unload a pipeline
-
diff --git a/docs/cli/docs/seldon_pipeline_infer.md b/docs/cli/docs/seldon_pipeline_infer.md
deleted file mode 100644
index cb5fdb784a..0000000000
--- a/docs/cli/docs/seldon_pipeline_infer.md
+++ /dev/null
@@ -1,33 +0,0 @@
-## seldon pipeline infer
-
-run inference on a pipeline
-
-### Synopsis
-
-call a pipeline with a given input and get a prediction
-
-```
-seldon pipeline infer <pipelineName> (data) [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -f, --file-path string        inference payload file
-      --header stringArray      add a header, e.g. key=value; use the flag multiple times to add more than one header
-  -h, --help                    help for infer
-      --inference-host string   seldon inference host (default "0.0.0.0:9000")
-      --inference-mode string   inference mode (rest or grpc) (default "rest")
-  -i, --iterations int          how many times to run inference (default 1)
-  -t, --seconds int             number of secs to run inference
-      --show-headers            show request and response headers
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-  -s, --sticky-session          use sticky session from last inference (only works with experiments)
-```
-
-### SEE ALSO
-
-* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
-
diff --git a/docs/cli/docs/seldon_pipeline_inspect.md b/docs/cli/docs/seldon_pipeline_inspect.md
deleted file mode 100644
index 19b5c0cf38..0000000000
--- a/docs/cli/docs/seldon_pipeline_inspect.md
+++ /dev/null
@@ -1,29 +0,0 @@
-## seldon pipeline inspect
-
-inspect data in a pipeline
-
-### Synopsis
-
-inspect data in a pipeline. Specify as pipelineName or pipelineName.(inputs|outputs) or pipelineName.stepName or pipelineName.stepName.(inputs|outputs) or pipelineName.stepName.(inputs|outputs).tensorName
-
-```
-seldon pipeline inspect <expression> [flags]
-```
-
-### Options
-
-```
-      --format string           inspect output format: raw or json. Default raw (default "raw")
-  -h, --help                    help for inspect
-      --kafka-broker string     kafka broker (default "0.0.0.0:9092")
-      --namespace string        Kubernetes namespace. Default default (default "default")
-      --offset int              message offset to start reading from, i.e. default 1 is the last message only (default 1)
-      --request-id string       request id to show, if not specified will be all messages in offset range
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-      --verbose                 display more details, such as headers
-```
-
-### SEE ALSO
-
-* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
-
diff --git a/docs/cli/docs/seldon_pipeline_list.md b/docs/cli/docs/seldon_pipeline_list.md
deleted file mode 100644
index be709b810c..0000000000
--- a/docs/cli/docs/seldon_pipeline_list.md
+++ /dev/null
@@ -1,24 +0,0 @@
-## seldon pipeline list
-
-list pipelines
-
-### Synopsis
-
-list pipelines
-
-```
-seldon pipeline list [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for list
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-```
-
-### SEE ALSO
-
-* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
-
diff --git a/docs/cli/docs/seldon_pipeline_load.md b/docs/cli/docs/seldon_pipeline_load.md
deleted file mode 100644
index c6d6b49398..0000000000
--- a/docs/cli/docs/seldon_pipeline_load.md
+++ /dev/null
@@ -1,27 +0,0 @@
-## seldon pipeline load
-
-load a pipeline
-
-### Synopsis
-
-load a pipeline
-
-```
-seldon pipeline load [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -f, --file-path string        pipeline manifest file (YAML)
-  -h, --help                    help for load
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-```
-
-### SEE ALSO
-
-* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
-
diff --git a/docs/cli/docs/seldon_pipeline_status.md b/docs/cli/docs/seldon_pipeline_status.md
deleted file mode 100644
index 0d861f255b..0000000000
--- a/docs/cli/docs/seldon_pipeline_status.md
+++ /dev/null
@@ -1,27 +0,0 @@
-## seldon pipeline status
-
-status of a pipeline
-
-### Synopsis
-
-status of a pipeline
-
-```
-seldon pipeline status <pipelineName> [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for status
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-  -w, --wait string             pipeline wait condition
-```
-
-### SEE ALSO
-
-* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
-
diff --git a/docs/cli/docs/seldon_pipeline_unload.md b/docs/cli/docs/seldon_pipeline_unload.md
deleted file mode 100644
index 1ab0c2d6a4..0000000000
--- a/docs/cli/docs/seldon_pipeline_unload.md
+++ /dev/null
@@ -1,26 +0,0 @@
-## seldon pipeline unload
-
-unload a pipeline
-
-### Synopsis
-
-unload a pipeline
-
-```
-seldon pipeline unload <pipelineName> [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for unload
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-```
-
-### SEE ALSO
-
-* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
-
diff --git a/docs/cli/docs/seldon_server.md b/docs/cli/docs/seldon_server.md
deleted file mode 100644
index 9ef3407200..0000000000
--- a/docs/cli/docs/seldon_server.md
+++ /dev/null
@@ -1,24 +0,0 @@
-## seldon server
-
-manage servers
-
-### Synopsis
-
-get status for servers
-
-```
-seldon server <subcomand> [flags]
-```
-
-### Options
-
-```
-  -h, --help   help for server
-```
-
-### SEE ALSO
-
-* [seldon](seldon.md)	 - 
-* [seldon server list](seldon_server_list.md)	 - get list of servers
-* [seldon server status](seldon_server_status.md)	 - get status for server
-
diff --git a/docs/cli/docs/seldon_server_list.md b/docs/cli/docs/seldon_server_list.md
deleted file mode 100644
index a335dd12cc..0000000000
--- a/docs/cli/docs/seldon_server_list.md
+++ /dev/null
@@ -1,24 +0,0 @@
-## seldon server list
-
-get list of servers
-
-### Synopsis
-
-get the available servers, their replicas and loaded models
-
-```
-seldon server list [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for list
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-```
-
-### SEE ALSO
-
-* [seldon server](seldon_server.md)	 - manage servers
-
diff --git a/docs/cli/docs/seldon_server_status.md b/docs/cli/docs/seldon_server_status.md
deleted file mode 100644
index aae6e5204a..0000000000
--- a/docs/cli/docs/seldon_server_status.md
+++ /dev/null
@@ -1,26 +0,0 @@
-## seldon server status
-
-get status for server
-
-### Synopsis
-
-get the status for a server
-
-```
-seldon server status [flags]
-```
-
-### Options
-
-```
-      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
-  -h, --help                    help for status
-      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
-  -r, --show-request            show request
-  -o, --show-response           show response (default true)
-```
-
-### SEE ALSO
-
-* [seldon server](seldon_server.md)	 - manage servers
-
diff --git a/docs/cli/index.md b/docs/cli/index.md
deleted file mode 100644
index 0ab673aab2..0000000000
--- a/docs/cli/index.md
+++ /dev/null
@@ -1,185 +0,0 @@
-# CLI
-
-Seldon provides a CLI to allow easy management and testing of Model, Experiment, and Pipeline resources.
-
-At present this needs to be built by hand from the operator folder.
-
-```
-make build-seldon     # for linux/macOS amd64
-make build-seldon-arm # for macOS ARM
-```
-
-Then place the `bin/seldon` executable in your path.
-
- * [cli docs](./docs/seldon.md)
-
-
-## Environment Variables and Services
-
-The CLI talks to 3 backend services on default endpoints:
-
- 1. The Seldon Core V2 Scheduler: default 0.0.0.0:9004
- 2. The Seldon Core inference endpoint: default 0.0.0.0:9000
- 3. The Seldon Kafka broker: default: 0.0.0.0:9092
-
-These defaults will be correct when Seldon Core v2 is installed locally as per the docs. For Kubernetes, you will need to change these by defining environment variables.
-
-```{literalinclude} ../../../../operator/cmd/seldon/cli/flags.go
-:language: golang
-:start-after: // Defaults
-:end-before: // Help statements
-```
-
-## Kubernetes Usage
-
-### Inference Service
-
-For a default install into the `seldon-mesh` namespace if you have exposed the inference `svc` as a loadbalancer you will find it at:
-
-```
-kubectl get svc seldon-mesh -n seldon-mesh -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
-```
-
-Use above IP at port 80:
-
-```
-export SELDON_INFER_HOST=<ip>:80
-```
-
-### Scheduler Service
-
-For a default install into the `seldon-mesh` namespace if you have exposed the scheduler svc as a loadbalancer you will find it at:
-
-```
-kubectl get svc seldon-scheduler -n seldon-mesh -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
-```
-
-Use above IP at port 9004:
-
-```
-export SELDON_SCHEDULE_HOST=<ip>:9004
-```
-
-### Kafka Broker
-
-The Kafka broker will depend on how you have installed Kafka into your Kubernetes cluster. Find the broker IP and use:
-
-```
-export SELDON_KAFKA_BROKER=<ip>:<port>
-```
-
-## Config file
-
-You can create a config file to manage connections to running seldon core v2 installs. The settings will override any environment variable settings.
-
-The definition is shown below:
-
-  ```{literalinclude} ../../../../operator/pkg/cli/config.go
-   :language: golang
-   :start-after: // start config struct
-   :end-before: // end config struct
-   ```
-
-An example below shows an example where we connect via TLS to the Seldon scheduler using our scheduler client certificate:
-
-```
-{
-    "controlplane":{
-	"schedulerHost": "seldon-scheduler.svc:9044",
-	"tls"; true,
-	"keyPath": "/home/certs/seldon-scheduler-client/tls.key",
-	"crtPath": "/home/certs/seldon-scheduler-client/tls.crt",
-	"caPath": "/home/certs/seldon-scheduler-client/ca.crt"
-    }
-}
-
-```
-
-To manage config files and activate them you can use the CLI command `seldon config` which has subcommands to list, add, remove, activate and decative configs.
-
-For example:
-
-```
-$ seldon config list
-config		path						active
-------		----						------
-kind-sasl	/home/work/seldon/cli/config-sasl.json		*
-
-$ seldon config deactivate kind-sasl
-
-$ seldon config list
-config		path						active
-------		----						------
-kind-sasl	/home/work/seldon/cli/config-sasl.json
-
-$ seldon config add gcp-scv2 ~/seldon/cli/gcp.json
-
-$ seldon config list
-config		path						active
-------		----						------
-gcp-scv2	/home/work/seldon/cli/gcp.json
-kind-sasl	/home/work/seldon/cli/config-sasl.json
-
-$ seldon config activate gcp-scv2
-
-$ seldon config list
-config		path						active
-------		----						------
-gcp-scv2	/home/work/seldon/cli/gcp.json	    		*
-kind-sasl	/home/work/seldon/cli/config-sasl.json
-
-$ seldon config list kind-sasl
-{
-  "controlplane": {
-    "schedulerHost": "172.19.255.2:9004"
-  },
-  "kafka": {
-    "bootstrap": "172.19.255.3:9093",
-    "caPath": "/home/work/gcp/scv2/certs/seldon-cluster-ca-cert/ca.crt"
-  }
-}
-```
-
-## TLS Certificates for Local Use
-
-For running with Kubernetes TLS connections on the control and/or data plane, certificates will need to be downloaded locally. We provide an example script which will download certificates from a Kubernetes secret and store them in a folder. It can be found in `hack/download-k8s-certs.sh` and takes 2 or 3 arguments:
-
-```
-./download-k8s-certs.sh <namespace> <secret> [<folder>]
-```
-
-e.g.:
-
-```
-./download-k8s-certs.sh seldon-mesh seldon-scheduler-client
-```
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-docs/seldon.md
-docs/seldon_config.md
-docs/seldon_model.md
-docs/seldon_experiment.md
-docs/seldon_pipeline.md
-docs/seldon_server.md
-docs/seldon_config_activate.md
-docs/seldon_config_add.md
-docs/seldon_config_deactivate.md
-docs/seldon_config_list.md
-docs/seldon_config_remove.md
-docs/seldon_model_infer.md
-docs/seldon_model_load.md
-docs/seldon_model_status.md
-docs/seldon_model_unload.md
-docs/seldon_experiment_start.md
-docs/seldon_experiment_status.md
-docs/seldon_experiment_stop.md
-docs/seldon_pipeline_infer.md
-docs/seldon_pipeline_load.md
-docs/seldon_pipeline_status.md
-docs/seldon_pipeline_unload.md
-docs/seldon_pipeline_inspect.md
-docs/seldon_server_status.md
-```
diff --git a/docs/conversational_memory.ipynb b/docs/conversational_memory.ipynb
deleted file mode 100644
index 5fea8d0edd..0000000000
--- a/docs/conversational_memory.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Conversational Memory Example\n",
-    "\n",
-    "This example demonstrates how to run locally a Conversational Memory Runtime MLServer instance. It \n",
-    "also illustrates the different ways it can be used. (Note: this example only showcases the Conversational \n",
-    "Memory as a stand-alone component, for a more integrated example check out [chatbot](/examples/chat_bot/chat_bot.md).)\n",
-    "\n",
-    "To get up and running we need to install the runtime. To do this create a virtual environment within \n",
-    "the base memory runtime directory:\n",
-    "\n",
-    "```sh\n",
-    "python3 -m venv venv\n",
-    "```\n",
-    "\n",
-    "and then install:\n",
-    "\n",
-    "```sh\n",
-    "pip install .\n",
-    "```\n",
-    "\n",
-    "We are now ready to create the `model-settings.json`. If this kind of file is new to you, make sure to \n",
-    "check out the [MLSerer docs](https://mlserver.readthedocs.io/en/latest/reference/model-settings.html) for \n",
-    "a detailed description on what they are and how they work."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "  \"name\": \"conversational_memory\",\n",
-      "  \"implementation\": \"mlserver_memory.ConversationalMemory\",\n",
-      "  \"parameters\": {\n",
-      "    \"extra\": {\n",
-      "      \"database_config\": {\n",
-      "        \"database\": \"filesys\"\n",
-      "      },\n",
-      "      \"memory_config\": {\n",
-      "        \"window_size\": 2,\n",
-      "        \"tensor_names\": [\"content\"]\n",
-      "      }\n",
-      "    }\n",
-      "  }\n",
-      "}\n"
-     ]
-    }
-   ],
-   "source": [
-    "!cat filesys/model-settings.json"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See the ConversationalMemory [specification](api-ref-cbms) for an explanation of the `memory_config` \n",
-    "parameters as well as the file system backend [specification](api-ref-fsc). \n",
-    "\n",
-    "\n",
-    "Now that the `model-settings.json` file is ready, we can start the runtime using:\n",
-    "\n",
-    "```sh\n",
-    "mlserver start filesys\n",
-    "```\n",
-    "\n",
-    "There are two intended workflows for using the memory runtime: memory updates and memory retrieval. In \n",
-    "the first one, the developer wishes to update the memory allocated to an LLM with new messages and, \n",
-    "in the second one, the developer wants to get messages from the memory store. When sending a request \n",
-    "to the runtime the developer must always include a `memory_id` tensor that the runtime uses to identify \n",
-    "the relevant part of a conversation needed. Regardless of whether the history of a conversation is updated, \n",
-    "the runtime will return a `window_size` object with the number of messages from the memory.\n",
-    "\n",
-    "![developer updates conversation history](./update_workflow.png)\n",
-    "\n",
-    "For a more concrete example of the first workflow, imagine we want to get the conversational history from a conversation about \n",
-    "some financial documents. A developer can do this by just sending the `memory_id` of a tensor corresponding to\n",
-    "the question `Why did Apple acquire X company?`. In doing so, the runtime will return the last `window_size` \n",
-    "number of messages it has stored, for example, `7` if that is the number of questions previously asked in the \n",
-    "same conversation.\n",
-    "\n",
-    "For the second workflow, suppose we have set the max number of messages in our memory to 10 and that we would like \n",
-    "to update the last one (`window_size - 1`) on behalf of our user. To do so we'd send a `memory_id` tensor as well \n",
-    "as a `content` tensor to the runtime to update the memory with the new message, read the last `window_size` messages \n",
-    "(including the new one) and return these. This is done to make the typical flow of a conversation easy to \n",
-    "implement with the runtime. The next examples will shed some light on how this process works.\n",
-    "\n",
-    "**Note**: If the developer wished they could store more than one tensor by updating the list of `tensor_names` \n",
-    "in the `model-settings.json` file. The only restriction is that if they specify multiple tensors, then they always \n",
-    "send an equal number of each to prevent the store from getting out of sync.\n",
-    "\n",
-    "**Note**: The `window_size` parameter is intended to be used to control the size of requests being sent \n",
-    "along the pipelines in which the memory runtime is deployed. It's not intended to be used to control the \n",
-    "number of tokens sent to the LLM. Token bounds can be configured in the [LLM inference runtime](../api/index.md).\n",
-    "\n",
-    "To get started, let's send a request with a `memory_id` and some content to initialize a conversational \n",
-    "store. We've chosen the `memory_id=6e1f1413` at random."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'id': 'dccd1dbd-4ec8-4172-a737-fb6480d20bc4',\n",
-      " 'model_name': 'conversational_memory',\n",
-      " 'outputs': [{'data': ['6e1f1413'],\n",
-      "              'datatype': 'BYTES',\n",
-      "              'name': 'memory_id',\n",
-      "              'parameters': {'content_type': 'str'},\n",
-      "              'shape': [1, 1]},\n",
-      "             {'data': ['Hello, how are you?'],\n",
-      "              'datatype': 'BYTES',\n",
-      "              'name': 'content',\n",
-      "              'parameters': {'content_type': 'str'},\n",
-      "              'shape': [1, 1]}],\n",
-      " 'parameters': {}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import requests\n",
-    "import pprint\n",
-    "\n",
-    "\n",
-    "inference_request = {\n",
-    "    \"inputs\": [\n",
-    "        {\n",
-    "            \"name\": \"memory_id\",\n",
-    "            \"shape\": [1],\n",
-    "            \"datatype\": \"BYTES\",\n",
-    "            \"data\": [\"6e1f1413\"],\n",
-    "            \"parameters\": {\"content_type\": \"str\"},\n",
-    "        },\n",
-    "        {\n",
-    "            \"name\": \"content\",\n",
-    "            \"shape\": [1],\n",
-    "            \"datatype\": \"BYTES\",\n",
-    "            \"data\": [\"Hello, how are you?\"],\n",
-    "            \"parameters\": {\"content_type\": \"str\"},\n",
-    "        }\n",
-    "    ]\n",
-    "}\n",
-    "\n",
-    "endpoint = f\"http://localhost:8080/v2/models/conversational_memory/infer\"\n",
-    "response_with_params = requests.post(\n",
-    "    endpoint,\n",
-    "    json=inference_request,\n",
-    ")\n",
-    "\n",
-    "pprint.pprint(response_with_params.json())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, the response returned contains the `memory_id` and the content. The message you've sent \n",
-    "has now been updated in the conversational store. We can now send a second request with the `memory_id` \n",
-    "alone in order to see what's contained in the memory:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'id': '618a7d1d-1a2b-40d5-9c43-28ab7769f4dc',\n",
-      " 'model_name': 'conversational_memory',\n",
-      " 'outputs': [{'data': ['6e1f1413'],\n",
-      "              'datatype': 'BYTES',\n",
-      "              'name': 'memory_id',\n",
-      "              'parameters': {'content_type': 'str'},\n",
-      "              'shape': [1, 1]},\n",
-      "             {'data': ['Hello, how are you?'],\n",
-      "              'datatype': 'BYTES',\n",
-      "              'name': 'content',\n",
-      "              'parameters': {'content_type': 'str'},\n",
-      "              'shape': [1, 1]}],\n",
-      " 'parameters': {}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "inference_request = {\n",
-    "    \"inputs\": [\n",
-    "        {\n",
-    "            \"name\": \"memory_id\",\n",
-    "            \"shape\": [1],\n",
-    "            \"datatype\": \"BYTES\",\n",
-    "            \"data\": [\"6e1f1413\"],\n",
-    "            \"parameters\": {\"content_type\": \"str\"},\n",
-    "        }\n",
-    "    ]\n",
-    "}\n",
-    "endpoint = f\"http://localhost:8080/v2/models/conversational_memory/infer\"\n",
-    "response_with_params = requests.post(\n",
-    "    endpoint,\n",
-    "    json=inference_request\n",
-    ")\n",
-    "\n",
-    "pprint.pprint(response_with_params.json())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As expected, we got back the previous message we just sent.\n",
-    "\n",
-    "Multiple messages can be sent to the memory runtime at one time. As an example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'id': 'fac725c0-0195-408e-a8fe-be4ad2b2ad2a',\n",
-      " 'model_name': 'conversational_memory',\n",
-      " 'outputs': [{'data': ['5P5pB2Lv'],\n",
-      "              'datatype': 'BYTES',\n",
-      "              'name': 'memory_id',\n",
-      "              'parameters': {'content_type': 'str'},\n",
-      "              'shape': [1, 1]},\n",
-      "             {'data': [\"Good, i'd like to buy a car.\",\n",
-      "                       'Really, what kind of car?'],\n",
-      "              'datatype': 'BYTES',\n",
-      "              'name': 'content',\n",
-      "              'parameters': {'content_type': 'str'},\n",
-      "              'shape': [2, 1]}],\n",
-      " 'parameters': {}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "inference_request = {\n",
-    "    \"inputs\": [\n",
-    "        {\n",
-    "            \"name\": \"memory_id\",\n",
-    "            \"shape\": [1],\n",
-    "            \"datatype\": \"BYTES\",\n",
-    "            \"data\": [\"5P5pB2Lv\"],\n",
-    "            \"parameters\": {\"content_type\": \"str\"},\n",
-    "        },\n",
-    "        {\n",
-    "            \"name\": \"content\",\n",
-    "            \"shape\": [1],\n",
-    "            \"datatype\": \"BYTES\",\n",
-    "            \"data\": [\n",
-    "                \"Hello, how are you?\",\n",
-    "                \"I'm well, how are you?\",\n",
-    "                \"Good, i'd like to buy a car.\",\n",
-    "                \"Really, what kind of car?\"\n",
-    "            ],\n",
-    "            \"parameters\": {\"content_type\": \"str\"},\n",
-    "        }\n",
-    "    ],\n",
-    "}\n",
-    "\n",
-    "endpoint = f\"http://localhost:8080/v2/models/conversational_memory/infer\"\n",
-    "response_with_params = requests.post(\n",
-    "    endpoint,\n",
-    "    json=inference_request,\n",
-    ")\n",
-    "\n",
-    "pprint.pprint(response_with_params.json())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above will write all the content tensors at the same time. This is useful for loading conversations \n",
-    "from a different store into the current conversational memory. Note that, we only received 2 messages back \n",
-    "out of the 4 we sent. This is because we've set the `window_size` parameter in the `model-settings.json` to \n",
-    "2. To change this we can either do so in the model settings themselves and then restart MLServer, or we \n",
-    "can pass a parameter in the inference request that sets the `window_size` value dynamically:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'id': '2e970796-4f75-4372-9cef-84c2afca854c',\n",
-      " 'model_name': 'conversational_memory',\n",
-      " 'outputs': [{'data': ['5P5pB2Lv'],\n",
-      "              'datatype': 'BYTES',\n",
-      "              'name': 'memory_id',\n",
-      "              'parameters': {'content_type': 'str'},\n",
-      "              'shape': [1, 1]},\n",
-      "             {'data': ['Hello, how are you?',\n",
-      "                       \"I'm well, how are you?\",\n",
-      "                       \"Good, i'd like to buy a car.\",\n",
-      "                       'Really, what kind of car?'],\n",
-      "              'datatype': 'BYTES',\n",
-      "              'name': 'content',\n",
-      "              'parameters': {'content_type': 'str'},\n",
-      "              'shape': [4, 1]}],\n",
-      " 'parameters': {}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "inference_request = {\n",
-    "    \"inputs\": [\n",
-    "        {\n",
-    "            \"name\": \"memory_id\",\n",
-    "            \"shape\": [1],\n",
-    "            \"datatype\": \"BYTES\",\n",
-    "            \"data\": [\"5P5pB2Lv\"],\n",
-    "            \"parameters\": {\"content_type\": \"str\"},\n",
-    "        },\n",
-    "    ],\n",
-    "    \"parameters\": {\n",
-    "        \"memory_parameters\": {\"window_size\": 5}\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "endpoint = f\"http://localhost:8080/v2/models/conversational_memory/infer\"\n",
-    "response_with_params = requests.post(\n",
-    "    endpoint,\n",
-    "    json=inference_request,\n",
-    ")\n",
-    "\n",
-    "pprint.pprint(response_with_params.json())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above showcases the memory runtime as a stand-alone server. The runtime itself is intended to be used \n",
-    "as a component within a Seldon Core V2 pipeline along with other components such as large language models \n",
-    "and other LLM-based tools. This [chatbot](/examples/chat_bot/chat_bot.md) example illustrates such a usecase."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/examples/index.md b/docs/examples/index.md
deleted file mode 100644
index c34c524c55..0000000000
--- a/docs/examples/index.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# Examples
-
-This section will provide some examples to allow operations with Seldon to be tested so you can run your own models, experiments, pipelines and explainers.
-
-## Getting Started Examples
-
- * [Local examples](local-examples.md)
- * [Kubernetes examples](k8s-examples.md)
-
-## Models
-
- * [Huggingface models](huggingface.md)
- * [Model zoo](model-zoo.md)
- * [Artifact versions](multi-version.md)
-
-## Pipelines
-
-* [Pipeline examples](pipeline-examples.md)
-* [Pipeline to pipeline examples](pipeline-to-pipeline.md)
-
-## Explainers
-
-* [Explainer examples](explainer-examples.md)
-
-## Servers
-
-* [Custom Servers](custom-servers.md)
-
-## Experiments
-
- * [Local experiments](local-experiments.md)
- * [Experiment version examples](experiment-versions.md)
-
-
-## Making Inference Requests
-
- * [Inference examples](inference.md)
- * [Tritonclient examples](tritonclient-examples.md)
- * [Batch Inference examples (kubernetes)](batch-examples-k8s.md)
- * [Batch Inference examples (local)](batch-examples-local.md)
-
-## Misc
-
- * [Checking Pipeline readiness](pipeline-ready-and-metadata.md)
-
-## Further Kubernetes Examples
-
- * [Kubernetes custerwide example](k8s-clusterwide.md)
-
-## Advanced Examples
-
- * [Huggingface speech to sentiment with explanations pipeline](speech-to-sentiment.md)
- * [Production image classifier with drift and outlier monitoring](cifar10.md)
- * [Production income classifier with drift, outlier and explanations](income.md)
- * [Conditional pipeline with pandas query model](pandasquery.md)
- * [Kubernetes Server with PVC](k8s-pvc.md)
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-local-examples.md
-k8s-examples.md
-huggingface.md
-model-zoo.md
-multi-version.md
-pipeline-examples.md
-pipeline-to-pipeline.md
-explainer-examples.md
-local-experiments.md
-experiment-versions.md
-inference.md
-tritonclient-examples.md
-pipeline-ready-and-metadata.md
-speech-to-sentiment.md
-cifar10.md
-income.md
-pandasquery.md
-k8s-pvc.md
-```
diff --git a/docs/examples/pipeline-to-pipeline.md b/docs/examples/pipeline-to-pipeline.md
index ebace88f81..de8692bacd 100644
--- a/docs/examples/pipeline-to-pipeline.md
+++ b/docs/examples/pipeline-to-pipeline.md
@@ -1,8 +1,1932 @@
-# Pipeline to Pipeline Examples
+# Seldon V2 Pipeline to Pipeline Examples
 
-Run these examples from the `samples` folder.
+This notebook illustrates a series of Pipelines that are joined together.
 
+### Models Used
+
+* `gs://seldon-models/triton/simple` an example Triton tensorflow model that takes 2
+ inputs INPUT0 and INPUT1 and adds them to produce OUTPUT0 and also subtracts `INPUT1`
+ from `INPUT0` to produce OUTPUT1. See [here](https://github.com/triton-inference-server/server/tree/main/docs/examples/model_repository/simple) for the original source code and license.
+* Other models can be found at https://github.com/SeldonIO/triton-python-examples
+
+### Pipeline pulling from one other Pipeline
+
+![pipeline-to-pipeline](../images/img_pipeline1.jpg)
+
+```bash
+cat ./models/tfsimple1.yaml
+cat ./models/tfsimple2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model load -f ./models/tfsimple2.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+seldon model status tfsimple2 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+cat ./pipelines/tfsimple.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple
+spec:
+  steps:
+    - name: tfsimple1
+  output:
+    steps:
+    - tfsimple1
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple.yaml
+```
+
+```bash
+seldon pipeline status tfsimple -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "tfsimple",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "tfsimple",
+        "uid": "cieq5dqi8ufs73flaj4g",
+        "version": 1,
+        "steps": [
+          {
+            "name": "tfsimple1"
+          }
+        ],
+        "output": {
+          "steps": [
+            "tfsimple1.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-29T15:26:48.074696631Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimple \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "",
+  "outputs": [
+    {
+      "data": [
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+        14,
+        16,
+        18,
+        20,
+        22,
+        24,
+        26,
+        28,
+        30,
+        32
+      ],
+      "name": "OUTPUT0",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    },
+    {
+      "data": [
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+      ],
+      "name": "OUTPUT1",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    }
+  ]
+}
+
+```
+
+```bash
+cat ./pipelines/tfsimple-extended.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-extended
+spec:
+  input:
+    externalInputs:
+      - tfsimple.outputs
+    tensorMap:
+      tfsimple.outputs.OUTPUT0: INPUT0
+      tfsimple.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple-extended.yaml
+```
+
+```bash
+seldon pipeline status tfsimple-extended -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "tfsimple-extended",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "tfsimple-extended",
+        "uid": "cieq5h2i8ufs73flaj50",
+        "version": 1,
+        "steps": [
+          {
+            "name": "tfsimple2"
+          }
+        ],
+        "output": {
+          "steps": [
+            "tfsimple2.outputs"
+          ]
+        },
+        "kubernetesMeta": {},
+        "input": {
+          "externalInputs": [
+            "tfsimple.outputs"
+          ],
+          "tensorMap": {
+            "tfsimple.outputs.OUTPUT0": "INPUT0",
+            "tfsimple.outputs.OUTPUT1": "INPUT1"
+          }
+        }
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-29T15:27:01.095715504Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimple --header x-request-id=test-id \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{
+	"model_name": "",
+	"outputs": [
+		{
+			"data": [
+				2,
+				4,
+				6,
+				8,
+				10,
+				12,
+				14,
+				16,
+				18,
+				20,
+				22,
+				24,
+				26,
+				28,
+				30,
+				32
+			],
+			"name": "OUTPUT0",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		},
+		{
+			"data": [
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0
+			],
+			"name": "OUTPUT1",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		}
+	]
+}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple
+```
+
+```
+seldon.default.model.tfsimple1.inputs	test-id	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}
+seldon.default.model.tfsimple1.outputs	test-id	{"modelName":"tfsimple1_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+seldon.default.pipeline.tfsimple.inputs	test-id	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}
+seldon.default.pipeline.tfsimple.outputs	test-id	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-extended
+```
+
+```
+seldon.default.model.tfsimple2.inputs	test-id	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.model.tfsimple2.outputs	test-id	{"modelName":"tfsimple2_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+seldon.default.pipeline.tfsimple-extended.inputs	test-id	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended.outputs	test-id	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+
+```
+
+```bash
+seldon pipeline unload tfsimple-extended
+seldon pipeline unload tfsimple
+```
+
+```bash
+seldon model unload tfsimple1
+seldon model unload tfsimple2
+```
+
+### Pipeline pulling from two other Pipelines
+
+![pipeline-to-pipeline](img_pipeline2.jpg)
+
+```bash
+cat ./models/tfsimple1.yaml
+cat ./models/tfsimple2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model load -f ./models/tfsimple2.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+seldon model status tfsimple2 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+cat ./pipelines/tfsimple.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple
+spec:
+  steps:
+    - name: tfsimple1
+  output:
+    steps:
+    - tfsimple1
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple.yaml
+```
+
+```bash
+seldon pipeline status tfsimple -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "tfsimple",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "tfsimple",
+        "uid": "cieq6aai8ufs73flaj5g",
+        "version": 1,
+        "steps": [
+          {
+            "name": "tfsimple1"
+          }
+        ],
+        "output": {
+          "steps": [
+            "tfsimple1.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-29T15:28:41.766794892Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimple \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "",
+  "outputs": [
+    {
+      "data": [
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+        14,
+        16,
+        18,
+        20,
+        22,
+        24,
+        26,
+        28,
+        30,
+        32
+      ],
+      "name": "OUTPUT0",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    },
+    {
+      "data": [
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+      ],
+      "name": "OUTPUT1",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    }
+  ]
+}
+
+```
+
+```bash
+cat ./pipelines/tfsimple-extended.yaml
+echo "---"
+cat ./pipelines/tfsimple-extended2.yaml
+echo "---"
+cat ./pipelines/tfsimple-combined.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-extended
+spec:
+  input:
+    externalInputs:
+      - tfsimple.outputs
+    tensorMap:
+      tfsimple.outputs.OUTPUT0: INPUT0
+      tfsimple.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-extended2
+spec:
+  input:
+    externalInputs:
+      - tfsimple.outputs
+    tensorMap:
+      tfsimple.outputs.OUTPUT0: INPUT0
+      tfsimple.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-combined
+spec:
+  input:
+    externalInputs:
+      - tfsimple-extended.outputs.OUTPUT0
+      - tfsimple-extended2.outputs.OUTPUT1
+    tensorMap:
+      tfsimple-extended.outputs.OUTPUT0: INPUT0
+      tfsimple-extended2.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple-extended.yaml
+seldon pipeline load -f ./pipelines/tfsimple-extended2.yaml
+seldon pipeline load -f ./pipelines/tfsimple-combined.yaml
+```
+
+```bash
+seldon pipeline status tfsimple-extended -w PipelineReady
+seldon pipeline status tfsimple-extended2 -w PipelineReady
+seldon pipeline status tfsimple-combined -w PipelineReady
+```
+
+```json
+{"pipelineName":"tfsimple-extended", "versions":[{"pipeline":{"name":"tfsimple-extended", "uid":"cieq6dai8ufs73flaj60", "version":1, "steps":[{"name":"tfsimple2"}], "output":{"steps":["tfsimple2.outputs"]}, "kubernetesMeta":{}, "input":{"externalInputs":["tfsimple.outputs"], "tensorMap":{"tfsimple.outputs.OUTPUT0":"INPUT0", "tfsimple.outputs.OUTPUT1":"INPUT1"}}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T15:28:53.963808852Z", "modelsReady":true}}]}
+{"pipelineName":"tfsimple-extended2", "versions":[{"pipeline":{"name":"tfsimple-extended2", "uid":"cieq6dai8ufs73flaj6g", "version":1, "steps":[{"name":"tfsimple2"}], "output":{"steps":["tfsimple2.outputs"]}, "kubernetesMeta":{}, "input":{"externalInputs":["tfsimple.outputs"], "tensorMap":{"tfsimple.outputs.OUTPUT0":"INPUT0", "tfsimple.outputs.OUTPUT1":"INPUT1"}}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T15:28:54.087670106Z", "modelsReady":true}}]}
+{"pipelineName":"tfsimple-combined", "versions":[{"pipeline":{"name":"tfsimple-combined", "uid":"cieq6dii8ufs73flaj70", "version":1, "steps":[{"name":"tfsimple2"}], "output":{"steps":["tfsimple2.outputs"]}, "kubernetesMeta":{}, "input":{"externalInputs":["tfsimple-extended.outputs.OUTPUT0", "tfsimple-extended2.outputs.OUTPUT1"], "tensorMap":{"tfsimple-extended.outputs.OUTPUT0":"INPUT0", "tfsimple-extended2.outputs.OUTPUT1":"INPUT1"}}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T15:28:54.330770841Z", "modelsReady":true}}]}
+
+```
+
+```bash
+seldon pipeline infer tfsimple --header x-request-id=test-id2 \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{
+	"model_name": "",
+	"outputs": [
+		{
+			"data": [
+				2,
+				4,
+				6,
+				8,
+				10,
+				12,
+				14,
+				16,
+				18,
+				20,
+				22,
+				24,
+				26,
+				28,
+				30,
+				32
+			],
+			"name": "OUTPUT0",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		},
+		{
+			"data": [
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0
+			],
+			"name": "OUTPUT1",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		}
+	]
+}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple
+```
+
+```
+seldon.default.model.tfsimple1.inputs	test-id2	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}
+seldon.default.model.tfsimple1.outputs	test-id2	{"modelName":"tfsimple1_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+seldon.default.pipeline.tfsimple.inputs	test-id2	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}
+seldon.default.pipeline.tfsimple.outputs	test-id2	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-extended --offset 2 --verbose
+```
+
+```
+seldon.default.model.tfsimple2.inputs	test-id2	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}		x-request-id=[test-id2]	x-forwarded-proto=[http]	x-seldon-route=[:tfsimple1_1:]	x-envoy-upstream-service-time=[1]	pipeline=[tfsimple-extended]	traceparent=[00-e438b82ad361ac2d5481bcfc494074d2-e468d06afdab8f52-01]	x-envoy-expected-rq-timeout-ms=[60000]
+seldon.default.model.tfsimple2.outputs	test-id2	{"modelName":"tfsimple2_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}		x-envoy-expected-rq-timeout-ms=[60000]	x-request-id=[test-id2]	x-forwarded-proto=[http]	x-seldon-route=[:tfsimple1_1: :tfsimple2_1:]	x-envoy-upstream-service-time=[1]	pipeline=[tfsimple-extended]	traceparent=[00-e438b82ad361ac2d5481bcfc494074d2-73bd1ee54a94d8fb-01]
+seldon.default.pipeline.tfsimple-extended.inputs	test-id	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}		pipeline=[tfsimple-extended]	traceparent=[00-3a6047efa647efc2b3fc5266ae023d23-fee12926788ce3b6-01]	x-envoy-expected-rq-timeout-ms=[60000]	x-request-id=[test-id]	x-forwarded-proto=[http]	x-envoy-upstream-service-time=[5]	x-seldon-route=[:tfsimple1_1:]
+seldon.default.pipeline.tfsimple-extended.inputs	test-id2	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}		x-forwarded-proto=[http]	x-seldon-route=[:tfsimple1_1:]	x-envoy-upstream-service-time=[1]	pipeline=[tfsimple-extended]	traceparent=[00-e438b82ad361ac2d5481bcfc494074d2-4df8459a992e0278-01]	x-envoy-expected-rq-timeout-ms=[60000]	x-request-id=[test-id2]
+seldon.default.pipeline.tfsimple-extended.outputs	test-id	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}		pipeline=[tfsimple-extended]	traceparent=[00-3a6047efa647efc2b3fc5266ae023d23-b2f899a739c5cafd-01]	x-envoy-expected-rq-timeout-ms=[60000]	x-request-id=[test-id]	x-forwarded-proto=[http]	x-envoy-upstream-service-time=[5]	x-seldon-route=[:tfsimple1_1: :tfsimple2_1:]
+seldon.default.pipeline.tfsimple-extended.outputs	test-id2	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}		x-envoy-upstream-service-time=[1]	pipeline=[tfsimple-extended]	traceparent=[00-e438b82ad361ac2d5481bcfc494074d2-dfa399143feec23d-01]	x-envoy-expected-rq-timeout-ms=[60000]	x-request-id=[test-id2]	x-forwarded-proto=[http]	x-seldon-route=[:tfsimple1_1: :tfsimple2_1:]
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-extended2 --offset 2
+```
+
+```
+seldon.default.pipeline.tfsimple-extended2.inputs	test-id3	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended2.inputs	test-id	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended2.outputs	test-id3	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+seldon.default.pipeline.tfsimple-extended2.outputs	test-id	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-combined
+```
+
+```
+seldon.default.model.tfsimple2.inputs	test-id	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA=="]}
+seldon.default.model.tfsimple2.outputs	test-id	{"modelName":"tfsimple2_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+seldon.default.pipeline.tfsimple-combined.inputs	test-id	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA=="]}
+seldon.default.pipeline.tfsimple-combined.outputs	test-id	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+
+```
+
+```bash
+seldon pipeline unload tfsimple-extended
+seldon pipeline unload tfsimple-extended2
+seldon pipeline unload tfsimple-combined
+seldon pipeline unload tfsimple
+```
+
+```bash
+seldon model unload tfsimple1
+seldon model unload tfsimple2
+```
+
+### Pipeline pullin from one pipeline with a trigger to another
+
+![pipeline-to-pipeline](img_pipeline3.jpg)
+
+```bash
+cat ./models/tfsimple1.yaml
+cat ./models/tfsimple2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model load -f ./models/tfsimple2.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+seldon model status tfsimple2 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+cat ./pipelines/tfsimple.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple
+spec:
+  steps:
+    - name: tfsimple1
+  output:
+    steps:
+    - tfsimple1
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple.yaml
+```
+
+```bash
+seldon pipeline status tfsimple -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "tfsimple",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "tfsimple",
+        "uid": "ciepkmii8ufs73flaj2g",
+        "version": 1,
+        "steps": [
+          {
+            "name": "tfsimple1"
+          }
+        ],
+        "output": {
+          "steps": [
+            "tfsimple1.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-29T14:51:06.822716088Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimple \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "",
+  "outputs": [
+    {
+      "data": [
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+        14,
+        16,
+        18,
+        20,
+        22,
+        24,
+        26,
+        28,
+        30,
+        32
+      ],
+      "name": "OUTPUT0",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    },
+    {
+      "data": [
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+      ],
+      "name": "OUTPUT1",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    }
+  ]
+}
+
+```
+
+```bash
+cat ./pipelines/tfsimple-extended.yaml
+echo "---"
+cat ./pipelines/tfsimple-extended2.yaml
+echo "---"
+cat ./pipelines/tfsimple-combined-trigger.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-extended
+spec:
+  input:
+    externalInputs:
+      - tfsimple.outputs
+    tensorMap:
+      tfsimple.outputs.OUTPUT0: INPUT0
+      tfsimple.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-extended2
+spec:
+  input:
+    externalInputs:
+      - tfsimple.outputs
+    tensorMap:
+      tfsimple.outputs.OUTPUT0: INPUT0
+      tfsimple.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-combined-trigger
+spec:
+  input:
+    externalInputs:
+      - tfsimple-extended.outputs
+    externalTriggers:
+      - tfsimple-extended2.outputs
+    tensorMap:
+      tfsimple-extended.outputs.OUTPUT0: INPUT0
+      tfsimple-extended.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple-extended.yaml
+seldon pipeline load -f ./pipelines/tfsimple-extended2.yaml
+seldon pipeline load -f ./pipelines/tfsimple-combined-trigger.yaml
+```
+
+```bash
+seldon pipeline status tfsimple-extended -w PipelineReady
+seldon pipeline status tfsimple-extended2 -w PipelineReady
+seldon pipeline status tfsimple-combined-trigger -w PipelineReady
+```
+
+```json
+{"pipelineName":"tfsimple-extended", "versions":[{"pipeline":{"name":"tfsimple-extended", "uid":"ciepkoii8ufs73flaj30", "version":1, "steps":[{"name":"tfsimple2"}], "output":{"steps":["tfsimple2.outputs"]}, "kubernetesMeta":{}, "input":{"externalInputs":["tfsimple.outputs"], "tensorMap":{"tfsimple.outputs.OUTPUT0":"INPUT0", "tfsimple.outputs.OUTPUT1":"INPUT1"}}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T14:51:14.937544974Z", "modelsReady":true}}]}
+{"pipelineName":"tfsimple-extended2", "versions":[{"pipeline":{"name":"tfsimple-extended2", "uid":"ciepkoii8ufs73flaj3g", "version":1, "steps":[{"name":"tfsimple2"}], "output":{"steps":["tfsimple2.outputs"]}, "kubernetesMeta":{}, "input":{"externalInputs":["tfsimple.outputs"], "tensorMap":{"tfsimple.outputs.OUTPUT0":"INPUT0", "tfsimple.outputs.OUTPUT1":"INPUT1"}}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T14:51:15.062097751Z", "modelsReady":true}}]}
+{"pipelineName":"tfsimple-combined-trigger", "versions":[{"pipeline":{"name":"tfsimple-combined-trigger", "uid":"ciepkoqi8ufs73flaj40", "version":1, "steps":[{"name":"tfsimple2"}], "output":{"steps":["tfsimple2.outputs"]}, "kubernetesMeta":{}, "input":{"externalInputs":["tfsimple-extended.outputs"], "externalTriggers":["tfsimple-extended2.outputs"], "tensorMap":{"tfsimple-extended.outputs.OUTPUT0":"INPUT0", "tfsimple-extended.outputs.OUTPUT1":"INPUT1"}}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T14:51:15.326170068Z", "modelsReady":true}}]}
+
+```
+
+```bash
+seldon pipeline infer tfsimple --header x-request-id=test-id3 \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{
+	"model_name": "",
+	"outputs": [
+		{
+			"data": [
+				2,
+				4,
+				6,
+				8,
+				10,
+				12,
+				14,
+				16,
+				18,
+				20,
+				22,
+				24,
+				26,
+				28,
+				30,
+				32
+			],
+			"name": "OUTPUT0",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		},
+		{
+			"data": [
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0
+			],
+			"name": "OUTPUT1",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		}
+	]
+}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple
+```
+
+```
+seldon.default.model.tfsimple1.inputs	test-id3	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}
+seldon.default.model.tfsimple1.outputs	test-id3	{"modelName":"tfsimple1_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+seldon.default.pipeline.tfsimple.inputs	test-id3	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}
+seldon.default.pipeline.tfsimple.outputs	test-id3	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-extended --offset 2
+```
+
+```
+seldon.default.model.tfsimple2.outputs	test-id3	{"modelName":"tfsimple2_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+seldon.default.pipeline.tfsimple-extended.inputs	test-id3	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended.inputs	test-id3	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended.outputs	test-id3	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+seldon.default.pipeline.tfsimple-extended.outputs	test-id3	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-extended2 --offset 2
+```
+
+```
+seldon.default.model.tfsimple2.inputs	test-id3	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended2.inputs	test-id3	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended2.inputs	test-id3	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended2.outputs	test-id3	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+seldon.default.pipeline.tfsimple-extended2.outputs	test-id3	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-combined-trigger
+```
+
+```
+seldon.default.model.tfsimple2.inputs	test-id3	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA=="]}
+seldon.default.model.tfsimple2.outputs	test-id3	{"modelName":"tfsimple2_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+seldon.default.pipeline.tfsimple-combined-trigger.inputs	test-id3	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA=="]}
+seldon.default.pipeline.tfsimple-combined-trigger.outputs	test-id3	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+
+```
+
+```bash
+seldon pipeline unload tfsimple-extended
+seldon pipeline unload tfsimple-extended2
+seldon pipeline unload tfsimple-combined-trigger
+seldon pipeline unload tfsimple
+```
+
+```bash
+seldon model unload tfsimple1
+seldon model unload tfsimple2
+```
+
+### Pipeline pulling from one other Pipeline Step
+
+![pipeline-to-pipeline](img_pipeline4.jpg)
+
+```bash
+cat ./models/tfsimple1.yaml
+cat ./models/tfsimple2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model load -f ./models/tfsimple2.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+seldon model status tfsimple2 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+cat ./pipelines/tfsimple.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple
+spec:
+  steps:
+    - name: tfsimple1
+  output:
+    steps:
+    - tfsimple1
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon pipeline status tfsimple -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "tfsimple",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "tfsimple",
+        "uid": "cg5g6m46dpcs73c4qhl0",
+        "version": 1,
+        "steps": [
+          {
+            "name": "tfsimple1"
+          }
+        ],
+        "output": {
+          "steps": [
+            "tfsimple1.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-03-10T10:15:52.515491456Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimple \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "",
+  "outputs": [
+    {
+      "data": [
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+        14,
+        16,
+        18,
+        20,
+        22,
+        24,
+        26,
+        28,
+        30,
+        32
+      ],
+      "name": "OUTPUT0",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    },
+    {
+      "data": [
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+      ],
+      "name": "OUTPUT1",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    }
+  ]
+}
+
+```
+
+```bash
+cat ./pipelines/tfsimple-extended-step.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-extended-step
+spec:
+  input:
+    externalInputs:
+      - tfsimple.step.tfsimple1.outputs
+    tensorMap:
+      tfsimple.step.tfsimple1.outputs.OUTPUT0: INPUT0
+      tfsimple.step.tfsimple1.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple-extended-step.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon pipeline status tfsimple-extended-step -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "tfsimple-extended-step",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "tfsimple-extended-step",
+        "uid": "cg5g6ns6dpcs73c4qhlg",
+        "version": 1,
+        "steps": [
+          {
+            "name": "tfsimple2"
+          }
+        ],
+        "output": {
+          "steps": [
+            "tfsimple2.outputs"
+          ]
+        },
+        "kubernetesMeta": {},
+        "input": {
+          "externalInputs": [
+            "tfsimple.step.tfsimple1.outputs"
+          ],
+          "tensorMap": {
+            "tfsimple.step.tfsimple1.outputs.OUTPUT0": "INPUT0",
+            "tfsimple.step.tfsimple1.outputs.OUTPUT1": "INPUT1"
+          }
+        }
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-03-10T10:15:59.634720740Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimple \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{
+	"model_name": "",
+	"outputs": [
+		{
+			"data": [
+				2,
+				4,
+				6,
+				8,
+				10,
+				12,
+				14,
+				16,
+				18,
+				20,
+				22,
+				24,
+				26,
+				28,
+				30,
+				32
+			],
+			"name": "OUTPUT0",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		},
+		{
+			"data": [
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0
+			],
+			"name": "OUTPUT1",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		}
+	]
+}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple --verbose
+```
+
+```
+seldon.default.model.tfsimple1.inputs	cg5g6ogfh5ss73a44vvg	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}}]}		pipeline=[tfsimple]	traceparent=[00-2c66ff815d920ad238365be52a4467f5-90824e4cb70c3242-01]	x-forwarded-proto=[http]	x-envoy-expected-rq-timeout-ms=[60000]	x-request-id=[cg5g6ogfh5ss73a44vvg]
+seldon.default.model.tfsimple1.outputs	cg5g6ogfh5ss73a44vvg	{"modelName":"tfsimple1_1","modelVersion":"1","outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}		x-request-id=[cg5g6ogfh5ss73a44vvg]	pipeline=[tfsimple]	x-envoy-upstream-service-time=[8]	x-seldon-route=[:tfsimple1_1:]	traceparent=[00-2c66ff815d920ad238365be52a4467f5-ca023a540fa463b3-01]	x-forwarded-proto=[http]	x-envoy-expected-rq-timeout-ms=[60000]
+seldon.default.pipeline.tfsimple.inputs	cg5g6ogfh5ss73a44vvg	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}}]}		pipeline=[tfsimple]	x-request-id=[cg5g6ogfh5ss73a44vvg]	traceparent=[00-2c66ff815d920ad238365be52a4467f5-843d6ce39292396d-01]	x-forwarded-proto=[http]	x-envoy-expected-rq-timeout-ms=[60000]
+seldon.default.pipeline.tfsimple.outputs	cg5g6ogfh5ss73a44vvg	{"outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}		x-envoy-expected-rq-timeout-ms=[60000]	x-request-id=[cg5g6ogfh5ss73a44vvg]	x-envoy-upstream-service-time=[8]	x-seldon-route=[:tfsimple1_1:]	pipeline=[tfsimple]	traceparent=[00-2c66ff815d920ad238365be52a4467f5-ee7527353e9fe5a2-01]	x-forwarded-proto=[http]
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-extended-step
+```
+
+```
+seldon.default.model.tfsimple2.inputs	cg5g6ogfh5ss73a44vvg	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}],"rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==","AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.model.tfsimple2.outputs	cg5g6ogfh5ss73a44vvg	{"modelName":"tfsimple2_1","modelVersion":"1","outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}]}
+seldon.default.pipeline.tfsimple-extended-step.inputs	cg5g6ogfh5ss73a44vvg	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}],"rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==","AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended-step.outputs	cg5g6ogfh5ss73a44vvg	{"outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}]}
+
+```
+
+```bash
+seldon pipeline unload tfsimple-extended-step
+seldon pipeline unload tfsimple
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model unload tfsimple1
+seldon model unload tfsimple2
+```
+
+```json
+{}
+{}
+
+```
+
+### Pipeline pulling from two other Pipeline steps from same model
+
+![pipeline-to-pipeline](img_pipeline5.jpg)
+
+```bash
+cat ./models/tfsimple1.yaml
+cat ./models/tfsimple2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model load -f ./models/tfsimple2.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+seldon model status tfsimple2 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+cat ./pipelines/tfsimple.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple
+spec:
+  steps:
+    - name: tfsimple1
+  output:
+    steps:
+    - tfsimple1
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon pipeline status tfsimple -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "tfsimple",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "tfsimple",
+        "uid": "cg5g6u46dpcs73c4qhm0",
+        "version": 1,
+        "steps": [
+          {
+            "name": "tfsimple1"
+          }
+        ],
+        "output": {
+          "steps": [
+            "tfsimple1.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-03-10T10:16:24.433333171Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimple \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "",
+  "outputs": [
+    {
+      "data": [
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+        14,
+        16,
+        18,
+        20,
+        22,
+        24,
+        26,
+        28,
+        30,
+        32
+      ],
+      "name": "OUTPUT0",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    },
+    {
+      "data": [
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+      ],
+      "name": "OUTPUT1",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    }
+  ]
+}
+
+```
+
+```bash
+cat ./pipelines/tfsimple-extended.yaml
+echo "---"
+cat ./pipelines/tfsimple-extended2.yaml
+echo "---"
+cat ./pipelines/tfsimple-combined-step.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-extended
+spec:
+  input:
+    externalInputs:
+      - tfsimple.outputs
+    tensorMap:
+      tfsimple.outputs.OUTPUT0: INPUT0
+      tfsimple.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-extended2
+spec:
+  input:
+    externalInputs:
+      - tfsimple.outputs
+    tensorMap:
+      tfsimple.outputs.OUTPUT0: INPUT0
+      tfsimple.outputs.OUTPUT1: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-combined-step
+spec:
+  input:
+    externalInputs:
+      - tfsimple-extended.step.tfsimple2.outputs.OUTPUT0
+      - tfsimple-extended2.step.tfsimple2.outputs.OUTPUT0
+    tensorMap:
+      tfsimple-extended.step.tfsimple2.outputs.OUTPUT0: INPUT0
+      tfsimple-extended2.step.tfsimple2.outputs.OUTPUT0: INPUT1
+  steps:
+    - name: tfsimple2
+  output:
+    steps:
+    - tfsimple2
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple-extended.yaml
+seldon pipeline load -f ./pipelines/tfsimple-extended2.yaml
+seldon pipeline load -f ./pipelines/tfsimple-combined-step.yaml
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+```bash
+seldon pipeline status tfsimple-extended -w PipelineReady
+seldon pipeline status tfsimple-extended2 -w PipelineReady
+seldon pipeline status tfsimple-combined-step -w PipelineReady
+```
+
+```json
+{"pipelineName":"tfsimple-extended","versions":[{"pipeline":{"name":"tfsimple-extended","uid":"cg5g7046dpcs73c4qhmg","version":1,"steps":[{"name":"tfsimple2"}],"output":{"steps":["tfsimple2.outputs"]},"kubernetesMeta":{},"input":{"externalInputs":["tfsimple.outputs"],"tensorMap":{"tfsimple.outputs.OUTPUT0":"INPUT0","tfsimple.outputs.OUTPUT1":"INPUT1"}}},"state":{"pipelineVersion":1,"status":"PipelineReady","reason":"created pipeline","lastChangeTimestamp":"2023-03-10T10:16:32.576588675Z","modelsReady":true}}]}
+{"pipelineName":"tfsimple-extended2","versions":[{"pipeline":{"name":"tfsimple-extended2","uid":"cg5g7046dpcs73c4qhn0","version":1,"steps":[{"name":"tfsimple2"}],"output":{"steps":["tfsimple2.outputs"]},"kubernetesMeta":{},"input":{"externalInputs":["tfsimple.outputs"],"tensorMap":{"tfsimple.outputs.OUTPUT0":"INPUT0","tfsimple.outputs.OUTPUT1":"INPUT1"}}},"state":{"pipelineVersion":1,"status":"PipelineReady","reason":"created pipeline","lastChangeTimestamp":"2023-03-10T10:16:32.711813099Z","modelsReady":true}}]}
+{"pipelineName":"tfsimple-combined-step","versions":[{"pipeline":{"name":"tfsimple-combined-step","uid":"cg5g7046dpcs73c4qhng","version":1,"steps":[{"name":"tfsimple2"}],"output":{"steps":["tfsimple2.outputs"]},"kubernetesMeta":{},"input":{"externalInputs":["tfsimple-extended.step.tfsimple2.outputs.OUTPUT0","tfsimple-extended2.step.tfsimple2.outputs.OUTPUT0"],"tensorMap":{"tfsimple-extended.step.tfsimple2.outputs.OUTPUT0":"INPUT0","tfsimple-extended2.step.tfsimple2.outputs.OUTPUT0":"INPUT1"}}},"state":{"pipelineVersion":1,"status":"PipelineReady","reason":"created pipeline","lastChangeTimestamp":"2023-03-10T10:16:33.017843490Z","modelsReady":true}}]}
+
+```
+
+```bash
+seldon pipeline infer tfsimple \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{
+	"model_name": "",
+	"outputs": [
+		{
+			"data": [
+				2,
+				4,
+				6,
+				8,
+				10,
+				12,
+				14,
+				16,
+				18,
+				20,
+				22,
+				24,
+				26,
+				28,
+				30,
+				32
+			],
+			"name": "OUTPUT0",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		},
+		{
+			"data": [
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0
+			],
+			"name": "OUTPUT1",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		}
+	]
+}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple
+```
+
+```
+seldon.default.model.tfsimple1.inputs	cg5g710fh5ss73a4500g	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}}]}
+seldon.default.model.tfsimple1.outputs	cg5g710fh5ss73a4500g	{"modelName":"tfsimple1_1","modelVersion":"1","outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}
+seldon.default.pipeline.tfsimple.inputs	cg5g710fh5ss73a4500g	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}}]}
+seldon.default.pipeline.tfsimple.outputs	cg5g710fh5ss73a4500g	{"outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-extended
+```
+
+```
+seldon.default.model.tfsimple2.inputs	cg5g710fh5ss73a4500g	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}],"rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==","AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA=="]}
+seldon.default.model.tfsimple2.outputs	cg5g710fh5ss73a4500g	{"modelName":"tfsimple2_1","modelVersion":"1","outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}
+seldon.default.pipeline.tfsimple-extended.inputs	cg5g710fh5ss73a4500g	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}],"rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==","AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended.outputs	cg5g710fh5ss73a4500g	{"outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}]}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-extended2
+```
+
+```
+seldon.default.model.tfsimple2.inputs	cg5g710fh5ss73a4500g	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}],"rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==","AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA=="]}
+seldon.default.model.tfsimple2.outputs	cg5g710fh5ss73a4500g	{"modelName":"tfsimple2_1","modelVersion":"1","outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}
+seldon.default.pipeline.tfsimple-extended2.inputs	cg5g710fh5ss73a4500g	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}],"rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==","AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.pipeline.tfsimple-extended2.outputs	cg5g710fh5ss73a4500g	{"outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}]}
+
+```
+
+```bash
+seldon pipeline inspect tfsimple-combined-step
+```
+
+```
+seldon.default.model.tfsimple2.inputs	cg5g710fh5ss73a4500g	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}],"rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==","AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA=="]}
+seldon.default.model.tfsimple2.outputs	cg5g710fh5ss73a4500g	{"modelName":"tfsimple2_1","modelVersion":"1","outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}
+seldon.default.pipeline.tfsimple-combined-step.inputs	cg5g710fh5ss73a4500g	{"inputs":[{"name":"INPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"INPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}],"rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==","AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA=="]}
+seldon.default.pipeline.tfsimple-combined-step.outputs	cg5g710fh5ss73a4500g	{"outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}
+
+```
+
+```bash
+seldon pipeline unload tfsimple-extended
+seldon pipeline unload tfsimple-extended2
+seldon pipeline unload tfsimple-combined-step
+seldon pipeline unload tfsimple
+```
+
+```json
+{}
+{}
+{}
+{}
+
+```
+
+```bash
+seldon model unload tfsimple1
+seldon model unload tfsimple2
+```
+
+```json
+{}
+{}
 
-```{include} ../../../../samples/pipeline-to-pipeline.md
-:relative-images:
 ```
diff --git a/docs/examples/speech-to-sentiment.md b/docs/examples/speech-to-sentiment.md
index a39dafd338..9d93b8a011 100644
--- a/docs/examples/speech-to-sentiment.md
+++ b/docs/examples/speech-to-sentiment.md
@@ -1,8 +1,525 @@
 # Huggingface speech to sentiment pipeline
 
-Run these examples from the `samples/examples/huggingface` folder.
+In this example we create a Pipeline to chain two huggingface models to allow speech to sentiment functionalityand add an explainer to understand the result.
 
+This example also illustrates how explainers can target pipelines to allow complex explanations flows.
 
-```{include} ../../../../samples/examples/huggingface/huggingface.md
-:relative-images:
+![architecture](../images/speech-to-sentiment.jpg)
+
+This example requires **ffmpeg** package to be installed locally. run `make install-requirements` for the Python dependencies.
+
+```python
+from ipywebrtc import AudioRecorder, CameraStream
+import torchaudio
+from IPython.display import Audio
+import base64
+import json
+import requests
+import os
+import time
+```
+
+Create a method to load speech from recorder; transform into mp3 and send at base64 data. On return of the result extract and show the text and sentiment.
+
+```python
+reqJson = json.loads('{"inputs":[{"name":"args", "parameters": {"content_type": "base64"}, "data":[],"datatype":"BYTES","shape":[1]}]}')
+url = "http://0.0.0.0:9000/v2/models/model/infer"
+def infer(resource: str):
+    with open('recording.webm', 'wb') as f:
+        f.write(recorder.audio.value)
+    !ffmpeg -i recording.webm -vn -ab 128k -ar 44100 file.mp3 -y -hide_banner -loglevel panic
+    with open("file.mp3", mode='rb') as file:
+        fileContent = file.read()
+        encoded = base64.b64encode(fileContent)
+        base64_message = encoded.decode('utf-8')
+    reqJson["inputs"][0]["data"] = [str(base64_message)]
+    headers = {"Content-Type": "application/json", "seldon-model": resource}
+    response_raw = requests.post(url, json=reqJson, headers=headers)
+    j = response_raw.json()
+    sentiment = j["outputs"][0]["data"][0]
+    text = j["outputs"][1]["data"][0]
+    reqId = response_raw.headers["x-request-id"]
+    print(reqId)
+    os.environ["REQUEST_ID"]=reqId
+    print(base64.b64decode(text))
+    print(base64.b64decode(sentiment))
+```
+
+### Load Huggingface Models
+
+We will load two Huggingface models for speech to text and text to sentiment.
+
+```bash
+cat ../../models/hf-whisper.yaml
+echo "---"
+cat ../../models/hf-sentiment.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: whisper
+spec:
+  storageUri: "gs://seldon-models/mlserver/huggingface/whisper"
+  requirements:
+  - huggingface
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment
+spec:
+  storageUri: "gs://seldon-models/mlserver/huggingface/sentiment"
+  requirements:
+  - huggingface
+
+```
+
+```bash
+seldon model load -f ../../models/hf-whisper.yaml
+seldon model load -f ../../models/hf-sentiment.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status whisper -w ModelAvailable | jq -M .
+seldon model status sentiment -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+### Create Explain Pipeline
+
+To allow Alibi-Explain to more easily explain the sentiment we will need:
+
+ * input and output transfrorms that take the Dict values input and output by the Huggingface sentiment model and turn them into values that Alibi-Explain can easily understand with the core values we want to explain and the outputs from the sentiment model.
+ * A separate Pipeline to allow us to join the sentiment model with the output transform
+
+These transform models are MLServer custom runtimes as shown below:
+
+```bash
+cat ./sentiment-input-transform/model.py | pygmentize
+```
+
+```
+# Copyright (c) 2024 Seldon Technologies Ltd.
+
+# Use of this software is governed BY
+# (1) the license included in the LICENSE file or
+# (2) if the license included in the LICENSE file is the Business Source License 1.1,
+# the Change License after the Change Date as each is defined in accordance with the LICENSE file.
+
+from mlserver import MLModel
+from mlserver.types import InferenceRequest, InferenceResponse, ResponseOutput
+from mlserver.codecs.string import StringRequestCodec
+from mlserver.logging import logger
+import json
+
+
+class SentimentInputTransformRuntime(MLModel):
+
+  async def load(self) -> bool:
+    return self.ready
+
+  async def predict(self, payload: InferenceRequest) -> InferenceResponse:
+    logger.info("payload (input-transform): %s",payload)
+    res_list = self.decode_request(payload, default_codec=StringRequestCodec)
+    logger.info("res list (input-transform): %s",res_list)
+    texts = []
+    for res in res_list:
+      logger.info("decoded data (input-transform): %s", res)
+      #text = json.loads(res)
+      text = res
+      texts.append(text["text"])
+
+    logger.info("transformed data (input-transform): %s", texts)
+    response =  StringRequestCodec.encode_response(
+      model_name="sentiment",
+      payload=texts
+    )
+    logger.info("response (input-transform): %s", response)
+    return response
+
+```
+
+```bash
+cat ./sentiment-output-transform/model.py | pygmentize
+```
+
+```py
+# Copyright (c) 2024 Seldon Technologies Ltd.
+
+# Use of this software is governed BY
+# (1) the license included in the LICENSE file or
+# (2) if the license included in the LICENSE file is the Business Source License 1.1,
+# the Change License after the Change Date as each is defined in accordance with the LICENSE file.
+
+from mlserver import MLModel
+from mlserver.types import InferenceRequest, InferenceResponse, ResponseOutput
+from mlserver.codecs import StringCodec, Base64Codec, NumpyRequestCodec
+from mlserver.codecs.string import StringRequestCodec
+from mlserver.codecs.numpy import NumpyRequestCodec
+import base64
+from mlserver.logging import logger
+import numpy as np
+import json
+
+class SentimentOutputTransformRuntime(MLModel):
+
+  async def load(self) -> bool:
+    return self.ready
+
+  async def predict(self, payload: InferenceRequest) -> InferenceResponse:
+    logger.info("payload (output-transform): %s",payload)
+    res_list = self.decode_request(payload, default_codec=StringRequestCodec)
+    logger.info("res list (output-transform): %s",res_list)
+    scores = []
+    for res in res_list:
+      logger.debug("decoded data (output transform): %s",res)
+      #sentiment = json.loads(res)
+      sentiment = res
+      if sentiment["label"] == "POSITIVE":
+        scores.append(1)
+      else:
+        scores.append(0)
+    response =  NumpyRequestCodec.encode_response(
+      model_name="sentiments",
+      payload=np.array(scores)
+    )
+    logger.info("response (output-transform): %s", response)
+    return response
+
+```
+
+```bash
+cat ../../models/hf-sentiment-input-transform.yaml
+echo "---"
+cat ../../models/hf-sentiment-output-transform.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment-input-transform
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/huggingface/mlserver_1.3.5/sentiment-input-transform"
+  requirements:
+  - mlserver
+  - python
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment-output-transform
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/huggingface/mlserver_1.3.5/sentiment-output-transform"
+  requirements:
+  - mlserver
+  - python
+
+```
+
+```bash
+seldon model load -f ../../models/hf-sentiment-input-transform.yaml
+seldon model load -f ../../models/hf-sentiment-output-transform.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status sentiment-input-transform -w ModelAvailable | jq -M .
+seldon model status sentiment-output-transform -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+cat ../../pipelines/sentiment-explain.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: sentiment-explain
+spec:
+  steps:
+    - name: sentiment
+      tensorMap:
+        sentiment-explain.inputs.predict: array_inputs
+    - name: sentiment-output-transform
+      inputs:
+      - sentiment
+  output:
+    steps:
+    - sentiment-output-transform
+
+```
+
+```bash
+seldon pipeline load -f ../../pipelines/sentiment-explain.yaml
+```
+
+```bash
+seldon pipeline status sentiment-explain -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "sentiment-explain",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "sentiment-explain",
+        "uid": "cihuo3svgtec73bj6ncg",
+        "version": 2,
+        "steps": [
+          {
+            "name": "sentiment",
+            "tensorMap": {
+              "sentiment-explain.inputs.predict": "array_inputs"
+            }
+          },
+          {
+            "name": "sentiment-output-transform",
+            "inputs": [
+              "sentiment.outputs"
+            ]
+          }
+        ],
+        "output": {
+          "steps": [
+            "sentiment-output-transform.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 2,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-07-04T09:53:19.250753906Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+cat ../../models/hf-sentiment-explainer.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/huggingface/speech-sentiment/explainer"
+  explainer:
+    type: anchor_text
+    pipelineRef: sentiment-explain
+
+```
+
+```bash
+seldon model load -f ../../models/hf-sentiment-explainer.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status sentiment-explainer -w ModelAvailable | jq -M .
+```
+
+```
+Error: Model wait status timeout
+
+```
+
+### Speech to Sentiment Pipeline with Explanation
+
+We can now create the final pipeline that will take speech and generate sentiment alongwith an explanation of why that sentiment was predicted.
+
+```bash
+cat ../../pipelines/speech-to-sentiment.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: speech-to-sentiment
+spec:
+  steps:
+    - name: whisper
+    - name: sentiment
+      inputs:
+      - whisper
+      tensorMap:
+        whisper.outputs.output: args
+    - name: sentiment-input-transform
+      inputs:
+      - whisper
+    - name: sentiment-explainer
+      inputs:
+      - sentiment-input-transform
+  output:
+    steps:
+    - sentiment
+    - whisper
+
+```
+
+```bash
+seldon pipeline load -f ../../pipelines/speech-to-sentiment.yaml
+```
+
+```bash
+seldon pipeline status speech-to-sentiment -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "speech-to-sentiment",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "speech-to-sentiment",
+        "uid": "cihuqb4vgtec73bj6nd0",
+        "version": 2,
+        "steps": [
+          {
+            "name": "sentiment",
+            "inputs": [
+              "whisper.outputs"
+            ],
+            "tensorMap": {
+              "whisper.outputs.output": "args"
+            }
+          },
+          {
+            "name": "sentiment-explainer",
+            "inputs": [
+              "sentiment-input-transform.outputs"
+            ]
+          },
+          {
+            "name": "sentiment-input-transform",
+            "inputs": [
+              "whisper.outputs"
+            ]
+          },
+          {
+            "name": "whisper"
+          }
+        ],
+        "output": {
+          "steps": [
+            "sentiment.outputs",
+            "whisper.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 2,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-07-04T09:58:04.277171896Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+### Test
+
+```python
+camera = CameraStream(constraints={'audio': True,'video':False})
+recorder = AudioRecorder(stream=camera)
+recorder
+```
+
+```
+AudioRecorder(audio=Audio(value=b'', format='webm'), stream=CameraStream(constraints={'audio': True, 'video': …
+
+```
+
+```python
+infer("speech-to-sentiment.pipeline")
+```
+
+```
+cihuqm8fh5ss73der5gg
+b'{"text": " Cambridge is a great place."}'
+b'{"label": "POSITIVE", "score": 0.9998548030853271}'
+
+```
+
+We will wait for the explanation which is run asynchronously to the functional output from the Pipeline above.
+
+```python
+while True:
+    base64Res = !seldon pipeline inspect speech-to-sentiment.sentiment-explainer.outputs --format json \
+          --request-id ${REQUEST_ID}
+    j = json.loads(base64Res[0])
+    if j["topics"][0]["msgs"] is not None:
+        expBase64 = j["topics"][0]["msgs"][0]["value"]["outputs"][0]["contents"]["bytesContents"][0]
+        expRaw = base64.b64decode(expBase64)
+        exp = json.loads(expRaw)
+        print("")
+        print("Explanation anchors:",exp["data"]["anchor"])
+        break
+    else:
+        print(".",end='')
+        time.sleep(1)
+
+```
+
+```
+......
+Explanation anchors: ['great']
+
+```
+
+### Cleanup
+
+```bash
+seldon pipeline unload speech-to-sentiment
+seldon pipeline unload sentiment-explain
+```
+
+```bash
+seldon model unload whisper
+seldon model unload sentiment
+seldon model unload sentiment-explainer
+seldon model unload sentiment-output-transform
+seldon model unload sentiment-input-transform
 ```
diff --git a/docs/examples/tritonclient-examples.md b/docs/examples/tritonclient-examples.md
index d7be635ad6..35810fb2e8 100644
--- a/docs/examples/tritonclient-examples.md
+++ b/docs/examples/tritonclient-examples.md
@@ -5,6 +5,517 @@ To install `tritonclient`
 pip install tritonclient[all]
 ```
 
-```{include} ../../../../samples/k8s-tritonclient.md
-:relative-images:
+## Tritonclient Examples with Seldon Core V2
+
+- Note: for compatibility of Tritonclient check [this issue](https://github.com/SeldonIO/seldon-core-v2/issues/471).
+
+```python
+import os
+os.environ["NAMESPACE"] = "seldon-mesh"
+```
+
+```python
+MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+MESH_IP=MESH_IP[0]
+import os
+os.environ['MESH_IP'] = MESH_IP
+MESH_IP
+```
+
+```
+'172.19.255.1'
+
+```
+
+## With MLServer
+
+- Note: binary data support in HTTP is blocked by [this issue](https://github.com/SeldonIO/MLServer/issues/324)
+
+### Deploy Model and Pipeline
+
+```bash
+cat models/sklearn-iris-gs.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
+```
+
+```bash
+cat pipelines/iris.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: iris-pipeline
+spec:
+  steps:
+    - name: iris
+  output:
+    steps:
+    - iris
+
+```
+
+```bash
+kubectl apply -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}
+kubectl apply -f pipelines/iris.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris created
+pipeline.mlops.seldon.io/iris-pipeline created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model iris -n ${NAMESPACE}
+kubectl wait --for condition=ready --timeout=300s pipelines iris-pipeline -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris condition met
+pipeline.mlops.seldon.io/iris-pipeline condition met
+
+```
+
+### HTTP Transport Protocol
+
+```python
+import tritonclient.http as httpclient
+import numpy as np
+
+http_triton_client = httpclient.InferenceServerClient(
+    url=f"{MESH_IP}:80",
+    verbose=False,
+)
+
+print("model ready:", http_triton_client.is_model_ready("iris"))
+print("model metadata:", http_triton_client.get_model_metadata("iris"))
+```
+
+```
+model ready: True
+model metadata: {'name': 'iris_1', 'versions': [], 'platform': '', 'inputs': [], 'outputs': [], 'parameters': {}}
+
+```
+
+```python
+# Against model
+
+binary_data = False
+
+inputs = [httpclient.InferInput("predict", (1, 4), "FP64")]
+inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype("float64"), binary_data=binary_data)
+
+outputs = [httpclient.InferRequestedOutput("predict", binary_data=binary_data)]
+
+result = http_triton_client.infer("iris", inputs, outputs=outputs)
+result.as_numpy("predict")
+```
+
+```
+array([[2]])
+
+```
+
+```python
+# Against pipeline
+
+binary_data = False
+
+inputs = [httpclient.InferInput("predict", (1, 4), "FP64")]
+inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype("float64"), binary_data=binary_data)
+
+outputs = [httpclient.InferRequestedOutput("predict", binary_data=binary_data)]
+
+result = http_triton_client.infer("iris-pipeline.pipeline", inputs, outputs=outputs)
+result.as_numpy("predict")
+```
+
+```
+array([[2]])
+
+```
+
+### GRPC Transport Protocol
+
+```python
+import tritonclient.grpc as grpcclient
+import numpy as np
+
+grpc_triton_client = grpcclient.InferenceServerClient(
+    url=f"{MESH_IP}:80",
+    verbose=False,
+)
+```
+
+```python
+model_name = "iris"
+headers = {"seldon-model": model_name}
+
+print("model ready:", grpc_triton_client.is_model_ready(model_name, headers=headers))
+print(grpc_triton_client.get_model_metadata(model_name, headers=headers))
+```
+
+```
+model ready: True
+name: "iris_1"
+
+```
+
+#### Against Model
+
+```python
+model_name = "iris"
+headers = {"seldon-model": model_name}
+
+inputs = [
+    grpcclient.InferInput("predict", (1, 4), "FP64"),
+]
+inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype("float64"))
+
+outputs = [grpcclient.InferRequestedOutput("predict")]
+
+result = grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)
+result.as_numpy("predict")
+```
+
+```
+array([[2]])
+
+```
+
+#### Against Pipeline
+
+```python
+model_name = "iris-pipeline.pipeline"
+headers = {"seldon-model": model_name}
+
+inputs = [
+    grpcclient.InferInput("predict", (1, 4), "FP64"),
+]
+inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype("float64"))
+
+outputs = [grpcclient.InferRequestedOutput("predict")]
+
+result = grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)
+result.as_numpy("predict")
+```
+
+```
+array([[2]])
+
+```
+
+## With Tritonserver
+
+- Note: binary data support in HTTP is blocked by https://github.com/SeldonIO/seldon-core-v2/issues/475
+
+### Deploy Model and Pipeline
+
+```bash
+cat models/tfsimple1.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+cat pipelines/tfsimple.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple
+spec:
+  steps:
+    - name: tfsimple1
+  output:
+    steps:
+    - tfsimple1
+
+```
+
+```bash
+kubectl apply -f models/tfsimple1.yaml -n ${NAMESPACE}
+kubectl apply -f pipelines/tfsimple.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/tfsimple1 created
+pipeline.mlops.seldon.io/tfsimple created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model tfsimple1 -n ${NAMESPACE}
+kubectl wait --for condition=ready --timeout=300s pipelines tfsimple -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/tfsimple1 condition met
+pipeline.mlops.seldon.io/tfsimple condition met
+
+```
+
+### HTTP Transport Protocol
+
+```python
+import tritonclient.http as httpclient
+import numpy as np
+
+http_triton_client = httpclient.InferenceServerClient(
+    url=f"{MESH_IP}:80",
+    verbose=False,
+)
+
+print("model ready:", http_triton_client.is_model_ready("iris"))
+print("model metadata:", http_triton_client.get_model_metadata("iris"))
+```
+
+```
+model ready: True
+model metadata: {'name': 'iris_1', 'versions': [], 'platform': '', 'inputs': [], 'outputs': [], 'parameters': {}}
+
+```
+
+```python
+# Against model (no binary data)
+
+binary_data = False
+
+inputs = [
+    httpclient.InferInput("INPUT0", (1, 16), "INT32"),
+    httpclient.InferInput("INPUT1", (1, 16), "INT32"),
+]
+inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"), binary_data=binary_data)
+inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"), binary_data=binary_data)
+
+outputs = [httpclient.InferRequestedOutput("OUTPUT0", binary_data=binary_data)]
+
+result = http_triton_client.infer("tfsimple1", inputs, outputs=outputs)
+result.as_numpy("OUTPUT0")
+```
+
+```
+array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],
+      dtype=int32)
+
+```
+
+```python
+# Against model (with binary data)
+
+binary_data = True
+
+inputs = [
+    httpclient.InferInput("INPUT0", (1, 16), "INT32"),
+    httpclient.InferInput("INPUT1", (1, 16), "INT32"),
+]
+inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"), binary_data=binary_data)
+inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"), binary_data=binary_data)
+
+outputs = [httpclient.InferRequestedOutput("OUTPUT0", binary_data=binary_data)]
+
+result = http_triton_client.infer("tfsimple1", inputs, outputs=outputs)
+result.as_numpy("OUTPUT0")
+```
+
+```
+array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],
+      dtype=int32)
+
+```
+
+```python
+# Against Pipeline (no binary data)
+
+binary_data = False
+
+inputs = [
+    httpclient.InferInput("INPUT0", (1, 16), "INT32"),
+    httpclient.InferInput("INPUT1", (1, 16), "INT32"),
+]
+inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"), binary_data=binary_data)
+inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"), binary_data=binary_data)
+
+outputs = [httpclient.InferRequestedOutput("OUTPUT0", binary_data=binary_data)]
+
+result = http_triton_client.infer("tfsimple.pipeline", inputs, outputs=outputs)
+result.as_numpy("OUTPUT0")
+```
+
+```
+array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],
+      dtype=int32)
+
+```
+
+```python
+## binary data does not work with http behind pipeline
+
+# import numpy as np
+
+# binary_data = True
+
+# inputs = [
+#     httpclient.InferInput("INPUT0", (1, 16), "INT32"),
+#     httpclient.InferInput("INPUT1", (1, 16), "INT32"),
+# ]
+# inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"), binary_data=binary_data)
+# inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"), binary_data=binary_data)
+
+# outputs = [httpclient.InferRequestedOutput("OUTPUT0", binary_data=binary_data)]
+
+# result = http_triton_client.infer("tfsimple.pipeline", inputs, outputs=outputs)
+# result.as_numpy("OUTPUT0")
+```
+
+### GRPC Transport Protocol
+
+```python
+import tritonclient.grpc as grpcclient
+import numpy as np
+
+grpc_triton_client = grpcclient.InferenceServerClient(
+    url=f"{MESH_IP}:80",
+    verbose=False,
+)
+```
+
+```python
+model_name = "tfsimple1"
+headers = {"seldon-model": model_name}
+
+print("model ready:", grpc_triton_client.is_model_ready(model_name, headers=headers))
+print(grpc_triton_client.get_model_metadata(model_name, headers=headers))
+```
+
+```
+model ready: True
+name: "tfsimple1_1"
+versions: "1"
+platform: "tensorflow_graphdef"
+inputs {
+  name: "INPUT0"
+  datatype: "INT32"
+  shape: -1
+  shape: 16
+}
+inputs {
+  name: "INPUT1"
+  datatype: "INT32"
+  shape: -1
+  shape: 16
+}
+outputs {
+  name: "OUTPUT0"
+  datatype: "INT32"
+  shape: -1
+  shape: 16
+}
+outputs {
+  name: "OUTPUT1"
+  datatype: "INT32"
+  shape: -1
+  shape: 16
+}
+
+```
+
+```python
+# Against Model
+
+model_name = "tfsimple1"
+headers = {"seldon-model": model_name}
+
+inputs = [
+    grpcclient.InferInput("INPUT0", (1, 16), "INT32"),
+    grpcclient.InferInput("INPUT1", (1, 16), "INT32"),
+]
+inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"))
+inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"))
+
+outputs = [grpcclient.InferRequestedOutput("OUTPUT0")]
+
+result = grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)
+result.as_numpy("OUTPUT0")
+```
+
+```
+array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],
+      dtype=int32)
+
+```
+
+```python
+# Against Pipeline
+
+model_name = "tfsimple.pipeline"
+headers = {"seldon-model": model_name}
+
+inputs = [
+    grpcclient.InferInput("INPUT0", (1, 16), "INT32"),
+    grpcclient.InferInput("INPUT1", (1, 16), "INT32"),
+]
+inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"))
+inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype("int32"))
+
+outputs = [grpcclient.InferRequestedOutput("OUTPUT0")]
+
+result = grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)
+result.as_numpy("OUTPUT0")
+```
+
+```
+array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],
+      dtype=int32)
+
+```
+
+## Cleanup
+
+```bash
+kubectl delete -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}
+kubectl delete -f pipelines/iris.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "iris" deleted
+pipeline.mlops.seldon.io "iris-pipeline" deleted
+
+```
+
+```bash
+kubectl delete -f models/tfsimple1.yaml -n ${NAMESPACE}
+kubectl delete -f pipelines/tfsimple.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "tfsimple1" deleted
+pipeline.mlops.seldon.io "tfsimple" deleted
+
 ```
diff --git a/docs/kubernetes/autoscaling/index.md b/docs/kubernetes/autoscaling/index.md
deleted file mode 100644
index cd1f7d0b33..0000000000
--- a/docs/kubernetes/autoscaling/index.md
+++ /dev/null
@@ -1,88 +0,0 @@
-# Autoscaling
-
-Autoscaling in Seldon applies to various concerns:
-
- * Inference servers autoscaling
- * Model autoscaling
- * Model memory overcommit
-
-## Inference servers autoscaling
-
-
-Autoscaling of servers can be done via `HorizontalPodAutoscaler` (HPA).
-
-HPA can be applied to any deployed `Server` resource.
-In this case HPA will manage the number of server replicas in the corresponding statefulset according to utilisation metrics  (e.g. CPU or memory).
-
-For example assuming that a `triton` server is deployed, then the user can attach an HPA based on cpu utilisation as follows:
-
-```
-kubectl autoscale server triton --cpu-percent=50 --min=1 --max=5
-```
-
-In this case, according to load, the system will add / remove server replicas to / from the `triton` statefulset. 
-
-It is worth considering the following points:
-
-- If HPA adds a new server replica, this new replica will be included in any **future** scheduling decisions.
-  In other words, when deploying a new model or rescheduling failed models this new replica will be considered.
-
-- If HPA deletes an existing server replica, the scheduler will first attempt to drain any loaded model on this server replica before the server replica gets actually deleted. This is achieved by leveraging a `PreStop` hook on the server replica pod that triggers a process before receiving the termination signal. This draining process is capped by `terminationGracePeriodSeconds`, which the user can set (default is 2 minutes).
-
-Therefore there should generally be minimal disruption to the inference workload during scaling.
-
-For more details on HPA check this [Kubernetes walk-through](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/).
-
-```{note}
-Autoscaling of inference servers via `seldon-scheduler` is under consideration for the roadmap. This allow for more fine grained interactions with model autoscaling.
-```
-
-## Model autoscaling
-
-As each model server can serve multiple models, models can scale across the available replicas of the server according to load.
-
-Autoscaling of models is enabled if at least `MinReplicas` or `MaxReplicas` is set in the model custom resource. Then according to load the system will scale the number of `Replicas` within this range. 
-
-For example the following model will be deployed at first with 1 replica and it can scale up according to load.
-```{literalinclude} ../../../../../samples/models/tfsimple_scaling.yaml
-:language: yaml
-```
-
-Note that model autoscaling will not attempt to add extra servers if the desired number of replicas cannot be currently fulfilled by the current provisioned number of servers. This is a process left to be done by server autoscaling.
-
-Additionally when the system autoscales, the initial model spec is not changed (e.g. the number of `Replicas`) and therefore the user cannot reset the number of replicas back to the initial specified value without an explicit change.
-
-If only `Replicas` is specified by the user, autoscaling of models is disabled and the system will have exactly the number of replicas of this model deployed regardless of inference load.
-
-### Architecture
-
-The model autoscaling architecture is designed such as each agent decides on which models to scale up / down according to some defined internal metrics and then sends a triggering message to the scheduler. The current metrics are collected from the data plane (inference path), representing a proxy on how loaded is a given model with fulfilling inference requests.
-
-![architecture](../../architecture/autoscaling_architecture.png)
-
-
-### Agent autoscaling stats collection
-
-#### Scale up logic:
-The main idea is that we keep the "lag" for each model. We define the "lag" as the difference between incoming and outgoing requests in a given time period. If the lag crosses a threshold, then we trigger a model scale up event. This threshold can be defined via `SELDON_MODEL_INFERENCE_LAG_THRESHOLD` inference server environment variable.
-
-#### Scale down logic:
-For now we keep things simple and we trigger model scale down events if a model has not been used for a number of seconds. This is defined in `SELDON_MODEL_INACTIVE_SECONDS_THRESHOLD` inference server environment variable.
-
-Each agent checks the above stats periodically and if any model hits the corresponding threshold, then the agent sends an event to the scheduler to request model scaling.
-
-How often this process executes can be defined via `SELDON_SCALING_STATS_PERIOD_SECONDS` inference server environment variable.
-
-### Scheduler autoscaling
-
-![state](../../architecture/scheduler_autoscaling_state_diagram.png)
-
-The scheduler will perform model autoscale if:
-* The model is stable (no state change in the last 5 minutes) and available.
-* The desired number of replicas is within range. Note we always have a least 1 replica of any deployed model and we rely on over commit to reduce the resources used further.
-* For scaling up, there is enough capacity for the new model replica.
-
-## Model memory overcommit
-
-Servers can hold more models than available memory if overcommit is swictched on (default yes). This allows under utilized models to be moved from inference server memory to allow for other models to take their place. Note that these evicted models are still registered and in the case future inference requests arrive, the system will reload the models back to memory before serving the requests. If traffic patterns for inference of models vary then this can allow more models than available server memory to be run on the system.
-
diff --git a/docs/kubernetes/index.md b/docs/kubernetes/index.md
deleted file mode 100644
index ba2ef5de51..0000000000
--- a/docs/kubernetes/index.md
+++ /dev/null
@@ -1,17 +0,0 @@
-# Kubernetes
-
-Kubernetes provides the core platform for production use. However, the architecture in agnostic to the underlying cluster technology as shown by the [local Docker installation](../getting-started/docker-installation/index.md).
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-kafka/index.md
-resources/index.md
-storage-secrets/index.md
-metrics/index.md
-tracing/index.md
-service-meshes/index.md
-scaling/index.md
-autoscaling/index.md
-```
diff --git a/docs/kubernetes/kafka/index.md b/docs/kubernetes/kafka/index.md
deleted file mode 100644
index 214f4a7781..0000000000
--- a/docs/kubernetes/kafka/index.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Kafka
-
-Seldon Core v2 requires [Kafka](https://kafka.apache.org/) to implement data-centric inference Pipelines.
-See our [architecture](../../architecture/index.md) documentation to learn more on how Seldon Core v2 uses Kafka.
-
-
-```{note}
-Kafka integration is required to enable data-centric inference pipelines feature.
-It is highly advice to configure Kafka integration to take full advantage of Seldon Core v2 features.
-```
-
-We list alternatives below.
-
-## Managed Kafka
-
-We recommend to use managed Kafka solution for production installation.
-This allow to take away all the complexity on running secure and scalable Kafka cluster away.
-
-We currently have tested and documented integration with following managed solutions:
-- Confluent Cloud (security: SASL/PLAIN)
-- Confluent Cloud (security: SASL/OAUTHBEARER)
-- Amazon MSK (security: mTLS)
-- Amazon MSK (security: SASL/SCRAM)
-- Azure Event Hub (security: SASL/PLAIN)
-
-See our [Kafka security](../../getting-started/kubernetes-installation/security/index.md#kafka) section for configuration examples.
-
-## Self Hosted Kafka
-
-```{include} ../../../../../kafka/strimzi/README.md
-:heading-offset: 2
-:relative-docs: ..
-```
diff --git a/docs/kubernetes/metrics/index.md b/docs/kubernetes/metrics/index.md
deleted file mode 100644
index 95314c5e90..0000000000
--- a/docs/kubernetes/metrics/index.md
+++ /dev/null
@@ -1,43 +0,0 @@
-# Metrics
-
-Metrics are exposed for scrapping by [Prometheus](https://prometheus.io/).
-
-## Example Installation
-
-We recommend to install [kube-prometheus](https://github.com/prometheus-operator/kube-prometheus#installing) that provides an all-in-one package with the [Prometheus operator](https://github.com/prometheus-operator/prometheus-operator).
-
-
-### RBAC
-
-You will need to modify the default RBAC installed by kube-prometheus as described [here](https://github.com/prometheus-operator/prometheus-operator/blob/main/Documentation/user-guides/getting-started.md#deploying-prometheus).
-
-From the `prometheus` folder in the project run:
-
-```bash
-kubectl apply -f rbac/cr.yaml
-```
-
-## Monitors
-
-We use a PodMonitor for scrapping agent metrics. The envoy and server monitors are there for completeness but not presently needed.
-
-```bash
-kubectl apply -f monitors
-```
-
-Includes:
-
- * Agent pod monitor. Monitors the metrics port of server inference pods.
- * Server pod monitor. Monitors the server-metrics port of inference server pods.
- * Envoy service monitor. Monitors the Envoy gateway proxies.
- * Pipeline gateway pod monitor. Monitors the metrics port of pipeline gateway pods.
-
-Pod monitors were chosen as ports for metrics are not exposed at service level as we do not have a top level service for server replicas but 1 headless service per replica. Future discussions could reference [this](https://github.com/prometheus-operator/prometheus-operator/issues/3119).
-
-## Example Grafana Dashboard
-
-Check [metrics](../metrics/index.md) for more information.
-
-## Reference
-
- * [Prometheus CRDs](https://github.com/prometheus-operator/prometheus-operator/blob/main/Documentation/api.md)
diff --git a/docs/kubernetes/resources/experiment/index.md b/docs/kubernetes/resources/experiment/index.md
deleted file mode 100644
index d13f409f5b..0000000000
--- a/docs/kubernetes/resources/experiment/index.md
+++ /dev/null
@@ -1,77 +0,0 @@
-# Experiment
-
-An Experiment defines a traffic split between Models or Pipelines. This allows new versions of models and pipelines to be tested.
-
-An experiment spec has three sections:
-
- * `candidates` (required) : a set of candidate models to split traffic.
- * `default` (optional) : an existing candidate who endpoint should be modified to split traffic as defined by the candidates.
-    * Each candidate has a traffic weight. The percentage of traffic will be this weight divided by the sum of traffic weights.
- * `mirror` (optional) : a single model to mirror traffic to the candidates. Responses from this model will not be returned to the caller.
-
-An example experiment with a `defaultModel` is shown below:
-
-```{literalinclude} ../../../../../../samples/experiments/ab-default-model.yaml 
-:language: yaml
-```
-
-This defines a split of 50% traffic between two models `iris` and `iris2`. In this case we want to expose this traffic split on the existing endpoint created for the `iris` model. This allows us to test new versions of models (in this case `iris2`) on an existing endpoint (in this case `iris`). The `default` key defines the model whose endpoint we want to change. The experiment will become active when both underplying models are in Ready status.
-
-An experiment over two separate models which exposes a new API endpoint is shown below:
-
-```{literalinclude} ../../../../../../samples/experiments/ab.yaml 
-:language: yaml
-```
-
-To call the endpoint add the header `seldon-model: <experiment-name>.experiment` in this case: `seldon-model: experiment-iris.experiment`. For example with curl:
-
-```bash
-curl http://${MESH_IP}/v2/models/experiment-iris/infer \
-   -H "Content-Type: application/json" \
-   -H "seldon-model: experiment-iris.experiment" \
-   -d '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
-```
-
-For examples see the [local experiments notebook](../../../examples/local-experiments.md).
-
-## Pipeline Experiments
-
-Running an experiment between some pipelines is very similar. The difference is `resourceType: pipeline` needs to be defined and in this case the candidates or mirrors will refer to pipelines. An example is shown below:
-
-```{literalinclude} ../../../../../../samples/experiments/addmul10.yaml 
-:language: yaml
-```
-For an example see the [local experiments notebook](../../../examples/local-experiments.md).
-
-## Mirror Experiments
-
-A mirror can be added easily for model or pipeline experiments. An example model mirror experiment is shown below:
-
-```{literalinclude} ../../../../../../samples/experiments/sklearn-mirror.yaml 
-:language: yaml
-```
-For an example see the [local experiments notebook](../../../examples/local-experiments.md).
-
-An example pipeline mirror experiment is shown below:
-
-```{literalinclude} ../../../../../../samples/experiments/addmul10-mirror.yaml 
-:language: yaml
-```
-For an example see the [local experiments notebook](../../../examples/local-experiments.md).
-
-
-## Sticky Sessions
-
-To allow cohorts to get consistent views in an experiment each inference request passes back a response header `x-seldon-route` which can be passed in future requests to an experiment to bypass the random traffic splits and get a prediction from the sequence of models and pipelines used in the initial request.
-
-Note: you must pass the normal `seldon-model` header along with the `x-seldon-route` header.
-
-This is illustrated in the [local experiments notebook](../../../examples/local-experiments.md).
-
-Caveats:
-
-  * Note the models used will be the same but not necessarily the same replica instances. This means at present this will not work for stateful models that need to go to the same model replica instance.
-
-## Service Meshes
-
-As an alternative you can choose to run experiments at the service mesh level if you use one of the popular service meshes that allow header based routing in traffic splits. For further discussion see [here](../../service-meshes/index.md).
diff --git a/docs/kubernetes/resources/index.md b/docs/kubernetes/resources/index.md
deleted file mode 100644
index 823e2d6cc7..0000000000
--- a/docs/kubernetes/resources/index.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# Resources
-
-For Kubernetes usage we provide a set of custom resources for interacting with Seldon.
-
- * [SeldonRuntime](./seldonruntime/index.md) - for installing Seldon in a particular namespace.
- * [Servers](./server/index.md) - for deploying sets of replicas of core inference servers (MLServer or Triton). 
- * [Models](./model/index.md) - for deploying single machine learning models, custom transformation logic, drift detectors, outliers detectors and explainers.
- * [Experiments](./experiment/index.md) - for testing new versions of models.
- * [Pipelines](./pipeline/index.md) - for connecting together flows of data between models.
-
-## Advanced Customization
-
-SeldonConfig and ServerConfig define the core installation configuration and machine learning inference server configuration for Seldon. Normally, you would not need to customize these but this may be required for your particular custom installation within your organisation.
-
- * [ServerConfigs](./serverconfig/index.md) - for defining new types of inference server that can be reference by a Server resource.
- * [SeldonConfig](./seldonconfig/index.md) - for defining how seldon is installed
-
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-seldonruntime/index.md
-model/index.md
-experiment/index.md
-pipeline/index.md
-server/index.md
-serverconfig/index.md
-seldonconfig/index.md
-```
-
diff --git a/docs/kubernetes/resources/model/index.md b/docs/kubernetes/resources/model/index.md
deleted file mode 100644
index 3cc8b0195a..0000000000
--- a/docs/kubernetes/resources/model/index.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Model
-
-A Model is the core atomic building block. It specifies a machine learning artifact that will be loaded onto one of the running Servers. A model could be a standard machine learning inference component such as
-
- * a Tensorflow model, PyTorch model or SKLearn model.
- * an inference transformation component such as a SKLearn pipeline or a piece of custom python logic.
- a monitoring component such as an outlier detector or drift detector.
- * An alibi-explain model explainer
-
-An example is shown below for a SKLearn model for iris classification:
-
-```{literalinclude} ../../../../../../samples/models/sklearn-iris-gs.yaml 
-:language: yaml
-```
-
-Its Kubernetes `spec` has two core requirements
-
- * A `storageUri` specifying the location of the artifact. This can be any rclone URI specification.
- * A `requirements` list which provides tags that need to be matched by the Server that can run this artifact type. By default when you install Seldon we provide a set of Servers that cover a range of artifact types.
-
-
diff --git a/docs/kubernetes/resources/pipeline/index.md b/docs/kubernetes/resources/pipeline/index.md
deleted file mode 100644
index 0080f62067..0000000000
--- a/docs/kubernetes/resources/pipeline/index.md
+++ /dev/null
@@ -1,24 +0,0 @@
-# Pipeline
-
-Pipelines allow one to connect flows of inference data transformed by `Model` components. A directed acyclic graph (DAG) of steps can be defined to join Models together. Each Model will need to be capable of receiving a V2 inference request and respond with a V2 inference response. An example Pipeline is shown below:
-
-```{literalinclude} ../../../../../../samples/pipelines/tfsimples-join.yaml
-:language: yaml
-```
-
-The `steps` list shows three models: `tfsimple1`, `tfsimple2` and `tfsimple3`. These three models each take two tensors called `INPUT0` and `INPUT1` of integers. The models produce two outputs `OUTPUT0` (the sum of the inputs) and `OUTPUT1` (subtraction of the second input from the first).
-
-`tfsimple1` and `tfsimple2` take as inputs the input to the Pipeline: the default assumption when no explicit inputs are defined. `tfsimple3` takes one V2 tensor input from each of the outputs of `tfsimple1` and `tfsimple2`. As the outputs of `tfsimple1` and `tfsimple2` have tensors named `OUTPUT0` and `OUTPUT1` their names need to be changed to respect the expected input tensors and this is done with a `tensorMap` component providing this tensor renaming. This is only required if your models can not be directly chained together.
-
-The output of the Pipeline is the output from the `tfsimple3` model.
-
-## Detailed Specification
-
-The full GoLang specification for a Pipeline is shown below:
-
-```{literalinclude} ../../../../../../operator/apis/mlops/v1alpha1/pipeline_types.go
-:language: golang
-:start-after: // PipelineSpec
-:end-before: // PipelineStatus
-```
-
diff --git a/docs/kubernetes/resources/seldonconfig/index.md b/docs/kubernetes/resources/seldonconfig/index.md
deleted file mode 100644
index 7102f4c73d..0000000000
--- a/docs/kubernetes/resources/seldonconfig/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Seldon Config
-
-```{note}
-This section is for advanced usage where you want to define how seldon is installed in each namespace.
-```
-
-The SeldonConfig resource defines the core installation components installed by Seldon. If you wish to install Seldon, you can use the [SeldonRuntime](../seldonruntime/index.md) resource which allows easy overriding of some parts defined in this specification. In general, we advise core DevOps to use the default SeldonConfig or customize it for their usage. Individual installation of Seldon can then use the SeldonRuntime with a few overrides for special customisation needed in that namespace.
-
-The specification contains core PodSpecs for each core component and a section for general configuration including the ConfigMaps that are created for the Agent (rclone defaults), Kafka and Tracing (open telemetry).
-
-
-```{literalinclude} ../../../../../../operator/apis/mlops/v1alpha1/seldonconfig_types.go
-:language: golang
-:start-after: // SeldonConfigSpec
-:end-before: // SeldonConfigStatus
-```
-Some of these values can be overridden on a per namespace basis via the SeldonRuntime resource.
-
-The default configuration is shown below.
-
-
-```{literalinclude} ../../../../../../operator/config/seldonconfigs/default.yaml
-:language: yaml
-```
-
diff --git a/docs/kubernetes/resources/seldonruntime/index.md b/docs/kubernetes/resources/seldonruntime/index.md
deleted file mode 100644
index 40ea17157f..0000000000
--- a/docs/kubernetes/resources/seldonruntime/index.md
+++ /dev/null
@@ -1,53 +0,0 @@
-# Seldon Runtime
-
-The SeldonRuntime resource is used to create an instance of Seldon installed in a particular namespace.
-
-```{literalinclude} ../../../../../../operator/apis/mlops/v1alpha1/seldonruntime_types.go
-:language: golang
-:start-after: // SeldonRuntimeSpec
-:end-before: // SeldonRuntimeStatus
-```
-
-For the definition of `SeldonConfiguration` above see the [SeldonConfig resource](../seldonconfig/index.md).
-
-The specification above contains overrides for the chosen `SeldonConfig`.
-To override the `PodSpec` for a given component, the `overrides` field needs to specify the component name and the `PodSpec` needs to specify the container name, along with fields to override.
-
-For instance, the following overrides the resource limits for `cpu` and `memory` in the `hodometer` component in the `seldon-mesh` namespace, while using values specified in the `seldonConfig` elsewhere (e.g. `default`).
-
-```yaml
-apiVersion: mlops.seldon.io/v1alpha1
-kind: SeldonRuntime
-metadata:
-  name: seldon
-  namespace: seldon-mesh
-spec:
-  overrides:
-  - name: hodometer
-    podSpec:
-      containers:
-      - name: hodometer
-        resources:
-          limits:
-            memory: 64Mi
-            cpu: 20m
-  seldonConfig: default
-```
-
-As a minimal use you should just define the `SeldonConfig` to use as a base for this install, for example to install in the `seldon-mesh` namespace with the `SeldonConfig` named `default`:
-
-```yaml
-apiVersion: mlops.seldon.io/v1alpha1
-kind: SeldonRuntime
-metadata:
-  name: seldon
-  namespace: seldon-mesh  
-spec:
-  seldonConfig: default
-```
-
-The helm chart `seldon-core-v2-runtime` allows easy creation of this resource and associated default Servers for an installation of Seldon in a particular namespace.
-
-## SeldonConfig Update Propagation
-
-When a [SeldonConfig](../seldonconfig/index.md) resource changes any SeldonRuntime resources that reference the changed SeldonConfig will also be updated immediately. If this behaviour is not desired you can set `spec.disableAutoUpdate` in the SeldonRuntime resource for it not be be updated immediately but only when it changes or any owned resource changes.
diff --git a/docs/kubernetes/resources/server/index.md b/docs/kubernetes/resources/server/index.md
deleted file mode 100644
index e86c7ae500..0000000000
--- a/docs/kubernetes/resources/server/index.md
+++ /dev/null
@@ -1,54 +0,0 @@
-# Server
-
-```{note}
-The default installation will provide two initial servers: one MLServer and one Triton. You only need to define additional servers for advanced use cases.
-```
-
-A Server defines an inference server onto which models will be placed for inference. By default on installation two server StatefulSets will be deployed one MlServer and one Triton. An example Server definition is shown below:
-
-```{literalinclude} ../../../../../../operator/config/servers/mlserver.yaml
-:language: yaml
-```
-
-The main requirement is a reference to a ServerConfig resource in this case `mlserver`.
-
-## Detailed Specs
-
-```{literalinclude} ../../../../../../operator/apis/mlops/v1alpha1/server_types.go
-:language: golang
-:start-after: // ServerSpec
-:end-before: // ServerStatus
-```
-
-## Custom Servers
-
-One can easily utilize a custom image with the existing ServerConfigs. For example, the following defines an MLServer server with a custom image:
-
-```{literalinclude} ../../../../../../samples/servers/custom-mlserver.yaml
-:language: yaml
-```
-
-This server can then be targeted by a particular model by specifying this server name when creating the model, for example:
-
-```{literalinclude} ../../../../../../samples/models/iris-custom-server.yaml
-:language: yaml
-```
-
-### Server with PVC
-
-One can also create a Server definition to add a persistent volume to your server. This can be used to allow models to be loaded directly from the persistent volume.
-
-```{literalinclude} ../../../../../../samples/examples/k8s-pvc/server.yaml
-:language: yaml
-```
-
-The server can be targeted by a model whose artifact is on the persistent volume as shown below.
-
-```{literalinclude} ../../../../../../samples/examples/k8s-pvc/iris.yaml
-:language: yaml
-```
-
-A fully worked example for this can be found [here](../../../examples/k8s-pvc.md).
-
-An alternative would be to create your own [ServerConfig](../serverconfig/index.md) for more complex use cases or you want to standardise the Server definition in one place.
-
diff --git a/docs/kubernetes/resources/serverconfig/index.md b/docs/kubernetes/resources/serverconfig/index.md
deleted file mode 100644
index b1250e51be..0000000000
--- a/docs/kubernetes/resources/serverconfig/index.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# Server Config
-
-```{note}
-This section is for advanced usage where you want to define new types of inference servers.
-```
-
-Server configurations define how to create an inference server. By default one is provided for Seldon MLServer and one for NVIDIA Triton Inference Server. Both these servers support the V2 inference protocol which is a requirement for all inference servers. They define how the Kubernetes ReplicaSet is defined which includes the Seldon Agent reverse proxy as well as an Rclone server for downloading artifacts for the server. The Kustomize ServerConfig for MlServer is shown below:
-
-```{literalinclude} ../../../../../../operator/config/serverconfigs/mlserver.yaml
-:language: yaml
-```
-
diff --git a/docs/kubernetes/scaling/index.md b/docs/kubernetes/scaling/index.md
deleted file mode 100644
index b92421904e..0000000000
--- a/docs/kubernetes/scaling/index.md
+++ /dev/null
@@ -1,61 +0,0 @@
-# Scaling
-
-## Models
-
-Models can be scaled by setting their replica count, e.g.
-
-```yaml
-apiVersion: mlops.seldon.io/v1alpha1
-kind: Model
-metadata:
-  name: iris
-spec:
-  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.2.3/iris-sklearn"
-  requirements:
-  - sklearn
-  memory: 100Ki
-  replicas: 3
-```
-
-Currently, the number of replicas will need not to exceed the replicas of the Server the model is scheduled to.
-
-## Servers
-
-Servers can be scaled by setting their replica count, e.g.
-
-```yaml
-apiVersion: mlops.seldon.io/v1alpha1
-kind: Server
-metadata:
-  name: mlserver
-  namespace: seldon
-spec:
-  replicas: 4
-  serverConfig: mlserver
-```
-
-Currently, models scheduled to a server can only scale up to the server replica count.
-
-## Internal Components
-
-Seldon Core v2 runs with several control and dataplane components. The scaling of these resources is discussed below:
-
-- Pipeline gateway.
-    - This pipeline gateway handles REST and gRPC synchronous requests to Pipelines. It is stateless and can be scaled based on traffic demand.
-- Model gateway.
-    - This component pulls model requests from Kafka and sends them to inference servers. It can be scaled up to the partition factor of your Kafka topics. At present we set a uniform partition factor for all topics in one installation of Seldon Core V2.
-- Dataflow engine.
-    - The dataflow engine runs KStream topologies to manage Pipelines. It can run as multiple replicas and the scheduler will balance Pipelines to run across it with a consistent hashing load balancer. Each Pipeline is managed up to the partition factor of Kafka (presently hardwired to one).
-- Scheduler.
-    - This manages the control plane operations. It is presently required to be one replica as it maintains internal state within a BadgerDB held on local persistent storage (stateful set in Kubernetes). Performance tests have shown this not to be a bottleneck at present.
-- Kubernetes Controller.
-    - The Kubernetes controller manages resources updates on the cluster which it passes on to the Scheduler. It is by default one replica but has the ability to scale.
-- Envoy
-    - Envoy replicas get their state from the scheduler for routing information and can be scaled as needed.
-
-
-### Future Enhancements
-
- * Allow configuration of partition factor for data plane consistent hashing load balancer.
- * Allow Model gateway and Pipeline gateway to use consistent hashing load balancer.
- * Consider control plane scaling options.
diff --git a/docs/kubernetes/service-meshes/ambassador/index.md b/docs/kubernetes/service-meshes/ambassador/index.md
deleted file mode 100644
index 35fd1a5371..0000000000
--- a/docs/kubernetes/service-meshes/ambassador/index.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Ambassador
-
-[Ambassador](https://www.getambassador.io/) provides service mesh and ingress products. Our examples here are based on the Emissary ingress.
-
-We will run through some examples as shown in the notebook `service-meshes/ambassador/ambassador.ipynb`
-
-## Single Model
-
- * Seldon Iris classifier model
- * Default Ambassador Host and Listener
- * Ambassador Mappings for REST and gRPC endpoints
-
-```{literalinclude} ../../../../../../service-meshes/ambassador/static/single-model.yaml 
-:language: yaml
-```
-
-## Traffic Split
-
-```{warning}
-Traffic splitting does not presently work due to this [issue](https://github.com/emissary-ingress/emissary/issues/4062). We recommend you use a Seldon Experiment instead.
-```
-
-Seldon provides an Experiment resource for service mesh agnostic traffic splitting but if you wish to control this via Ambassador and example is shown below to split traffic between two models.
-
-```{literalinclude} ../../../../../../service-meshes/ambassador/static/traffic-split.yaml 
-:language: yaml
-```
-
-
-
-```{include} ../../../../../../service-meshes/ambassador/README.md
-```
-
diff --git a/docs/kubernetes/service-meshes/index.md b/docs/kubernetes/service-meshes/index.md
deleted file mode 100644
index 7e7e834255..0000000000
--- a/docs/kubernetes/service-meshes/index.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# Service Meshes
-
-The Seldon models and pipelines are exposed via a single service endpoint in the install namespace called `seldon-mesh`. All models, pipelines and experiments can be reached via this single Service endpoint by setting appropriate headers on the inference REST/gRPC request. By this means Seldon is agnostic to any service mesh you may wish to use in your organisation. We provide some example integrations for some example service meshes below (alphabetical order):
-
- * [Ambassador](./ambassador/index.md)
- * [Istio](./istio/index.md)
- * [Traefik](./traefik/index.md)
-
-
-We welcome help to extend these to other service meshes.
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-ambassador/index.md
-istio/index.md
-traefik/index.md
-```
-
diff --git a/docs/kubernetes/service-meshes/istio/index.md b/docs/kubernetes/service-meshes/istio/index.md
deleted file mode 100644
index 464868ef66..0000000000
--- a/docs/kubernetes/service-meshes/istio/index.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# Istio
-
-[Istio](https://istio.io/) provides a service mesh and ingress solution.
-
-We will run through some examples as shown in the notebook `service-meshes/istio/istio.ipynb`
-
-## Single Model
-
- * A Seldon Iris Model
- * An istio Gateway
- * An instio VirtualService to expose REST and gRPC
-
-```{literalinclude} ../../../../../../service-meshes/istio/static/single-model.yaml 
-:language: yaml
-```
-
-## Traffic Split
-
- * Two Iris Models
- * An istio Gateway
- * An istio VirtualService with traffic split
-
-```{literalinclude} ../../../../../../service-meshes/istio/static/traffic-split.yaml 
-:language: yaml
-```
-
-```{include} ../../../../../../service-meshes/istio/README.md
-```
diff --git a/docs/kubernetes/service-meshes/traefik/index.md b/docs/kubernetes/service-meshes/traefik/index.md
deleted file mode 100644
index 472d6799eb..0000000000
--- a/docs/kubernetes/service-meshes/traefik/index.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Traefik
-
-[Traefik](https://doc.traefik.io/) provides a service mesh and ingress solution.
-
-We will run through some examples as shown in the notebook `service-meshes/traefik/traefik.ipynb`
-
-## Single Model
-
- * A Seldon Iris Model
- * Traefik Service
- * Traefik IngressRoute
- * Traefik Middleware for adding a header
-
-```{literalinclude} ../../../../../../service-meshes/traefik/static/single-model.yaml 
-:language: yaml
-```
-
-## Traffic Split
-
-```{warning}
-Traffic splitting does not presently work due to this [issue](https://github.com/emissary-ingress/emissary/issues/4062). We recommend you use a Seldon Experiment instead.
-```
-
-```{include} ../../../../../../service-meshes/traefik/README.md
-```
diff --git a/docs/kubernetes/storage-secrets/index.md b/docs/kubernetes/storage-secrets/index.md
deleted file mode 100644
index f2f464ff32..0000000000
--- a/docs/kubernetes/storage-secrets/index.md
+++ /dev/null
@@ -1,164 +0,0 @@
-# Storage Secrets
-
-Inference artifacts referenced by Models can be stored in any of the storage backends supported by [Rclone](https://rclone.org/).
-This includes local filesystems, AWS S3, and Google Cloud Storage (GCS), among others.
-Configuration is provided out-of-the-box for public GCS buckets, which enables the use of Seldon-provided models like in the below example:
-
-```{literalinclude} ../../../../../samples/models/sklearn-iris-gs.yaml 
-:language: yaml
-```
-
-This configuration is provided by the Kubernetes Secret `seldon-rclone-gs-public`.
-It is made available to Servers as a [preloaded secret](#preloaded-secrets).
-You can define and use your own storage configurations in exactly the same way.
-
-## Configuration Format
-
-To define a new storage configuration, you need the following details:
-* Remote name
-* Remote type
-* Provider parameters
-
-A _remote_ is what Rclone calls a storage location.
-The _type_ defines what protocol Rclone should use to talk to this remote.
-A _provider_ is a particular implementation for that storage type.
-Some storage types have multiple providers, such as `s3` having AWS S3 itself, MinIO, Ceph, and so on.
-
-The remote **name** is your choice.
-The prefix you use for models in `spec.storageUri` must be the same as this remote name.
-
-The remote type is one of the values [supported by Rclone](https://rclone.org/docs/).
-For example, for AWS S3 it is `s3` and for Dropbox it is `dropbox`.
-
-The provider parameters depend entirely on the remote _type_ and the specific _provider_ you are using.
-Please check the Rclone documentation for the appropriate provider.
-Note that Rclone docs for storage types call the parameters _properties_ and provide both _config_ and _env var_ formats--you need to use the _config_ format.
-For example, the GCS parameter `--gcs-client-id` described [here](https://rclone.org/googlecloudstorage/#gcs-client-id) should be used as `client_id`.
-
-For reference, this format is described in the [Rclone documentation](https://rclone.org/rc/#config-create).
-Note that we do not support the use of `opts` discussed in that section.
-
-## Kubernetes Secrets
-
-Kubernetes Secrets are used to store Rclone configurations, or _storage secrets_, for use by Servers.
-Each Secret should contain **exactly one** Rclone configuration.
-
-A Server can use storage secrets in one of two ways:
-* It can dynamically load a secret specified by a Model in its `.spec.secretName`
-* It can use global configurations made available via [preloaded secrets](#preloaded-secrets)
-
-The name of a Secret is entirely your choice, as is the name of the data key in that Secret.
-All that matters is that there is a single data key and that its value is in the format described above.
-
-```{note}
-It is possible to use preloaded secrets for some Models and dynamically loaded secrets for others.
-```
-
-### Preloaded Secrets
-
-Rather than Models always having to specify which secret to use, a Server can load storage secrets ahead of time.
-These can then be reused across many Models.
-
-When using a preloaded secret, the Model definition should leave `.spec.secretName` empty.
-The protocol prefix in `.spec.storageUri` still needs to match the remote name specified by a storage secret.
-
-The secrets to preload are named in a centralised ConfigMap called `seldon-agent`.
-This ConfigMap applies to _all_ Servers managed by the same SeldonRuntime.
-By default this ConfigMap only includes `seldon-rclone-gs-public`, but can be extended with your own secrets as shown below:
-
-```{literalinclude} ../../../../../samples/auth/agent.yaml
-:language: yaml
-```
-
-The easiest way to change this is to update your SeldonRuntime.
-* If your SeldonRuntime is configured using the `seldon-core-v2-runtime` Helm chart, the corresponding value is `config.agentConfig.rclone.configSecrets`.
-  This can be used as shown below:
-  ```yaml
-  config:
-    agentConfig:
-      rclone:
-        configSecrets:
-          - my-s3
-          - custom-gcs
-          - minio-in-cluster
-  ```
-* Otherwise, if your SeldonRuntime is configured directly, you can add secrets by setting `.spec.config.agentConfig.rclone.config_secrets`.
-  This can be used as follows:
-  ```yaml
-  apiVersion: mlops.seldon.io/v1alpha1
-  kind: SeldonRuntime
-  metadata:
-    name: seldon
-  spec:
-    seldonConfig: default
-    config:
-      agentConfig:
-        rclone:
-          config_secrets:
-            - my-s3
-            - custom-gcs
-            - minio-in-cluster
-    ...
-  ```
-
-## Examples
-
-`````{tabs}
-
-````{group-tab} S3 MinIO
-
-Assuming you have installed MinIO in the `minio-system` namespace, a corresponding secret could be:
-
-```{literalinclude} ../../../../../samples/auth/minio-secret.yaml
-:language: yaml
-```
-
-You can then reference this in a Model with `.spec.secretName`:
-
-```{literalinclude} ../../../../../samples/models/sklearn-iris-minio.yaml
-:language: yaml
-```
-````
-
-````{group-tab} Google Cloud Storage
-
-[GCS](https://rclone.org/googlecloudstorage/) can use [service accounts](https://cloud.google.com/iam/docs/service-accounts) for access.
-You can generate the credentials for a service account using the [gcloud CLI](https://cloud.google.com/sdk/gcloud/reference/iam/service-accounts/keys/create):
-
-```bash
-gcloud iam service-accounts keys create \
-  gcloud-application-credentials.json \
-  --iam-account [SERVICE-ACCOUNT--NAME]@[PROJECT-ID].iam.gserviceaccount.com
-```
-
-The contents of `gcloud-application-credentials.json` can be put into a secret:
-
-```yaml
-apiVersion: v1
-kind: Secret
-metadata:
-  name: gcs-bucket
-type: Opaque
-stringData:
-  gcs: |
-    type: gcs
-    name: gcs
-    parameters:
-      service_account_credentials: '<gcloud-application-credentials.json>'
-```
-
-You can then reference this in a Model with `.spec.secretName`:
-
-```yaml
-apiVersion: mlops.seldon.io/v1alpha1
-kind: Model
-metadata:
-  name: mymodel
-spec:
-  storageUri: "gcs://my-bucket/my-path/my-pytorch-model"
-  secretName: "gcs-bucket"
-  requirements:
-  - pytorch
-```
-````
-`````
diff --git a/docs/kubernetes/tracing/index.md b/docs/kubernetes/tracing/index.md
deleted file mode 100644
index 71a4886f3b..0000000000
--- a/docs/kubernetes/tracing/index.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# Tracing
-
-We support Open Telemetry tracing. By default all components will attempt to send OLTP events to `seldon-collector.seldon-mesh:4317` which will export to Jaeger at `simplest-collector.seldon-mesh:4317`.
-
-The components can be installed from the `tracing/k8s` folder. In future an Ansible playbook will be created. This installs a Open Telemetry collector and a simple Jaeger install with a service that can be port forwarded to at `simplest.seldon-mesh:16686`.
-
-An example Jaeger trace is show below:
-
-![trace](jaeger-trace.png)
-
diff --git a/docs/kubernetes/tracing/jaeger-trace.png b/docs/kubernetes/tracing/jaeger-trace.png
deleted file mode 100644
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diff --git a/docs/metrics/index.md b/docs/metrics/index.md
deleted file mode 100644
index e9a29dff18..0000000000
--- a/docs/metrics/index.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Metrics
-
-There are two kinds of metrics present in Seldon Core v2:
-* [operational metrics](./operational.md)
-* [usage metrics](./usage.md)
-
-Operational metrics describe the performance of components in the system.
-Examples of common operational considerations are memory consumption and CPU usage, request latency and throughput, and cache utilisation rates.
-Generally speaking, these are the metrics system administrators, operations teams, and engineers will be interested in.
-
-Usage metrics describe the system at a higher and less dynamic level.
-Examples include the number of deployed servers and models, and component versions.
-These are not typically metrics that engineers need insight into, but may be relevant to platform providers and operations teams.
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-operational.md
-usage.md
-```
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-</g>
-</svg>
diff --git a/docs/source/_templates/custom/index.html b/docs/source/_templates/custom/index.html
deleted file mode 100644
index 5c2eec2fef..0000000000
--- a/docs/source/_templates/custom/index.html
+++ /dev/null
@@ -1,123 +0,0 @@
-<style>
-   
-   body {
-     height: 100%;
-     background: url(_static/images/header-gradient-core-v2.png);
-     background-size: cover;
-   }
-  .mdx-container {
-  height: 100%;
-  }
-  
-  .md-header {
-    position: fixed;
-    top: 0;
-    right: 0;
-    left: 0;
-    height: 2.4rem;
-    background-color: rgba(63, 81, 181, 1)!important;
-    color: #fff;
-    box-shadow: none;
-    z-index: 2;
-    -webkit-backface-visibility: hidden;
-    backface-visibility: hidden;
-  }
-  
-  .md-hero {
-    transition: background .25s;
-    background-color: rgba(63, 81, 181, 1)!important;
-    color: #fff;
-    font-size: 1rem;
-    overflow: hidden;
-  }
-  .mdx-hero {
-  padding-top: 50px;
-  display: flex;
-  }
-  
-  .md-tabs {
-    width: 100%;
-    transition: background .25s;
-    background-color: rgba(63, 81, 181, 1)!important;
-    color: #fff;
-    overflow: auto;
-  }
-  
-  .md-hero__inner {
-    margin-top: 1rem!important;
-    margin-bottom: 1.2rem!important;
-  }
-  .mdx-hero__image {
-    padding-top: 50px;
-    color: #ffffff;
-    width: 50%;
-  }
-  .mdx-hero-content {
-  padding-top: 50px;
-  color: #ffffff;
-  width: 50%;
-  }
-
-  .mdx-hero-content > h1 {
-  color: currentColor;
-  font-weight: 700;
-  margin-bottom: 1rem;
-  }
-  .mdx-hero-content > div {
-  margin-top: 30px;
-  }
-
-  .md-button {
-  background-color: #fff;
-  border-color: #fff;
-  color: #894da8;
-  margin-right: 0.5rem;
-  margin-top: 0.5rem;
-  font-weight: 700;
-  padding: 0.625em 2em;
-  }
-  @media only screen and (min-width:76.25em){
-  .md-icon-home {
-    display: none;
-  }
-  }
-</style>
-
-<div class="mdx-container">
-  <div class="md-grid md-typeset">
-    <div class="mdx-hero">
-      <div class="mdx-hero__image">
-        <img
-          src="_static/images/illustration-core-v2.png"
-          draggable="false"
-        />
-      </div>
-      <div class="mdx-hero-content">
-        <h1>Data-centric MLOps: Seldon Core V2</h1>
-        <p>
-          Deploy machine learning locally with Docker or to a Kubernetes cluster.
-	  Scale to 1000s of models. Run data pipelines for models 
-	  containing outlier and drift detection along with model explainers. Comes with tracing, metrics and a CLI.
-	  Supports tensorflow, pytorch, sklearn, ONNX, custom python models and many more ML artifacts.
-	  Run experiments to test new models and combine with any Kubernetes service mesh. Create synchronous and asynchronous inference pipelines backed by Kafka.
-        </p>
-        <div>
-          <a
-            href="contents/getting-started/"
-            title="Get started"
-            class="md-button"
-          >
-            Get started
-          </a>
-          <a
-            href="contents/about/"
-            title="About"
-            class="md-button"
-          >
-            Learn More
-          </a>
-        </div>
-      </div>
-    </div>
-  </div>
-</div>
diff --git a/docs/source/_templates/header.html b/docs/source/_templates/header.html
deleted file mode 100644
index 67838b514a..0000000000
--- a/docs/source/_templates/header.html
+++ /dev/null
@@ -1,61 +0,0 @@
-<header class="md-header" data-md-component="header">
-  <nav class="md-header-nav md-grid">
-    <div class="md-flex navheader">
-      <div class="md-flex__cell md-flex__cell--shrink">
-        <a
-          href="{{ pathto(master_doc)|e }}"
-          title="{{ docstitle|e }}"
-          class="md-header-nav__button md-logo"
-        >
-          {% if theme_logo_icon|e %}
-          <i class="md-icon">{{ theme_logo_icon }}</i>
-          {% elif logo %}
-          <img
-            src="{{ pathto('_static/' ~ logo, 1) }}"
-            height="26"
-            alt="{{ shorttitle|striptags|e }} logo"
-          />
-          {% else %} &nbsp; {% endif %}
-        </a>
-      </div>
-      <div class="md-flex__cell md-flex__cell--shrink">
-        {% if pagename != 'index' %}
-        <label
-          class="md-icon md-icon--menu md-header-nav__button"
-          for="__drawer"
-        ></label>
-        {% else %}
-        <span class="md-icon material-icons md-icon-home md-header-nav__button">
-          home
-        </span>
-
-        {% endif %}
-      </div>
-      <div class="md-flex__cell md-flex__cell--stretch">
-        <div
-          class="md-flex__ellipsis md-header-nav__title"
-          data-md-component="title"
-        >
-          <span class="md-header-nav__topic"
-            >{{ theme_nav_title or shorttitle }}</span
-          >
-          <span class="md-header-nav__topic"> {{ title|striptags|e }} </span>
-        </div>
-      </div>
-      <div class="md-flex__cell md-flex__cell--shrink">
-        <label
-          class="md-icon md-icon--search md-header-nav__button"
-          for="__search"
-        ></label>
-        {% include "searchbox.html" %}
-      </div>
-      {% if theme_repo_url %}
-      <div class="md-flex__cell md-flex__cell--shrink">
-        <div class="md-header-nav__source">
-          {% include "repo.html" %}
-        </div>
-      </div>
-      {% endif %} {% include "version_dropdown.html" %}
-    </div>
-  </nav>
-</header>
diff --git a/docs/source/_templates/page.html b/docs/source/_templates/page.html
deleted file mode 100644
index 212b7f4d05..0000000000
--- a/docs/source/_templates/page.html
+++ /dev/null
@@ -1,53 +0,0 @@
-{%- extends "layout.html" %} 
-
-{%- block content %} 
-{% set nav_bar_tabs = parents or theme_master_doc or theme_nav_links %}
-<div class="md-container">
-  {%- block hero %} {% include "hero.html" %} {% endblock %} {% include
-  "relbar.html" %}
-  <main class="md-main">
-    {% if pagename == 'index' %} {% include 'custom/index.html' %} {% else %}
-    <div class="md-main__inner md-grid" data-md-component="container">
-      {% block site_nav %}
-      <div
-        class="md-sidebar md-sidebar--primary"
-        data-md-component="navigation"
-      >
-        <div class="md-sidebar__scrollwrap">
-          <div class="md-sidebar__inner">
-            {% include "sidebar.html" %}
-          </div>
-        </div>
-      </div>
-      <div class="md-sidebar md-sidebar--secondary" data-md-component="toc">
-        <div class="md-sidebar__scrollwrap">
-          <div class="md-sidebar__inner">
-            {% include "localtoc.html" %}
-          </div>
-        </div>
-      </div>
-      {% endblock %}
-      <div class="md-content">
-        <article class="md-content__inner md-typeset" role="main">
-          {% block body %} {{ table_fix(body,pagename) }} {% endblock %}
-        </article>
-      </div>
-    </div>
-    {% endif %}
-  </main>
-</div>
-{%- endblock %}
-
-{%- block extrahead %}
-{# Adding CSS and JS bundle for swagger#}
-<link
-  rel="stylesheet"
-  type="text/css"
-  href="https://unpkg.com/swagger-ui-dist@3.12.1/swagger-ui.css"
-/>
-<script
-  src="https://unpkg.com/swagger-ui-dist@3.12.1/swagger-ui-bundle.js"
-  charset="UTF-8"
-></script>
-{{ super() }}
-{%- endblock %}
\ No newline at end of file
diff --git a/docs/source/index.md b/docs/source/index.md
deleted file mode 100644
index debd0af14c..0000000000
--- a/docs/source/index.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Home
-
-```{toctree}
-:maxdepth: 4
-:titlesonly:
-:includehidden:
-
-contents/about/index
-contents/getting-started/index
-contents/tutorials/index
-contents/servers/index
-contents/models/index
-contents/explainers/index
-contents/drift/index
-contents/outlier/index
-contents/experiments/index
-contents/pipelines/index
-contents/inference/index
-contents/kubernetes/index
-contents/examples/index
-contents/cli/index
-contents/apis/index
-contents/architecture/index
-contents/faqs/index
-contents/metrics/index
-contents/performance-tests/index
-contents/upgrading/index
-contents/development/index
-```
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
deleted file mode 100644
index a867006328..0000000000
--- a/docs/tutorials/index.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Workflow and Tutorials
-
-## Workflow
-
-Seldon inference is built from atomic Model components. Models as [shown here](../models/inference-artifacts/index) cover a wide range of artifacts including:
-
-  * Core machine learning models, e.g. a Tensorflow model.
-  * Feature transformations that might be built with custom python code.
-  * Drift detectors.
-  * Outlier detectors.
-  * Explainers
-  * Adversarial detectors.
-
-A typical workflow for a production machine learning setup might be as follows:
-
- 1. You create a Tensorflow model for your core application use case and test this model in isolation to validate.
- 1. You create SKLearn feature transformation component before your model to convert the input into the correct form for your model. You also create Drift and Outlier detectors using Seldon's open source Alibi-detect library and test these in isolation. 
- 1. You join these components together into a Pipeline for the final production setup.
-
-These steps are shown in the diagram below:
-
-![Workflow](./workflow.png)
-
-## Worked Examples
-
-See our selection of [examples](../examples/index.md).
-
diff --git a/docs/tutorials/workflow.png b/docs/tutorials/workflow.png
deleted file mode 100644
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diff --git a/docs/upgrading/index.md b/docs/upgrading/index.md
deleted file mode 100644
index 2d604ad0d5..0000000000
--- a/docs/upgrading/index.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Upgrading
-
-## Upgrading from 2.6 - 2.7
-
-All pods provisioned through the operator i.e. `SeldonRuntime` and `Server` resources now have the label `app.kubernetes.io/name` for identifying the pods. 
-
-Previously, the labelling has been inconsistent across different versions of Seldon Core v2, with mixture of `app` and `app.kubernetes.io/name` used.
-
-If using the Prometheus operator ("Kube Prometheus"), please apply the v2.7.0 manifests for Core v2 according to the [metrics documentation](../kubernetes/metrics/index.md).
-
-Note that these manifests need to be adjusted to discover metrics endpoints based on the existing setup.
-
-If previous pod monitors had `namespaceSelector` fields set, these should be copied over and applied to the new manifests.
-
-If namespaces do not matter, cluster-wide metrics endpoint discovery can be setup by modifying the `namespaceSelector` field in the pod monitors:
-```yaml
-spec:
-  namespaceSelector:
-    any: true
-```
-
-## Upgrading from 2.5 - 2.6
-
-Release 2.6 brings with it new custom resources `SeldonConfig` and `SeldonRuntime`, which provide a new way to install Seldon Core V2 in Kubernetes. Upgrading in the same namespace will cause downtime while the pods are being recreated. Alternatively  users can have an external service mesh or other means to be used over multiple namespaces to bring up the system in a new namespace and redeploy models before switch traffic between them.
-
-If the new 2.6 charts are used to upgrade in an existing namespace models will eventually be redeloyed but there will be service downtime as the core components are redeployed.
-

From 5e3cbbfe2af08fac83bb82621a64969d7f5a5db2 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 13 Aug 2024 17:17:54 +1000
Subject: [PATCH 18/32] updated SUMMARY.md for GitBook

---
 docs/SUMMARY.md | 44 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)
 create mode 100644 docs/SUMMARY.md

diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
new file mode 100644
index 0000000000..7e37473075
--- /dev/null
+++ b/docs/SUMMARY.md
@@ -0,0 +1,44 @@
+* [Home](about.md)
+* [Getting Started](getting-started/README.md)
+  * [Docker Installation](getting-started/docker-installation.md)
+  * [Kubernetes Installation](getting-started/kubernetes-installation/README.md)
+    * [Ansible](getting-started/kubernetes-installation/ansible.md)
+    * [Helm](getting-started/kubernetes-installation/helm.md)
+    * [Security](getting-started/kubernetes-installation/security/README.md)
+      * [AWS MSK mTLS](getting-started/kubernetes-installation/security/aws-msk-mtls.md)
+      * [AWS MSK SASL](getting-started/kubernetes-installation/security/aws-msk-sasl.md)
+      * [Azure Event Hub SASL Example](getting-started/kubernetes-installation/security/azure-event-hub-sasl.md)
+      * [Confluent Cloud Oauth 2.0 Example](getting-started/kubernetes-installation/security/confluent-oauth.md)
+      * [Confluent Cloud SASL Example](getting-started/kubernetes-installation/security/confluent-sasl.md)
+      * [Strimzi mTLS Example](getting-started/kubernetes-installation/security/strimzi-mtls.md)
+      * [Strimzi SASL Example](getting-started/kubernetes-installation/security/strimzi-sasl.md)
+      * [Reference](getting-started/kubernetes-installation/security/reference.md)
+  * [Configuration](getting-started/configuration.md)
+  * [Seldon CLI](getting-started/cli.md)
+* [APIs](apis/README.md)
+  * [Internal](apis/internal/README.md)
+    * [Chainer](apis/internal/chainer.md)
+    * [Agent](apis/internal/agent.md)
+  * [Inference](apis/inference/README.md)
+    * [V2](apis/inference/v2.md)
+  * [Scheduler](apis/scheduler.md)
+* [Architecture](architecture/README.md)
+  * [DataFlow](architecture/dataflow.md)
+* [Examples](examples/README.md)
+* [Models](models/README.md)
+  * [Multi-Model Serving](models/mms.md)
+  * [Inference Artifacts](models/inference-artifacts.md)
+  * [rClone](models/rclone.md)
+  * [Parameterized Models](models/parameterized-models/README.md)
+  * [Pandas Query](models/parameterized-models/pandasquery.md)
+
+* [Pipelines](pipelines.md)
+* [Experiments](experiments.md)
+* [Servers](servers.md)
+* [Inference](inference.md)
+* [Outlier Detection](outlier.md)
+* [Drift Detection](drift.md)
+* [Explainers](explainers.md)
+* [Performance Tests](performance-tests.md)
+* [Upgrading](upgrading.md)
+* [FAQ](faqs.md)

From 49a44cfa1173aedb112c7c8345713f23bca3f9a7 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Wed, 21 Aug 2024 21:25:15 +1000
Subject: [PATCH 19/32] adding additional images

---
 docs/images/demo.png           | Bin 0 -> 116036 bytes
 docs/images/infer_14_0.png     | Bin 0 -> 5514 bytes
 docs/images/infer_14_1.png     | Bin 0 -> 5671 bytes
 docs/images/infer_14_10.png    | Bin 0 -> 5024 bytes
 docs/images/infer_14_11.png    | Bin 0 -> 5473 bytes
 docs/images/infer_14_12.png    | Bin 0 -> 4954 bytes
 docs/images/infer_14_13.png    | Bin 0 -> 4581 bytes
 docs/images/infer_14_14.png    | Bin 0 -> 5579 bytes
 docs/images/infer_14_15.png    | Bin 0 -> 4829 bytes
 docs/images/infer_14_16.png    | Bin 0 -> 5070 bytes
 docs/images/infer_14_17.png    | Bin 0 -> 5085 bytes
 docs/images/infer_14_18.png    | Bin 0 -> 4941 bytes
 docs/images/infer_14_19.png    | Bin 0 -> 5338 bytes
 docs/images/infer_14_2.png     | Bin 0 -> 4642 bytes
 docs/images/infer_14_3.png     | Bin 0 -> 5331 bytes
 docs/images/infer_14_4.png     | Bin 0 -> 5263 bytes
 docs/images/infer_14_5.png     | Bin 0 -> 5523 bytes
 docs/images/infer_14_6.png     | Bin 0 -> 5641 bytes
 docs/images/infer_14_7.png     | Bin 0 -> 5509 bytes
 docs/images/infer_14_8.png     | Bin 0 -> 4840 bytes
 docs/images/infer_14_9.png     | Bin 0 -> 4769 bytes
 docs/images/infer_16_0.png     | Bin 0 -> 6163 bytes
 docs/images/infer_16_1.png     | Bin 0 -> 6112 bytes
 docs/images/infer_16_10.png    | Bin 0 -> 6117 bytes
 docs/images/infer_16_11.png    | Bin 0 -> 6138 bytes
 docs/images/infer_16_12.png    | Bin 0 -> 6137 bytes
 docs/images/infer_16_13.png    | Bin 0 -> 6073 bytes
 docs/images/infer_16_14.png    | Bin 0 -> 6175 bytes
 docs/images/infer_16_15.png    | Bin 0 -> 6198 bytes
 docs/images/infer_16_16.png    | Bin 0 -> 6132 bytes
 docs/images/infer_16_17.png    | Bin 0 -> 6191 bytes
 docs/images/infer_16_18.png    | Bin 0 -> 6060 bytes
 docs/images/infer_16_19.png    | Bin 0 -> 6187 bytes
 docs/images/infer_16_2.png     | Bin 0 -> 6145 bytes
 docs/images/infer_16_3.png     | Bin 0 -> 6168 bytes
 docs/images/infer_16_4.png     | Bin 0 -> 6169 bytes
 docs/images/infer_16_5.png     | Bin 0 -> 6151 bytes
 docs/images/infer_16_6.png     | Bin 0 -> 6091 bytes
 docs/images/infer_16_7.png     | Bin 0 -> 6164 bytes
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 docs/images/infer_16_9.png     | Bin 0 -> 6186 bytes
 docs/images/infer_18_0.png     | Bin 0 -> 5614 bytes
 docs/images/infer_19_0.png     | Bin 0 -> 5514 bytes
 docs/images/model-zoo_17_0.png | Bin 0 -> 6382 bytes
 docs/images/model-zoo_31_0.png | Bin 0 -> 2417 bytes
 docs/images/release-1.png      | Bin 0 -> 117374 bytes
 docs/images/release-2.png      | Bin 0 -> 110369 bytes
 docs/images/release-3.png      | Bin 0 -> 76862 bytes
 docs/images/release-4.png      | Bin 0 -> 88056 bytes
 docs/images/release-5.png      | Bin 0 -> 72557 bytes
 docs/images/release-6.png      | Bin 0 -> 106170 bytes
 51 files changed, 0 insertions(+), 0 deletions(-)
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 create mode 100644 docs/images/infer_14_0.png
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 create mode 100644 docs/images/infer_14_10.png
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 create mode 100644 docs/images/infer_14_15.png
 create mode 100644 docs/images/infer_14_16.png
 create mode 100644 docs/images/infer_14_17.png
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 create mode 100644 docs/images/model-zoo_31_0.png
 create mode 100644 docs/images/release-1.png
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 create mode 100644 docs/images/release-3.png
 create mode 100644 docs/images/release-4.png
 create mode 100644 docs/images/release-5.png
 create mode 100644 docs/images/release-6.png

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new file mode 100644
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From a7e3590a69f34c35b56d87bb78bc4e8f16260395 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Wed, 21 Aug 2024 21:25:59 +1000
Subject: [PATCH 20/32] restructured development dir

---
 docs/development/README.md                    |  22 +++
 docs/development/index.md                     |  30 ----
 docs/development/licenses.md                  |   8 +-
 docs/development/release.md                   | 152 ++++++++++++++++++
 .../development/release/.images/release-1.png | Bin 117374 -> 0 bytes
 .../development/release/.images/release-2.png | Bin 110369 -> 0 bytes
 .../development/release/.images/release-3.png | Bin 76862 -> 0 bytes
 .../development/release/.images/release-4.png | Bin 88056 -> 0 bytes
 .../development/release/.images/release-5.png | Bin 72557 -> 0 bytes
 .../development/release/.images/release-6.png | Bin 106170 -> 0 bytes
 docs/development/release/index.md             |   5 -
 11 files changed, 178 insertions(+), 39 deletions(-)
 create mode 100644 docs/development/README.md
 delete mode 100644 docs/development/index.md
 create mode 100644 docs/development/release.md
 delete mode 100644 docs/development/release/.images/release-1.png
 delete mode 100644 docs/development/release/.images/release-2.png
 delete mode 100644 docs/development/release/.images/release-3.png
 delete mode 100644 docs/development/release/.images/release-4.png
 delete mode 100644 docs/development/release/.images/release-5.png
 delete mode 100644 docs/development/release/.images/release-6.png
 delete mode 100644 docs/development/release/index.md

diff --git a/docs/development/README.md b/docs/development/README.md
new file mode 100644
index 0000000000..b7f6ef8b9a
--- /dev/null
+++ b/docs/development/README.md
@@ -0,0 +1,22 @@
+# Development
+
+## Development Requirements
+
+* Go 1.17+ with glibc development libraries installed.
+  * e.g. for Ubuntu systems: `sudo apt-get install libc6-dev`
+* Java JDK 17+ and Kotlin 1.6.10+
+* Kubebuilder V2
+* Docker and docker-compose
+* Helm
+* Kustomize
+* Ansible
+
+## Testing resources
+
+* Kind
+* k6
+* unix utils: jq, curl, grpcurl
+
+## Release Process
+
+[Releases are carried out via Github.](./release.md)
diff --git a/docs/development/index.md b/docs/development/index.md
deleted file mode 100644
index de26de8f51..0000000000
--- a/docs/development/index.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# Development
-
-## Development Requirements
-
- * Go 1.17+ with glibc development libraries installed.
-    * e.g. for Ubuntu systems: `sudo apt-get install libc6-dev`
- * Java JDK 17+ and Kotlin 1.6.10+
- * Kubebuilder V2
- * Docker and docker-compose
- * Helm
- * Kustomize
- * Ansible
-
-## Testing resources
-
- * Kind
- * k6
- * unix utils: jq, curl, grpcurl
-
-## Release Process
-
-[Releases are carried out via Github.](./release/index.md)
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-release/index.md
-licenses.md
-```
diff --git a/docs/development/licenses.md b/docs/development/licenses.md
index c3ec5af4b1..6ff97c78d9 100644
--- a/docs/development/licenses.md
+++ b/docs/development/licenses.md
@@ -10,22 +10,22 @@ For Go modules we use a fork of Kubeflow's testing repo with some fixes.
 
 Clone and update to [this branch](https://github.com/SeldonIO/kubeflow-testing/tree/seldon-update).
 
-```
+```sh
 git clone https://github.com/SeldonIO/kubeflow-testing -b seldon-update
 ```
 
 Go to go-license-tools folder:
 
-```
+```sh
 cd kubeflow-testing/py/kubeflow/testing/go-license-tools/
 ```
 
 Install
 
-```
+```sh
 python setup.py install
 ```
 
 ### Generation
 
-Run `make update-3rd-party-licenses` in top level folder.
\ No newline at end of file
+Run `make update-3rd-party-licenses` in top level folder.
diff --git a/docs/development/release.md b/docs/development/release.md
new file mode 100644
index 0000000000..90f5e1d3bf
--- /dev/null
+++ b/docs/development/release.md
@@ -0,0 +1,152 @@
+# Release Process
+
+This document summarizes the release process for Seldon Core v2.
+It is aimed mainly at the maintainers.
+
+{% hint style="warning" %}
+This is a work in progress.
+
+This is an early version of the release process, which is subject to change.
+Please, always check this document before conducting a release, and verify if everything goes as expected.
+{% endhint %}
+
+## Process Summary
+
+1. Cut branch for release, e.g. `release-0.1`
+2. Run "Draft New Release" workflow (e.g. choose `release-0.1` branch and `v0.1.0-rc1` version)
+3. Run "Build docker images" workflow (e.g. choose `release-0.1` branch and `0.1.0-rc1` tag)
+4. Verify correctness of created artifacts and images (not yet automated!)
+5. Publish release
+6. Publish tags for the Go modules
+
+
+## Process discussion
+
+The development process follows a standard GitHub workflow.
+
+![Development Graph](../images/release-1.png)
+
+The main development is happening in the `v2` branch.
+This is where new features land through Pull Requests.
+When all features for a new release have been merged, for example `v0.1.0`, we cut a branch for that release, e.g. `release-0.1`.
+
+The `release-0.1` branch will be the base for the `v0.1.0` release as well as the release candidates, i.e. `v0.1.0-rcX`, and successive patch releases, i.e. `v0.1.X`.
+We use GitHub Actions to prepare the release, build images and run all necessary testing.
+
+If the release draft needs to be updated before being published, the new commits should be merged into the `release-0.1` branch and relevant workflows re-triggered as required.
+
+![Draft Update Graph](../images/release-2.png)
+
+
+### Draft New Release Action
+
+The [Draft New Release](https://github.com/SeldonIO/seldon-core/blob/v2/.github/workflows/draft-release.yml) workflow is the
+first one to run. It must be triggered manually using the [Actions](https://github.com/SeldonIO/seldon-core/actions/workflows/draft-release.yml)
+interface in the GitHub UI.
+
+When triggering the workflow, you must:
+- Select the release branch (here `release-0.1`)
+- Specify the release `version` (here `v0.1.0-rc1`)
+
+![Triggering Draft Workflow](../images/release-4.png)
+
+This workflow cannot run on the `v2` branch.
+
+It will validate the provided `version` against a SemVer regex.
+
+It will create a few commits with:
+- Updated Helm charts
+- Updated Kubernetes YAML manifests
+- An updated changelog
+
+![Created Commits](../images/release-3.png)
+
+Once the workflow finishes, you will find a new release draft waiting to be published.
+
+![Draft Release](../images/release-5.png)
+
+> :warning: **NOTE:** Before publishing the release, run the images build workflow and necessary tests (not yet automated)!
+
+
+### Build docker images Action
+
+The [Build docker images](https://github.com/SeldonIO/seldon-core/blob/v2/.github/workflows/images.yml) workflow is the
+second one to run. It must be triggered manually using the [Actions](https://github.com/SeldonIO/seldon-core/actions/workflows/draft-release.yml)
+interface in the GitHub UI.
+
+When triggering the workflow, you must:
+- Select the release branch (here `release-0.1`)
+- Specify the release `version`, e.g. `0.1.0-rc1`
+  - Note the lack of the `v` prefix here
+
+![Triggering Build images](../images/release-6.png)
+
+This workflow will then run unit tests and build a series of Docker images that will be automatically pushed to
+[DockerHub](https://hub.docker.com/).
+
+
+### Add Go module tags
+
+Go module versions are mapped to VCS versions via semantic version tags.
+This process is described in the [Go documentation](https://go.dev/ref/mod#vcs-version).
+
+As we have multiple Go modules in subdirectories of the repository, we need to use corresponding prefixes for our git tags.
+From the above link on mapping versions to commits:
+> If a module is defined in a subdirectory within the repository, that is, the module subdirectory portion of the module
+path is not empty, then each tag name must be prefixed with the module subdirectory, followed by a slash. For example,
+the module golang.org/x/tools/gopls is defined in the gopls subdirectory of the repository with root path
+golang.org/x/tools. The version v0.4.0 of that module must have the tag named gopls/v0.4.0 in that repository.
+
+Thus, for any given release, we should have one tag for the release as a whole plus one corresponding tag for every Go module.
+At the time of writing, this comprises:
+* `apis/go`
+* `components/tls`
+* `hodometer`
+* `operator`
+* `scheduler`
+
+{% hint style="warning" %}
+Adding these tags is currently a **manual** process.
+{% endhint %}
+
+To add the appropriate tags:
+* Check out the relevant tag, e.g.
+  ```sh
+  git checkout v2.4.0
+  ```
+* Find all relevant Go modules and identify their subdirectory paths, e.g. with
+  ```sh
+  find . -name go.mod -exec sed -n '1 { s|^module.*seldon-core/||; s|/v2$||; p }' {} \;
+  ```
+* Add corresponding tags for each module, e.g.
+  ```sh
+  git tag apis/go/v2.4.0 v2.4.0
+  ```
+* Confirm that all tags point to the same place, e.g. with
+  ```sh
+  git tag --contains v2.4.0
+  ```
+* Push the tags to the upstream repository, e.g.
+  ```sh
+  git push <upstream name> apis/go/v2.4.0 components/tls/v2.4.0 ...
+  ```
+
+If you are feeling confident in the process, you can chain these together into a longer pipeline.
+In any case, it is best to confirm that the tags appear as expected both via the git CLI and also in the GitHub UI.
+
+
+A short list of commands to cover all above in single go is:
+```bash
+VERSION=v2.4.0
+git tag apis/go/${VERSION} ${VERSION}
+git tag components/tls/${VERSION} ${VERSION}
+git tag hodometer/${VERSION} ${VERSION}
+git tag operator/${VERSION} ${VERSION}
+git tag scheduler/${VERSION} ${VERSION}
+
+git push origin apis/go/${VERSION}
+git push origin components/tls/${VERSION}
+git push origin hodometer/${VERSION}
+git push origin operator/${VERSION}
+git push origin scheduler/${VERSION}
+```
diff --git a/docs/development/release/.images/release-1.png b/docs/development/release/.images/release-1.png
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diff --git a/docs/development/release/index.md b/docs/development/release/index.md
deleted file mode 100644
index 582ae9e4ca..0000000000
--- a/docs/development/release/index.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Release process
-
-```{include} ../../../../../RELEASE.md
-```
-

From 25fcba510483a3c71a477f4a029b8124ff93705c Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Wed, 21 Aug 2024 21:26:40 +1000
Subject: [PATCH 21/32] restructured and reformatted examples dir to match
 GitBook's md flavor

---
 docs/examples/README.md                       |   84 +
 docs/examples/assets/models/add10.yaml        |    9 +
 docs/examples/assets/models/add20.yaml        |    9 +
 docs/examples/assets/models/check.yaml        |    9 +
 docs/examples/assets/models/choice1.yaml      |   12 +
 docs/examples/assets/models/choice2.yaml      |   12 +
 .../assets/models/cifar10-drift-detect.yaml   |    9 +
 .../assets/models/cifar10-no-config.yaml      |    8 +
 .../assets/models/cifar10-outlier-detect.yaml |    9 +
 docs/examples/assets/models/cifar10.yaml      |    8 +
 docs/examples/assets/models/conditional.yaml  |    9 +
 .../assets/models/error-bad-capabilities.yaml |    8 +
 .../assets/models/error-bad-spec.yaml         |    8 +
 docs/examples/assets/models/error-no-uri.yaml |    8 +
 .../assets/models/error-uri-notexist.yaml     |    8 +
 .../models/extra-model-requirements.yaml      |    8 +
 .../assets/models/hf-sentiment-explainer.yaml |    9 +
 .../models/hf-sentiment-input-transform.yaml  |    9 +
 .../models/hf-sentiment-output-transform.yaml |    9 +
 docs/examples/assets/models/hf-sentiment.yaml |    8 +
 .../models/hf-text-gen-custom-gpt2.yaml       |    9 +
 .../hf-text-gen-custom-tiny-stories.yaml      |    8 +
 docs/examples/assets/models/hf-text-gen.yaml  |    8 +
 docs/examples/assets/models/hf-whisper.yaml   |    8 +
 docs/examples/assets/models/id1_node.yaml     |    9 +
 docs/examples/assets/models/id2_node.yaml     |    9 +
 docs/examples/assets/models/income-drift.yaml |    9 +
 .../models/income-explainer-pipeline.yaml     |    9 +
 .../assets/models/income-explainer.yaml       |    9 +
 docs/examples/assets/models/income-lgb.yaml   |    8 +
 .../assets/models/income-outlier.yaml         |    9 +
 .../assets/models/income-preprocess.yaml      |    8 +
 docs/examples/assets/models/income-xgb.yaml   |    8 +
 docs/examples/assets/models/income.yaml       |    8 +
 .../models/iris-custom-requirements.yaml      |    8 +
 .../assets/models/iris-custom-server.yaml     |    7 +
 .../assets/models/iris-drift-detect.yaml      |    9 +
 docs/examples/assets/models/iris-v1.yaml      |    8 +
 docs/examples/assets/models/iris-v2.yaml      |    8 +
 docs/examples/assets/models/iris-v3.yaml      |    8 +
 docs/examples/assets/models/join_node.yaml    |    9 +
 docs/examples/assets/models/mnist-onnx.yaml   |    8 +
 .../examples/assets/models/mnist-pytorch.yaml |    8 +
 .../models/moviesentiment-explainer.yaml      |    9 +
 .../assets/models/moviesentiment.yaml         |    8 +
 docs/examples/assets/models/mul10.yaml        |    9 +
 .../assets/models/multi-version-1.yaml        |   10 +
 .../assets/models/multi-version-2.yaml        |   10 +
 .../examples/assets/models/outlier-error.yaml |    9 +
 .../assets/models/sklearn-iris-gs-baduri.yaml |    9 +
 .../assets/models/sklearn-iris-gs.yaml        |    9 +
 .../assets/models/sklearn-iris-local.yaml     |    8 +
 .../assets/models/sklearn-iris-minio.yaml     |    9 +
 docs/examples/assets/models/sklearn1.yaml     |    8 +
 docs/examples/assets/models/sklearn2.yaml     |    8 +
 docs/examples/assets/models/sklearn3.yaml     |    8 +
 docs/examples/assets/models/tfsimple1.yaml    |    9 +
 docs/examples/assets/models/tfsimple2.yaml    |    9 +
 docs/examples/assets/models/tfsimple3.yaml    |    9 +
 .../assets/models/tfsimple_scaling.yaml       |   11 +
 docs/examples/assets/models/wine-mlflow.yaml  |    8 +
 docs/examples/batch-examples-k8s.md           |  713 ++++-
 docs/examples/batch-examples-local.md         |   58 +-
 docs/examples/cifar10.md                      |  551 +++-
 docs/examples/custom-servers.md               |  274 +-
 docs/examples/experiment-versions.md          |  487 ++-
 docs/examples/explainer-examples.md           |  316 +-
 docs/examples/huggingface.md                  |  187 +-
 docs/examples/income.md                       |  340 ++-
 docs/examples/inference.md                    |  355 ++-
 docs/examples/k8s-clusterwide.md              |  661 +++-
 docs/examples/k8s-examples.md                 |  864 +++++-
 docs/examples/k8s-pvc.md                      |  267 +-
 docs/examples/local-examples.md               |  630 +++-
 docs/examples/local-experiments.md            | 1070 ++++++-
 docs/examples/local-overcommit-examples.md    | 1670 +++++++++-
 docs/examples/model-zoo.md                    |  705 ++++-
 docs/examples/multi-version.md                |  171 +-
 docs/examples/notebooks/ambassador.ipynb      |  583 ++++
 docs/examples/notebooks/cli-tests.ipynb       |  577 ++++
 .../notebooks/experiment_versions.ipynb       |  965 ++++++
 .../notebooks/explainer-examples.ipynb        |  542 ++++
 docs/examples/notebooks/huggingface.ipynb     |  385 +++
 docs/examples/notebooks/infer.ipynb           |  365 +++
 docs/examples/notebooks/inference.ipynb       |  557 ++++
 docs/examples/notebooks/istio.ipynb           |  568 ++++
 .../notebooks/k8s-batch-examples.ipynb        |  990 ++++++
 .../k8s-clusterwide-kafka-mtls-demo.ipynb     |  932 ++++++
 .../notebooks/k8s-clusterwide-ssl-demo.ipynb  |  762 +++++
 docs/examples/notebooks/k8s-clusterwide.ipynb | 1070 +++++++
 .../notebooks/k8s-custom-servers.ipynb        |  504 +++
 docs/examples/notebooks/k8s-examples.ipynb    | 1372 +++++++++
 docs/examples/notebooks/k8s-explainers.ipynb  |  763 +++++
 .../notebooks/k8s-multi-version.ipynb         |  319 ++
 .../notebooks/k8s-resource-deletion.ipynb     |  922 ++++++
 .../notebooks/k8s-tritonclient-aio.ipynb      |  924 ++++++
 .../examples/notebooks/k8s-tritonclient.ipynb |  867 ++++++
 docs/examples/notebooks/local-examples.ipynb  | 1052 +++++++
 .../notebooks/local-experiments.ipynb         | 1910 ++++++++++++
 .../notebooks/local-over-commit-test.ipynb    | 1849 +++++++++++
 docs/examples/notebooks/metrics.ipynb         |  399 +++
 docs/examples/notebooks/model-zoo.ipynb       | 1167 +++++++
 docs/examples/notebooks/pipeline-errors.ipynb |  438 +++
 .../notebooks/pipeline-examples.ipynb         | 2150 +++++++++++++
 .../pipeline-ready-and-metadata.ipynb         |  845 ++++++
 .../notebooks/pipeline-to-pipeline.ipynb      | 2693 +++++++++++++++++
 .../notebooks/pipeline-versions.ipynb         |  493 +++
 docs/examples/notebooks/python-examples.ipynb |  217 ++
 docs/examples/notebooks/rolling-update.ipynb  |  131 +
 docs/examples/notebooks/train.ipynb           |  409 +++
 docs/examples/notebooks/trigger_joins.ipynb   |  527 ++++
 docs/examples/pandasquery.md                  |  295 +-
 docs/examples/pipeline-examples.md            | 1527 +++++++++-
 docs/examples/pipeline-ready-and-metadata.md  |  385 ++-
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diff --git a/docs/examples/README.md b/docs/examples/README.md
new file mode 100644
index 0000000000..267f60b742
--- /dev/null
+++ b/docs/examples/README.md
@@ -0,0 +1,84 @@
+# Workflow, Examples and Tutorials
+
+## Workflows
+
+Seldon inference is built from atomic Model components. Models as [shown here](../models/inference-artifacts.md)
+cover a wide range of artifacts including:
+
+* Core machine learning models, e.g. a PyTorch model.
+* Feature transformations that might be built with custom python code.
+* Drift detectors.
+* Outlier detectors.
+* Explainers
+* Adversarial detectors.
+
+A typical workflow for a production machine learning setup might be as follows:
+
+1. You create a Tensorflow model for your core application use case and test this model in isolation to validate.
+2. You create SKLearn feature transformation component before your model to convert the input into the correct
+form for your model. You also create Drift and Outlier detectors using Seldon's open source Alibi-detect library
+and test these in isolation.
+3. You join these components together into a Pipeline for the final production setup.
+
+These steps are shown in the diagram below:
+
+![Workflow](../images/workflow.png)
+
+## Examples & Tutorials
+
+This section will provide some examples to allow operations with Seldon to be tested so you can run your own
+models, experiments, pipelines and explainers.
+
+## Getting Started Examples
+
+* [Local examples](local-examples.md)
+* [Kubernetes examples](k8s-examples.md)
+
+## Models
+
+* [Huggingface models](huggingface.md)
+* [Model zoo](model-zoo.md)
+* [Artifact versions](multi-version.md)
+
+## Pipelines
+
+* [Pipeline examples](pipeline-examples.md)
+* [Pipeline to pipeline examples](pipeline-to-pipeline.md)
+
+## Explainers
+
+* [Explainer examples](explainer-examples.md)
+
+## Servers
+
+* [Custom Servers](custom-servers.md)
+
+## Experiments
+
+* [Local experiments](local-experiments.md)
+* [Experiment version examples](experiment-versions.md)
+
+
+## Making Inference Requests
+
+* [Inference examples](inference.md)
+* [Tritonclient examples](tritonclient-examples.md)
+* [Batch Inference examples (kubernetes)](batch-examples-k8s.md)
+* [Batch Inference examples (local)](batch-examples-local.md)
+
+## Misc
+
+* [Checking Pipeline readiness](pipeline-ready-and-metadata.md)
+* [Local Overcommit](local-overcommit-examples)
+
+## Further Kubernetes Examples
+
+* [Kubernetes custerwide example](k8s-clusterwide.md)
+
+## Advanced Examples
+
+* [Huggingface speech to sentiment with explanations pipeline](speech-to-sentiment.md)
+* [Production image classifier with drift and outlier monitoring](cifar10.md)
+* [Production income classifier with drift, outlier and explanations](income.md)
+* [Conditional pipeline with pandas query model](pandasquery.md)
+* [Kubernetes Server with PVC](k8s-pvc.md)
diff --git a/docs/examples/assets/models/add10.yaml b/docs/examples/assets/models/add10.yaml
new file mode 100644
index 0000000000..b415aa1fad
--- /dev/null
+++ b/docs/examples/assets/models/add10.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: add10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/add10"
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/add20.yaml b/docs/examples/assets/models/add20.yaml
new file mode 100644
index 0000000000..0a982e89d5
--- /dev/null
+++ b/docs/examples/assets/models/add20.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: add20
+spec:
+  storageUri: "gs://seldon-models/triton/add20"
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/check.yaml b/docs/examples/assets/models/check.yaml
new file mode 100644
index 0000000000..7c41361f3a
--- /dev/null
+++ b/docs/examples/assets/models/check.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: check
+spec:
+  storageUri: "gs://seldon-models/triton/check"
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/choice1.yaml b/docs/examples/assets/models/choice1.yaml
new file mode 100644
index 0000000000..760074eb06
--- /dev/null
+++ b/docs/examples/assets/models/choice1.yaml
@@ -0,0 +1,12 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: choice-is-one
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/pandasquery"
+  requirements:
+  - mlserver
+  - python
+  parameters:
+  - name: query
+    value: "choice == 1"
diff --git a/docs/examples/assets/models/choice2.yaml b/docs/examples/assets/models/choice2.yaml
new file mode 100644
index 0000000000..25a4c668e1
--- /dev/null
+++ b/docs/examples/assets/models/choice2.yaml
@@ -0,0 +1,12 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: choice-is-two
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/pandasquery"
+  requirements:
+  - mlserver
+  - python
+  parameters:
+  - name: query
+    value: "choice == 2"
diff --git a/docs/examples/assets/models/cifar10-drift-detect.yaml b/docs/examples/assets/models/cifar10-drift-detect.yaml
new file mode 100644
index 0000000000..a572dd2336
--- /dev/null
+++ b/docs/examples/assets/models/cifar10-drift-detect.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: cifar10-drift
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/cifar10/drift-detector"
+  requirements:
+    - mlserver
+    - alibi-detect
diff --git a/docs/examples/assets/models/cifar10-no-config.yaml b/docs/examples/assets/models/cifar10-no-config.yaml
new file mode 100644
index 0000000000..bd69297437
--- /dev/null
+++ b/docs/examples/assets/models/cifar10-no-config.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: cifar10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/tensorflow/cifar10"
+  requirements:
+  - tensorflow
diff --git a/docs/examples/assets/models/cifar10-outlier-detect.yaml b/docs/examples/assets/models/cifar10-outlier-detect.yaml
new file mode 100644
index 0000000000..8651038edc
--- /dev/null
+++ b/docs/examples/assets/models/cifar10-outlier-detect.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: cifar10-outlier
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/cifar10/outlier-detector"
+  requirements:
+    - mlserver
+    - alibi-detect
diff --git a/docs/examples/assets/models/cifar10.yaml b/docs/examples/assets/models/cifar10.yaml
new file mode 100644
index 0000000000..27ee402e02
--- /dev/null
+++ b/docs/examples/assets/models/cifar10.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: cifar10
+spec:
+  storageUri: "gs://seldon-models/triton/tf_cifar10"
+  requirements:
+  - tensorflow
diff --git a/docs/examples/assets/models/conditional.yaml b/docs/examples/assets/models/conditional.yaml
new file mode 100644
index 0000000000..362a440040
--- /dev/null
+++ b/docs/examples/assets/models/conditional.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: conditional
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/conditional"
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/error-bad-capabilities.yaml b/docs/examples/assets/models/error-bad-capabilities.yaml
new file mode 100644
index 0000000000..29c54c016f
--- /dev/null
+++ b/docs/examples/assets/models/error-bad-capabilities.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: badcapabilities
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - foobar
diff --git a/docs/examples/assets/models/error-bad-spec.yaml b/docs/examples/assets/models/error-bad-spec.yaml
new file mode 100644
index 0000000000..1db50b313f
--- /dev/null
+++ b/docs/examples/assets/models/error-bad-spec.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storagUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/error-no-uri.yaml b/docs/examples/assets/models/error-no-uri.yaml
new file mode 100644
index 0000000000..953f74af76
--- /dev/null
+++ b/docs/examples/assets/models/error-no-uri.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: bad-uri
+spec:
+  storageUri: ""
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/error-uri-notexist.yaml b/docs/examples/assets/models/error-uri-notexist.yaml
new file mode 100644
index 0000000000..a482b7d72d
--- /dev/null
+++ b/docs/examples/assets/models/error-uri-notexist.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/xyz"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/extra-model-requirements.yaml b/docs/examples/assets/models/extra-model-requirements.yaml
new file mode 100644
index 0000000000..a7b75c24a1
--- /dev/null
+++ b/docs/examples/assets/models/extra-model-requirements.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: extra-model-requirements
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - extra
diff --git a/docs/examples/assets/models/hf-sentiment-explainer.yaml b/docs/examples/assets/models/hf-sentiment-explainer.yaml
new file mode 100644
index 0000000000..13e9083e2f
--- /dev/null
+++ b/docs/examples/assets/models/hf-sentiment-explainer.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/huggingface/speech-sentiment/explainer"
+  explainer:
+    type: anchor_text
+    pipelineRef: sentiment-explain
diff --git a/docs/examples/assets/models/hf-sentiment-input-transform.yaml b/docs/examples/assets/models/hf-sentiment-input-transform.yaml
new file mode 100644
index 0000000000..e5b146f71e
--- /dev/null
+++ b/docs/examples/assets/models/hf-sentiment-input-transform.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment-input-transform
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/huggingface/mlserver_1.3.5/sentiment-input-transform"
+  requirements:
+  - mlserver
+  - python
diff --git a/docs/examples/assets/models/hf-sentiment-output-transform.yaml b/docs/examples/assets/models/hf-sentiment-output-transform.yaml
new file mode 100644
index 0000000000..f351f97035
--- /dev/null
+++ b/docs/examples/assets/models/hf-sentiment-output-transform.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment-output-transform
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/huggingface/mlserver_1.3.5/sentiment-output-transform"
+  requirements:
+  - mlserver
+  - python
diff --git a/docs/examples/assets/models/hf-sentiment.yaml b/docs/examples/assets/models/hf-sentiment.yaml
new file mode 100644
index 0000000000..5693a37e03
--- /dev/null
+++ b/docs/examples/assets/models/hf-sentiment.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment
+spec:
+  storageUri: "gs://seldon-models/mlserver/huggingface/sentiment"
+  requirements:
+  - huggingface
diff --git a/docs/examples/assets/models/hf-text-gen-custom-gpt2.yaml b/docs/examples/assets/models/hf-text-gen-custom-gpt2.yaml
new file mode 100644
index 0000000000..095ee4f97a
--- /dev/null
+++ b/docs/examples/assets/models/hf-text-gen-custom-gpt2.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: custom-gpt2-text-gen
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/huggingface-text-gen-custom-gpt2"
+  requirements:
+    - huggingface
+  memory: 3Gi
diff --git a/docs/examples/assets/models/hf-text-gen-custom-tiny-stories.yaml b/docs/examples/assets/models/hf-text-gen-custom-tiny-stories.yaml
new file mode 100644
index 0000000000..a65428a145
--- /dev/null
+++ b/docs/examples/assets/models/hf-text-gen-custom-tiny-stories.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: custom-tiny-stories-text-gen
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/huggingface-text-gen-custom-tiny-stories"
+  requirements:
+    - huggingface
diff --git a/docs/examples/assets/models/hf-text-gen.yaml b/docs/examples/assets/models/hf-text-gen.yaml
new file mode 100644
index 0000000000..9ccdcae320
--- /dev/null
+++ b/docs/examples/assets/models/hf-text-gen.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: text-gen
+spec:
+  storageUri: "gs://seldon-models/mlserver/huggingface/text-generation"
+  requirements:
+  - huggingface
diff --git a/docs/examples/assets/models/hf-whisper.yaml b/docs/examples/assets/models/hf-whisper.yaml
new file mode 100644
index 0000000000..02994b5249
--- /dev/null
+++ b/docs/examples/assets/models/hf-whisper.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: whisper
+spec:
+  storageUri: "gs://seldon-models/mlserver/huggingface/whisper"
+  requirements:
+  - huggingface
diff --git a/docs/examples/assets/models/id1_node.yaml b/docs/examples/assets/models/id1_node.yaml
new file mode 100644
index 0000000000..40a63b9e78
--- /dev/null
+++ b/docs/examples/assets/models/id1_node.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: id1_node
+spec:
+  storageUri: "gs://seldon-models/triton/id_node"
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/id2_node.yaml b/docs/examples/assets/models/id2_node.yaml
new file mode 100644
index 0000000000..24936d23f8
--- /dev/null
+++ b/docs/examples/assets/models/id2_node.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: id2_node
+spec:
+  storageUri: "gs://seldon-models/triton/id_node"
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/income-drift.yaml b/docs/examples/assets/models/income-drift.yaml
new file mode 100644
index 0000000000..05066a93ef
--- /dev/null
+++ b/docs/examples/assets/models/income-drift.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-drift
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/drift-detector"
+  requirements:
+    - mlserver
+    - alibi-detect
diff --git a/docs/examples/assets/models/income-explainer-pipeline.yaml b/docs/examples/assets/models/income-explainer-pipeline.yaml
new file mode 100644
index 0000000000..3ffc43faed
--- /dev/null
+++ b/docs/examples/assets/models/income-explainer-pipeline.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/income-sklearn/anchor-explainer"
+  explainer:
+    type: anchor_tabular
+    pipelineRef: income-prod
diff --git a/docs/examples/assets/models/income-explainer.yaml b/docs/examples/assets/models/income-explainer.yaml
new file mode 100644
index 0000000000..ed4211361c
--- /dev/null
+++ b/docs/examples/assets/models/income-explainer.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/income-sklearn/anchor-explainer"
+  explainer:
+    type: anchor_tabular
+    modelRef: income
diff --git a/docs/examples/assets/models/income-lgb.yaml b/docs/examples/assets/models/income-lgb.yaml
new file mode 100644
index 0000000000..65f3ca60ac
--- /dev/null
+++ b/docs/examples/assets/models/income-lgb.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-lgb
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/income-lgb"
+  requirements:
+  - lightgbm
diff --git a/docs/examples/assets/models/income-outlier.yaml b/docs/examples/assets/models/income-outlier.yaml
new file mode 100644
index 0000000000..02763f7ded
--- /dev/null
+++ b/docs/examples/assets/models/income-outlier.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-outlier
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/outlier-detector"
+  requirements:
+    - mlserver
+    - alibi-detect
diff --git a/docs/examples/assets/models/income-preprocess.yaml b/docs/examples/assets/models/income-preprocess.yaml
new file mode 100644
index 0000000000..fd9b527efa
--- /dev/null
+++ b/docs/examples/assets/models/income-preprocess.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-preprocess
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/preprocessor"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/income-xgb.yaml b/docs/examples/assets/models/income-xgb.yaml
new file mode 100644
index 0000000000..214e836bad
--- /dev/null
+++ b/docs/examples/assets/models/income-xgb.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-xgb
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/income-xgb"
+  requirements:
+  - xgboost
diff --git a/docs/examples/assets/models/income.yaml b/docs/examples/assets/models/income.yaml
new file mode 100644
index 0000000000..9c8e6247e4
--- /dev/null
+++ b/docs/examples/assets/models/income.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/income-sklearn/classifier"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/iris-custom-requirements.yaml b/docs/examples/assets/models/iris-custom-requirements.yaml
new file mode 100644
index 0000000000..b2201f9be0
--- /dev/null
+++ b/docs/examples/assets/models/iris-custom-requirements.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - mlserver-1.3.4
diff --git a/docs/examples/assets/models/iris-custom-server.yaml b/docs/examples/assets/models/iris-custom-server.yaml
new file mode 100644
index 0000000000..c624d69394
--- /dev/null
+++ b/docs/examples/assets/models/iris-custom-server.yaml
@@ -0,0 +1,7 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  server: mlserver-134
diff --git a/docs/examples/assets/models/iris-drift-detect.yaml b/docs/examples/assets/models/iris-drift-detect.yaml
new file mode 100644
index 0000000000..428d608bbc
--- /dev/null
+++ b/docs/examples/assets/models/iris-drift-detect.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris-drift-detect
+spec:
+  storageUri: "gs://seldon-models/mlserver/alibi-detect/iris-tabular-drift/alibi-detector-artifacts"
+  requirements:
+    - mlserver
+    - alibi-detect
diff --git a/docs/examples/assets/models/iris-v1.yaml b/docs/examples/assets/models/iris-v1.yaml
new file mode 100644
index 0000000000..2ec93ef0a1
--- /dev/null
+++ b/docs/examples/assets/models/iris-v1.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/rolling/iris/v1"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/iris-v2.yaml b/docs/examples/assets/models/iris-v2.yaml
new file mode 100644
index 0000000000..cf74aaa908
--- /dev/null
+++ b/docs/examples/assets/models/iris-v2.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/rolling/iris/v2"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/iris-v3.yaml b/docs/examples/assets/models/iris-v3.yaml
new file mode 100644
index 0000000000..bf1e0d3dc2
--- /dev/null
+++ b/docs/examples/assets/models/iris-v3.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/rolling/iris/v3"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/join_node.yaml b/docs/examples/assets/models/join_node.yaml
new file mode 100644
index 0000000000..7e6568370f
--- /dev/null
+++ b/docs/examples/assets/models/join_node.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: join_node
+spec:
+  storageUri: "gs://seldon-models/triton/join_node"
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/mnist-onnx.yaml b/docs/examples/assets/models/mnist-onnx.yaml
new file mode 100644
index 0000000000..0c88658554
--- /dev/null
+++ b/docs/examples/assets/models/mnist-onnx.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mnist-onnx
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mnist-onnx"
+  requirements:
+  - onnx
diff --git a/docs/examples/assets/models/mnist-pytorch.yaml b/docs/examples/assets/models/mnist-pytorch.yaml
new file mode 100644
index 0000000000..54b9f0c607
--- /dev/null
+++ b/docs/examples/assets/models/mnist-pytorch.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mnist-pytorch
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mnist-pytorch"
+  requirements:
+  - pytorch
diff --git a/docs/examples/assets/models/moviesentiment-explainer.yaml b/docs/examples/assets/models/moviesentiment-explainer.yaml
new file mode 100644
index 0000000000..a74ec5ed1b
--- /dev/null
+++ b/docs/examples/assets/models/moviesentiment-explainer.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/moviesentiment-sklearn-explainer"
+  explainer:
+    type: anchor_text
+    modelRef: sentiment
diff --git a/docs/examples/assets/models/moviesentiment.yaml b/docs/examples/assets/models/moviesentiment.yaml
new file mode 100644
index 0000000000..14a5da0faa
--- /dev/null
+++ b/docs/examples/assets/models/moviesentiment.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/moviesentiment-sklearn"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/mul10.yaml b/docs/examples/assets/models/mul10.yaml
new file mode 100644
index 0000000000..d400fca37f
--- /dev/null
+++ b/docs/examples/assets/models/mul10.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mul10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mul10"
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/multi-version-1.yaml b/docs/examples/assets/models/multi-version-1.yaml
new file mode 100644
index 0000000000..0e0f1619aa
--- /dev/null
+++ b/docs/examples/assets/models/multi-version-1.yaml
@@ -0,0 +1,10 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: math
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/multi-version"
+  artifactVersion: 1
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/multi-version-2.yaml b/docs/examples/assets/models/multi-version-2.yaml
new file mode 100644
index 0000000000..55af34ccaa
--- /dev/null
+++ b/docs/examples/assets/models/multi-version-2.yaml
@@ -0,0 +1,10 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: math
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/multi-version"
+  artifactVersion: 2
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/outlier-error.yaml b/docs/examples/assets/models/outlier-error.yaml
new file mode 100644
index 0000000000..d9cdac421d
--- /dev/null
+++ b/docs/examples/assets/models/outlier-error.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: outlier-error
+spec:
+  storageUri: "gs://seldon-models/triton/outlier"
+  requirements:
+  - triton
+  - python
diff --git a/docs/examples/assets/models/sklearn-iris-gs-baduri.yaml b/docs/examples/assets/models/sklearn-iris-gs-baduri.yaml
new file mode 100644
index 0000000000..6b7cb1cd28
--- /dev/null
+++ b/docs/examples/assets/models/sklearn-iris-gs-baduri.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/iris-notexistent-uri"
+  requirements:
+  - sklearn
+  memory: 100Ki
diff --git a/docs/examples/assets/models/sklearn-iris-gs.yaml b/docs/examples/assets/models/sklearn-iris-gs.yaml
new file mode 100644
index 0000000000..2704864813
--- /dev/null
+++ b/docs/examples/assets/models/sklearn-iris-gs.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
diff --git a/docs/examples/assets/models/sklearn-iris-local.yaml b/docs/examples/assets/models/sklearn-iris-local.yaml
new file mode 100644
index 0000000000..f950b8ebf9
--- /dev/null
+++ b/docs/examples/assets/models/sklearn-iris-local.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "/mnt/models/iris"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/sklearn-iris-minio.yaml b/docs/examples/assets/models/sklearn-iris-minio.yaml
new file mode 100644
index 0000000000..af9d02fb08
--- /dev/null
+++ b/docs/examples/assets/models/sklearn-iris-minio.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "s3://models/iris"
+  secretName: "minio-secret"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/sklearn1.yaml b/docs/examples/assets/models/sklearn1.yaml
new file mode 100644
index 0000000000..e73c64ca5a
--- /dev/null
+++ b/docs/examples/assets/models/sklearn1.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/sklearn2.yaml b/docs/examples/assets/models/sklearn2.yaml
new file mode 100644
index 0000000000..f950f59714
--- /dev/null
+++ b/docs/examples/assets/models/sklearn2.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris2
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/sklearn3.yaml b/docs/examples/assets/models/sklearn3.yaml
new file mode 100644
index 0000000000..c9ce3c0cff
--- /dev/null
+++ b/docs/examples/assets/models/sklearn3.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris3
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
diff --git a/docs/examples/assets/models/tfsimple1.yaml b/docs/examples/assets/models/tfsimple1.yaml
new file mode 100644
index 0000000000..1eb2d6257a
--- /dev/null
+++ b/docs/examples/assets/models/tfsimple1.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
diff --git a/docs/examples/assets/models/tfsimple2.yaml b/docs/examples/assets/models/tfsimple2.yaml
new file mode 100644
index 0000000000..f010b82b4e
--- /dev/null
+++ b/docs/examples/assets/models/tfsimple2.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
diff --git a/docs/examples/assets/models/tfsimple3.yaml b/docs/examples/assets/models/tfsimple3.yaml
new file mode 100644
index 0000000000..423da3b5da
--- /dev/null
+++ b/docs/examples/assets/models/tfsimple3.yaml
@@ -0,0 +1,9 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple3
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
diff --git a/docs/examples/assets/models/tfsimple_scaling.yaml b/docs/examples/assets/models/tfsimple_scaling.yaml
new file mode 100644
index 0000000000..6b94ca9b82
--- /dev/null
+++ b/docs/examples/assets/models/tfsimple_scaling.yaml
@@ -0,0 +1,11 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+  minReplicas: 1
+  replicas: 1
\ No newline at end of file
diff --git a/docs/examples/assets/models/wine-mlflow.yaml b/docs/examples/assets/models/wine-mlflow.yaml
new file mode 100644
index 0000000000..2a2e4598f1
--- /dev/null
+++ b/docs/examples/assets/models/wine-mlflow.yaml
@@ -0,0 +1,8 @@
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: wine
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/wine-mlflow"
+  requirements:
+  - mlflow
diff --git a/docs/examples/batch-examples-k8s.md b/docs/examples/batch-examples-k8s.md
index d0660e9d10..d0c27ac70b 100644
--- a/docs/examples/batch-examples-k8s.md
+++ b/docs/examples/batch-examples-k8s.md
@@ -2,14 +2,719 @@
 
 Requires `mlserver` to be installed.
 
-```{warning}
+{% hint style="warning" %}
 Deprecated: The MLServer CLI `infer` feature is experimental and will be removed in future work.
-```
+{% endhint %}
 
 ```bash
 pip install mlserver
 ```
 
-```{include} ../../../../samples/k8s-batch-examples.md
-:relative-images:
+### Seldon V2 Batch Examples
+
+```python
+import os
+os.environ["NAMESPACE"] = "seldon-mesh"
+```
+
+```python
+MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+MESH_IP=MESH_IP[0]
+import os
+os.environ['MESH_IP'] = MESH_IP
+MESH_IP
+```
+
+```
+'172.18.255.2'
+
+```
+
+## Deploy Models and Pipelines
+
+```bash
+cat models/sklearn-iris-gs.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
+
+```
+
+```bash
+cat pipelines/iris.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: iris-pipeline
+spec:
+  steps:
+    - name: iris
+  output:
+    steps:
+    - iris
+
+```
+
+```bash
+cat models/tfsimple1.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+cat pipelines/tfsimple.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple
+spec:
+  steps:
+    - name: tfsimple1
+  output:
+    steps:
+    - tfsimple1
+
+```
+
+```bash
+kubectl apply -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}
+kubectl apply -f pipelines/iris.yaml -n ${NAMESPACE}
+
+kubectl apply -f models/tfsimple1.yaml -n ${NAMESPACE}
+kubectl apply -f pipelines/tfsimple.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris created
+pipeline.mlops.seldon.io/iris-pipeline created
+model.mlops.seldon.io/tfsimple1 created
+pipeline.mlops.seldon.io/tfsimple created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+kubectl wait --for condition=ready --timeout=300s pipelines --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris condition met
+model.mlops.seldon.io/tfsimple1 condition met
+pipeline.mlops.seldon.io/iris-pipeline condition met
+pipeline.mlops.seldon.io/tfsimple condition met
+
+```
+
+## Test Predictions
+
+```bash
+seldon model infer iris --inference-host ${MESH_IP}:80 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "iris_1",
+  "model_version": "1",
+  "id": "25e1c1b9-a20f-456d-bdff-c75d5ba83b1f",
+  "parameters": {},
+  "outputs": [
+    {
+      "name": "predict",
+      "shape": [
+        1,
+        1
+      ],
+      "datatype": "INT64",
+      "parameters": {
+        "content_type": "np"
+      },
+      "data": [
+        2
+      ]
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer iris-pipeline --inference-host ${MESH_IP}:80 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}' |  jq -M .
+```
+
+```json
+{
+  "model_name": "",
+  "outputs": [
+    {
+      "data": [
+        2
+      ],
+      "name": "predict",
+      "shape": [
+        1,
+        1
+      ],
+      "datatype": "INT64",
+      "parameters": {
+        "content_type": "np"
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon model infer tfsimple1 --inference-host ${MESH_IP}:80 \
+  '{"outputs":[{"name":"OUTPUT0"}], "inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "tfsimple1_1",
+  "model_version": "1",
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        1,
+        16
+      ],
+      "data": [
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+        14,
+        16,
+        18,
+        20,
+        22,
+        24,
+        26,
+        28,
+        30,
+        32
+      ]
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimple --inference-host ${MESH_IP}:80 \
+  '{"outputs":[{"name":"OUTPUT0"}], "inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "",
+  "outputs": [
+    {
+      "data": [
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+        14,
+        16,
+        18,
+        20,
+        22,
+        24,
+        26,
+        28,
+        30,
+        32
+      ],
+      "name": "OUTPUT0",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    },
+    {
+      "data": [
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+      ],
+      "name": "OUTPUT1",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    }
+  ]
+}
+
+```
+
+## MLServer Iris Batch Job
+
+```bash
+cat batch-inputs/iris-input.txt | head -n 1 | jq -M .
+```
+
+```json
+{
+  "inputs": [
+    {
+      "name": "predict",
+      "data": [
+        0.38606369295833043,
+        0.006894049558299753,
+        0.6104082981607108,
+        0.3958954239450676
+      ],
+      "datatype": "FP64",
+      "shape": [
+        1,
+        4
+      ]
+    }
+  ]
+}
+
+```
+
+```bash
+%%bash
+mlserver infer -u ${MESH_IP} -m iris -i batch-inputs/iris-input.txt -o /tmp/iris-output.txt --workers 5
+
+```
+
+```
+2023-06-30 11:05:32,389 [mlserver] INFO - server url: 172.18.255.2
+2023-06-30 11:05:32,389 [mlserver] INFO - model name: iris
+2023-06-30 11:05:32,389 [mlserver] INFO - request headers: {}
+2023-06-30 11:05:32,389 [mlserver] INFO - input file path: batch-inputs/iris-input.txt
+2023-06-30 11:05:32,389 [mlserver] INFO - output file path: /tmp/iris-output.txt
+2023-06-30 11:05:32,389 [mlserver] INFO - workers: 5
+2023-06-30 11:05:32,389 [mlserver] INFO - retries: 3
+2023-06-30 11:05:32,389 [mlserver] INFO - batch interval: 0.0
+2023-06-30 11:05:32,389 [mlserver] INFO - batch jitter: 0.0
+2023-06-30 11:05:32,389 [mlserver] INFO - connection timeout: 60
+2023-06-30 11:05:32,389 [mlserver] INFO - micro-batch size: 1
+2023-06-30 11:05:32,503 [mlserver] INFO - Finalizer: processed instances: 100
+2023-06-30 11:05:32,503 [mlserver] INFO - Total processed instances: 100
+2023-06-30 11:05:32,503 [mlserver] INFO - Time taken: 0.11 seconds
+
+```
+
+```bash
+%%bash
+mlserver infer -u ${MESH_IP} -m iris-pipeline.pipeline -i batch-inputs/iris-input.txt -o /tmp/iris-pipeline-output.txt --workers 5
+
+```
+
+```
+2023-06-30 11:05:35,857 [mlserver] INFO - server url: 172.18.255.2
+2023-06-30 11:05:35,858 [mlserver] INFO - model name: iris-pipeline.pipeline
+2023-06-30 11:05:35,858 [mlserver] INFO - request headers: {}
+2023-06-30 11:05:35,858 [mlserver] INFO - input file path: batch-inputs/iris-input.txt
+2023-06-30 11:05:35,858 [mlserver] INFO - output file path: /tmp/iris-pipeline-output.txt
+2023-06-30 11:05:35,858 [mlserver] INFO - workers: 5
+2023-06-30 11:05:35,858 [mlserver] INFO - retries: 3
+2023-06-30 11:05:35,858 [mlserver] INFO - batch interval: 0.0
+2023-06-30 11:05:35,858 [mlserver] INFO - batch jitter: 0.0
+2023-06-30 11:05:35,858 [mlserver] INFO - connection timeout: 60
+2023-06-30 11:05:35,858 [mlserver] INFO - micro-batch size: 1
+2023-06-30 11:05:36,145 [mlserver] INFO - Finalizer: processed instances: 100
+2023-06-30 11:05:36,146 [mlserver] INFO - Total processed instances: 100
+2023-06-30 11:05:36,146 [mlserver] INFO - Time taken: 0.29 seconds
+
+```
+
+```bash
+cat /tmp/iris-output.txt | head -n 1 | jq -M .
+```
+
+```json
+{
+  "model_name": "iris_1",
+  "model_version": "1",
+  "id": "46bdfca2-8805-4a72-b1ce-95e4f38c1a19",
+  "parameters": {
+    "inference_id": "46bdfca2-8805-4a72-b1ce-95e4f38c1a19",
+    "batch_index": 0
+  },
+  "outputs": [
+    {
+      "name": "predict",
+      "shape": [
+        1,
+        1
+      ],
+      "datatype": "INT64",
+      "parameters": {
+        "content_type": "np"
+      },
+      "data": [
+        1
+      ]
+    }
+  ]
+}
+
+```
+
+```bash
+cat /tmp/iris-pipeline-output.txt | head -n 1 | jq .
+```
+
+```json
+{
+  "model_name": "",
+  "id": "37e8c013-b348-41e8-89b9-fea86a4f9632",
+  "parameters": {
+    "batch_index": 1
+  },
+  "outputs": [
+    {
+      "name": "predict",
+      "shape": [
+        1,
+        1
+      ],
+      "datatype": "INT64",
+      "parameters": {
+        "content_type": "np"
+      },
+      "data": [
+        1
+      ]
+    }
+  ]
+}
+
+```
+
+## Triton TFSimple Batch Job
+
+```bash
+cat batch-inputs/tfsimple-input.txt | head -n 1 | jq -M .
+```
+
+```json
+{
+  "inputs": [
+    {
+      "name": "INPUT0",
+      "data": [
+        75,
+        39,
+        9,
+        44,
+        32,
+        97,
+        99,
+        40,
+        13,
+        27,
+        25,
+        36,
+        18,
+        77,
+        62,
+        60
+      ],
+      "datatype": "INT32",
+      "shape": [
+        1,
+        16
+      ]
+    },
+    {
+      "name": "INPUT1",
+      "data": [
+        39,
+        7,
+        14,
+        58,
+        13,
+        88,
+        98,
+        66,
+        97,
+        57,
+        49,
+        3,
+        49,
+        63,
+        37,
+        12
+      ],
+      "datatype": "INT32",
+      "shape": [
+        1,
+        16
+      ]
+    }
+  ]
+}
+
+```
+
+```bash
+%%bash
+mlserver infer -u ${MESH_IP} -m tfsimple1 -i batch-inputs/tfsimple-input.txt -o /tmp/tfsimple-output.txt --workers 5 -b
+
+```
+
+```
+2023-06-30 11:22:52,662 [mlserver] INFO - server url: 172.18.255.2
+2023-06-30 11:22:52,662 [mlserver] INFO - model name: tfsimple1
+2023-06-30 11:22:52,662 [mlserver] INFO - request headers: {}
+2023-06-30 11:22:52,662 [mlserver] INFO - input file path: batch-inputs/tfsimple-input.txt
+2023-06-30 11:22:52,662 [mlserver] INFO - output file path: /tmp/tfsimple-output.txt
+2023-06-30 11:22:52,662 [mlserver] INFO - workers: 5
+2023-06-30 11:22:52,662 [mlserver] INFO - retries: 3
+2023-06-30 11:22:52,662 [mlserver] INFO - batch interval: 0.0
+2023-06-30 11:22:52,662 [mlserver] INFO - batch jitter: 0.0
+2023-06-30 11:22:52,662 [mlserver] INFO - connection timeout: 60
+2023-06-30 11:22:52,662 [mlserver] INFO - micro-batch size: 1
+2023-06-30 11:22:52,755 [mlserver] INFO - Finalizer: processed instances: 100
+2023-06-30 11:22:52,755 [mlserver] INFO - Total processed instances: 100
+2023-06-30 11:22:52,756 [mlserver] INFO - Time taken: 0.09 seconds
+
+```
+
+```bash
+%%bash
+mlserver infer -u ${MESH_IP} -m tfsimple.pipeline -i batch-inputs/tfsimple-input.txt -o /tmp/tfsimple-pipeline-output.txt --workers 5
+
+```
+
+```
+2023-06-30 11:22:54,065 [mlserver] INFO - server url: 172.18.255.2
+2023-06-30 11:22:54,065 [mlserver] INFO - model name: tfsimple.pipeline
+2023-06-30 11:22:54,065 [mlserver] INFO - request headers: {}
+2023-06-30 11:22:54,065 [mlserver] INFO - input file path: batch-inputs/tfsimple-input.txt
+2023-06-30 11:22:54,065 [mlserver] INFO - output file path: /tmp/tfsimple-pipeline-output.txt
+2023-06-30 11:22:54,065 [mlserver] INFO - workers: 5
+2023-06-30 11:22:54,065 [mlserver] INFO - retries: 3
+2023-06-30 11:22:54,065 [mlserver] INFO - batch interval: 0.0
+2023-06-30 11:22:54,065 [mlserver] INFO - batch jitter: 0.0
+2023-06-30 11:22:54,065 [mlserver] INFO - connection timeout: 60
+2023-06-30 11:22:54,065 [mlserver] INFO - micro-batch size: 1
+2023-06-30 11:22:54,302 [mlserver] INFO - Finalizer: processed instances: 100
+2023-06-30 11:22:54,302 [mlserver] INFO - Total processed instances: 100
+2023-06-30 11:22:54,303 [mlserver] INFO - Time taken: 0.24 seconds
+
+```
+
+```bash
+cat /tmp/tfsimple-output.txt | head -n 1 | jq -M .
+```
+
+```json
+{
+  "model_name": "tfsimple1_1",
+  "model_version": "1",
+  "id": "19952272-b023-4079-aa08-f1880ded05e5",
+  "parameters": {
+    "inference_id": "19952272-b023-4079-aa08-f1880ded05e5",
+    "batch_index": 1
+  },
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32",
+      "parameters": {},
+      "data": [
+        115,
+        69,
+        97,
+        112,
+        73,
+        106,
+        58,
+        182,
+        114,
+        66,
+        64,
+        110,
+        100,
+        24,
+        22,
+        77
+      ]
+    },
+    {
+      "name": "OUTPUT1",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32",
+      "parameters": {},
+      "data": [
+        -77,
+        33,
+        25,
+        -52,
+        -49,
+        -88,
+        -48,
+        0,
+        -50,
+        26,
+        -44,
+        46,
+        -2,
+        18,
+        -6,
+        -47
+      ]
+    }
+  ]
+}
+
+```
+
+```bash
+cat /tmp/tfsimple-pipeline-output.txt | head -n 1 | jq -M .
+```
+
+```json
+{
+  "model_name": "",
+  "id": "46b05aab-07d9-414d-be96-c03d1863552a",
+  "parameters": {
+    "batch_index": 3
+  },
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32",
+      "data": [
+        140,
+        164,
+        85,
+        58,
+        152,
+        76,
+        70,
+        56,
+        100,
+        141,
+        98,
+        181,
+        115,
+        177,
+        106,
+        193
+      ]
+    },
+    {
+      "name": "OUTPUT1",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32",
+      "data": [
+        -10,
+        0,
+        -11,
+        -38,
+        2,
+        -36,
+        -52,
+        -8,
+        -18,
+        57,
+        94,
+        -5,
+        -27,
+        17,
+        58,
+        -1
+      ]
+    }
+  ]
+}
+
+```
+
+## Cleanup
+
+```bash
+kubectl delete -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}
+kubectl delete -f pipelines/iris.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "iris" deleted
+pipeline.mlops.seldon.io "iris-pipeline" deleted
+
+```
+
+```bash
+kubectl delete -f models/tfsimple1.yaml -n ${NAMESPACE}
+kubectl delete -f pipelines/tfsimple.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "tfsimple1" deleted
+pipeline.mlops.seldon.io "tfsimple" deleted
+
 ```
diff --git a/docs/examples/batch-examples-local.md b/docs/examples/batch-examples-local.md
index e62115b8ac..7553478e70 100644
--- a/docs/examples/batch-examples-local.md
+++ b/docs/examples/batch-examples-local.md
@@ -2,21 +2,24 @@
 
 This example runs you through a series of batch inference requests made to both models and pipelines running on Seldon Core locally.
 
-```{warning}
+{% hint style="warning" %}
 Deprecated: The MLServer CLI `infer` feature is experimental and will be removed in future work.
-```
+{% endhint %}
 
 ## Setup
 
-If you haven't already, you'll need to [clone the Seldon Core repository and run it locally](../getting-started/docker-installation/index) before you run through this example.
+If you haven't already, you'll need to [clone the Seldon Core repository and run it locally](../getting-started/docker-installation.md)
+before you run through this example.
 
-```{important}
-By default, the CLI will expect your inference endpoint to be at `0.0.0.0:9000`. If you have customized this, you'll need to [redirect the CLI](../cli/index).
-```
+{% hint style="info" %}
+By default, the CLI will expect your inference endpoint to be at `0.0.0.0:9000`. If you have
+customized this, you'll need to [redirect the CLI](../cli/README.md).
+{% endhint %}
 
 ## Deploy Models and Pipelines
 
-First, let's jump in to the `samples` folder where we'll find some sample models and pipelines we can use:
+First, let's jump in to the `samples` folder where we'll find some sample models and pipelines
+we can use:
 
 ```bash
 cd samples/
@@ -41,7 +44,8 @@ spec:
   memory: 100Ki
 ```
 
-The above manifest will deploy a simple [sci-kit learn](https://scikit-learn.org/stable/) model based on the [iris dataset](https://archive.ics.uci.edu/ml/datasets/iris).
+The above manifest will deploy a simple [sci-kit learn](https://scikit-learn.org/stable/) model based on
+the [iris dataset](https://archive.ics.uci.edu/ml/datasets/iris).
 
 Let's now deploy that model using the Seldon CLI:
 
@@ -51,7 +55,7 @@ seldon model load -f models/sklearn-iris-gs.yaml
 
 ### Deploy the Iris Pipeline
 
-Now that we've deployed our iris model, let's create a [pipeline](../pipelines/index) around the model.
+Now that we've deployed our iris model, let's create a [pipeline](../pipelines.md) around the model.
 
 ```bash
 cat pipelines/iris.yaml
@@ -69,7 +73,8 @@ spec:
     - iris
 ```
 
-We see that this pipeline only has one step, which is to call the `iris` model we deployed earlier. We can create the pipeline by running:
+We see that this pipeline only has one step, which is to call the `iris` model we deployed
+earlier. We can create the pipeline by running:
 
 ```bash
 seldon pipeline load -f pipelines/iris.yaml
@@ -77,7 +82,8 @@ seldon pipeline load -f pipelines/iris.yaml
 
 ### Deploy the Tensorflow Model
 
-To demonstrate batch inference requests to different types of models, we'll also deploy a simple [tensorflow](https://www.tensorflow.org/) model:
+To demonstrate batch inference requests to different types of models, we'll also deploy a
+simple [tensorflow](https://www.tensorflow.org/) model:
 
 ```bash
 cat models/tfsimple1.yaml
@@ -95,7 +101,8 @@ spec:
 
 ```
 
-The tensorflow model takes two arrays as inputs and returns two arrays as outputs. The first output is the addition of the two inputs and the second output is the value of (first input - second input).
+The tensorflow model takes two arrays as inputs and returns two arrays as outputs. The first
+output is the addition of the two inputs and the second output is the value of (first input - second input).
 
 Let's deploy the model:
 
@@ -131,7 +138,8 @@ seldon pipeline load -f pipelines/tfsimple.yaml
 
 ### Check Model and Pipeline Status
 
-Once we've deployed a model or pipeline to Seldon Core, we can list them and check their status by running:
+Once we've deployed a model or pipeline to Seldon Core, we can list them and check their status
+by running:
 
 ```bash
 seldon model list
@@ -145,7 +153,9 @@ Your models and pieplines should be showing a state of `ModelAvailable` and `Pip
 
 ## Test Predictions
 
-Before we run a large batch job of predictions through our models and pipelines, let's quickly check that they work with a single standalone inference request. We can do this using the `seldon model infer` command.
+Before we run a large batch job of predictions through our models and pipelines, let's quickly
+check that they work with a single standalone inference request. We can do this using the
+`seldon model infer` command.
 
 ### Scikit-learn Model
 ```bash
@@ -177,7 +187,9 @@ seldon model infer iris '{"inputs": [{"name": "predict", "shape": [1, 4], "datat
 
 ```
 
-The preidiction request body needs to be an [Open Inference Protocol](../apis/inference/v2.md) compatible payload and also match the expected inputs for the model you've deployed. In this case, the iris model expects data of shape `[1, 4]` and of type `FP32`.
+The preidiction request body needs to be an [Open Inference Protocol](../apis/inference/v2.md)
+compatible payload and also match the expected inputs for the model you've deployed. In this case,
+the iris model expects data of shape `[1, 4]` and of type `FP32`.
 
 You'll notice that the prediction results for this request come back on `outputs[0].data`.
 
@@ -245,7 +257,9 @@ seldon model infer tfsimple1 '{"outputs":[{"name":"OUTPUT0"}], "inputs":[{"name"
 }
 ```
 
-You'll notice that the inputs for our tensorflow model look different from the ones we sent to the iris model. This time, we're sending two arrays of shape `[1,16]`. When sending an inference request, we can optionally chose which outputs we want back by including an `{"outputs":...}` object.
+You'll notice that the inputs for our tensorflow model look different from the ones we sent to the
+iris model. This time, we're sending two arrays of shape `[1,16]`. When sending an inference request,
+we can optionally chose which outputs we want back by including an `{"outputs":...}` object.
 
 ### Tensorflow Pipeline
 
@@ -316,7 +330,8 @@ seldon pipeline infer tfsimple '"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7
 
 ## Running the Scikit-Learn Batch Job
 
-In the samples folder there is a batch request input file: `batch-inputs/iris-input.txt`. It contains 100 input payloads for our iris model. Let's take a look at the first line in that file:
+In the samples folder there is a batch request input file: `batch-inputs/iris-input.txt`. It contains
+100 input payloads for our iris model. Let's take a look at the first line in that file:
 
 ```bash
 cat batch-inputs/iris-input.txt | head -n 1 | jq
@@ -374,7 +389,10 @@ mlserver infer -u localhost:9000 -m iris -i batch-inputs/iris-input.txt -o /tmp/
 
 ```
 
-The mlserver batch component will take your input file `batch-inputs/iris-input.txt`, distribute those payloads across 5 different workers (`--workers 5`), collect the responses and write them to a file `/tmp/iris-output.txt`. For a full set of options check out the [MLServer CLI Reference](https://mlserver.readthedocs.io/en/latest/reference/cli.html#mlserver-infer).
+The mlserver batch component will take your input file `batch-inputs/iris-input.txt`, distribute
+those payloads across 5 different workers (`--workers 5`), collect the responses and write them
+to a file `/tmp/iris-output.txt`. For a full set of options check out the
+[MLServer CLI Reference](https://mlserver.readthedocs.io/en/latest/reference/cli.html#mlserver-infer).
 
 #### Checking the Output
 
@@ -417,7 +435,9 @@ cat /tmp/iris-pipeline-output.txt | head -n 1 | jq
 
 ## Running the Tensorflow Batch Job
 
-The samples folder contains an example batch input for the tensorflow model, just as it did for the scikit-learn model. You can find it at `batch-inputs/tfsimple-input.txt`. Let's take a look at the first inference request in the file:
+The samples folder contains an example batch input for the tensorflow model, just as it did for
+the scikit-learn model. You can find it at `batch-inputs/tfsimple-input.txt`. Let's take a look
+at the first inference request in the file:
 
 ```bash
 cat batch-inputs/tfsimple-input.txt | head -n 1 | jq
diff --git a/docs/examples/cifar10.md b/docs/examples/cifar10.md
index a440eeb0fc..07c0dc2ae2 100644
--- a/docs/examples/cifar10.md
+++ b/docs/examples/cifar10.md
@@ -1,8 +1,553 @@
-# Production Image Classifier 
+# Production Image Classifier
 
 Run these examples from the `samples/examples/image_classifier` folder.
 
+## CIFAR10 Image Classification Production Deployment
 
-```{include} ../../../../samples/examples/image_classifier/infer.md
-:relative-images:
+![cifar10](../images/demo.png)
+
+We show an image classifier (CIFAR10) with associated outlier and drift detectors using a Pipeline.
+
+* The model is a tensorflow [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) image classfier
+* The outlier detector is created from the [CIFAR10 VAE Outlier example](https://docs.seldon.io/projects/alibi-detect/en/stable/examples/od_vae_cifar10.html).
+* The drift detector is created from the [CIFAR10 KS Drift example](https://docs.seldon.io/projects/alibi-detect/en/stable/examples/cd_ks_cifar10.html)
+
+### Model Training (optional for notebook)
+
+To run local training run the [training notebook](notebooks/train.ipynb).
+
+```python
+import requests
+import json
+from typing import Dict, List
+import numpy as np
+import os
+import tensorflow as tf
+from alibi_detect.utils.perturbation import apply_mask
+from alibi_detect.datasets import fetch_cifar10c
+import matplotlib.pyplot as plt
+tf.keras.backend.clear_session()
+```
+
+```
+2023-06-30 15:39:28.732453: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2023-06-30 15:39:28.732465: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+
+```
+
+```python
+train, test = tf.keras.datasets.cifar10.load_data()
+X_train, y_train = train
+X_test, y_test = test
+
+X_train = X_train.astype('float32') / 255
+X_test = X_test.astype('float32') / 255
+print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
+classes = (
+    "plane",
+    "car",
+    "bird",
+    "cat",
+    "deer",
+    "dog",
+    "frog",
+    "horse",
+    "ship",
+    "truck",
+)
+
+```
+
+```
+(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)
+
+```
+
+```python
+outliers = []
+for idx in range(0,X_train.shape[0]):
+    X_mask, mask = apply_mask(X_train[idx].reshape(1, 32, 32, 3),
+                                  mask_size=(14,14),
+                                  n_masks=1,
+                                  channels=[0,1,2],
+                                  mask_type='normal',
+                                  noise_distr=(0,1),
+                                  clip_rng=(0,1))
+    outliers.append(X_mask)
+X_outliers = np.vstack(outliers)
+X_outliers.shape
+```
+
+```
+(50000, 32, 32, 3)
+
+```
+
+```python
+corruption = ['brightness']
+X_corr, y_corr = fetch_cifar10c(corruption=corruption, severity=5, return_X_y=True)
+X_corr = X_corr.astype('float32') / 255
+```
+
+```python
+reqJson = json.loads('{"inputs":[{"name":"input_1","data":[],"datatype":"FP32","shape":[]}]}')
+url = "http://0.0.0.0:9000/v2/models/model/infer"
+```
+
+```python
+def infer(resourceName: str, batchSz: int, requestType: str):
+    if requestType == "outlier":
+        rows = X_outliers[0:0+batchSz]
+    elif requestType == "drift":
+        rows = X_corr[0:0+batchSz]
+    else:
+        rows = X_train[0:0+batchSz]
+    for i in range(batchSz):
+        show(rows[i])
+    reqJson["inputs"][0]["data"] = rows.flatten().tolist()
+    reqJson["inputs"][0]["shape"] = [batchSz, 32, 32, 3]
+    headers = {"Content-Type": "application/json", "seldon-model":resourceName}
+    response_raw = requests.post(url, json=reqJson, headers=headers)
+    print(response_raw)
+    print(response_raw.json())
+
+
+def show(X):
+    plt.imshow(X.reshape(32, 32, 3))
+    plt.axis("off")
+    plt.show()
+
+```
+
+### Pipeline
+
+```bash
+cat ../../models/cifar10.yaml
+echo "---"
+cat ../../models/cifar10-outlier-detect.yaml
+echo "---"
+cat ../../models/cifar10-drift-detect.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: cifar10
+spec:
+  storageUri: "gs://seldon-models/triton/tf_cifar10"
+  requirements:
+  - tensorflow
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: cifar10-outlier
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/cifar10/outlier-detector"
+  requirements:
+    - mlserver
+    - alibi-detect
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: cifar10-drift
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/cifar10/drift-detector"
+  requirements:
+    - mlserver
+    - alibi-detect
+
+```
+
+```bash
+seldon model load -f ../../models/cifar10.yaml
+seldon model load -f ../../models/cifar10-outlier-detect.yaml
+seldon model load -f ../../models/cifar10-drift-detect.yaml
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+```bash
+seldon model status cifar10 -w ModelAvailable | jq .
+seldon model status cifar10-outlier -w ModelAvailable | jq .
+seldon model status cifar10-drift -w ModelAvailable | jq .
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+```bash
+cat ../../pipelines/cifar10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: cifar10-production
+spec:
+  steps:
+    - name: cifar10
+    - name: cifar10-outlier
+    - name: cifar10-drift
+      batch:
+        size: 20
+  output:
+    steps:
+    - cifar10
+    - cifar10-outlier.outputs.is_outlier
+
+```
+
+```bash
+seldon pipeline load -f ../../pipelines/cifar10.yaml
+```
+
+```bash
+seldon pipeline status cifar10-production -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "cifar10-production",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "cifar10-production",
+        "uid": "cifeii2ufmbc73e5insg",
+        "version": 1,
+        "steps": [
+          {
+            "name": "cifar10"
+          },
+          {
+            "name": "cifar10-drift",
+            "batch": {
+              "size": 20
+            }
+          },
+          {
+            "name": "cifar10-outlier"
+          }
+        ],
+        "output": {
+          "steps": [
+            "cifar10.outputs",
+            "cifar10-outlier.outputs.is_outlier"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-30T14:40:09.047429817Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```python
+infer("cifar10-production.pipeline",20, "normal")
+```
+
+```
+![png](infer_files/infer_14_0.png)
+
+```
+
+```
+![png](infer_files/infer_14_1.png)
+
+```
+
+```
+![png](infer_files/infer_14_2.png)
+
+```
+
+```
+![png](infer_files/infer_14_3.png)
+
+```
+
+```
+![png](infer_files/infer_14_4.png)
+
+```
+
+```
+![png](infer_files/infer_14_5.png)
+
+```
+
+```
+![png](infer_files/infer_14_6.png)
+
+```
+
+```
+![png](infer_files/infer_14_7.png)
+
+```
+
+```
+![png](infer_files/infer_14_8.png)
+
+```
+
+```
+![png](infer_files/infer_14_9.png)
+
+```
+
+```
+![png](infer_files/infer_14_10.png)
+
+```
+
+```
+![png](infer_files/infer_14_11.png)
+
+```
+
+```
+![png](infer_files/infer_14_12.png)
+
+```
+
+```
+![png](infer_files/infer_14_13.png)
+
+```
+
+```
+![png](infer_files/infer_14_14.png)
+
+```
+
+```
+![png](infer_files/infer_14_15.png)
+
+```
+
+```
+![png](infer_files/infer_14_16.png)
+
+```
+
+```
+![png](infer_files/infer_14_17.png)
+
+```
+
+```
+![png](infer_files/infer_14_18.png)
+
+```
+
+```
+![png](infer_files/infer_14_19.png)
+
+```
+
+```
+<Response [200]>
+{'model_name': '', 'outputs': [{'data': [1.45001495e-08, 1.2525752e-09, 1.6298458e-07, 0.11529388, 1.7431412e-07, 6.1856604e-06, 0.8846994, 6.0739285e-09, 7.437921e-08, 4.7317337e-09, 1.26449e-06, 4.8814868e-09, 1.5153439e-09, 8.490656e-09, 5.5131194e-10, 1.1617216e-09, 5.7729294e-10, 2.8839776e-07, 0.0006149016, 0.99938357, 0.888746, 2.5331951e-06, 0.00012967695, 0.10531583, 2.4284174e-05, 6.3332986e-06, 0.0016261435, 1.13079e-05, 0.0013286703, 0.0028091935, 2.0993439e-06, 3.680449e-08, 0.0013269952, 2.1766558e-05, 0.99841356, 0.00015300694, 6.9472035e-06, 1.3277059e-05, 6.1860555e-05, 3.4072806e-07, 1.1205097e-05, 0.99997175, 1.9948227e-07, 6.9880834e-08, 3.3387135e-08, 5.2603138e-08, 3.0352305e-07, 4.3738982e-08, 5.3243946e-07, 1.5870584e-05, 0.0006525102, 0.013322109, 1.480307e-06, 0.9766325, 4.9847167e-05, 0.00058075984, 0.008405659, 5.2234273e-06, 0.00023390084, 0.000116047224, 1.6682397e-06, 5.7737526e-10, 0.9975605, 6.45564e-05, 0.002371972, 1.0392675e-07, 9.747962e-08, 1.4484569e-07, 8.762438e-07, 2.4758325e-08, 5.028761e-09, 6.856381e-11, 5.9932094e-12, 4.921233e-10, 1.471166e-07, 2.7940719e-06, 3.4563383e-09, 0.99999714, 5.9420524e-10, 9.445026e-11, 4.1854888e-05, 5.041549e-08, 8.0302314e-08, 1.2119854e-07, 6.781646e-09, 1.2616152e-08, 1.1878505e-08, 1.628573e-09, 0.9999578, 3.281738e-08, 0.08930307, 1.4065135e-07, 4.1117343e-07, 0.90898305, 8.933351e-07, 0.0015637449, 0.00013868928, 9.092981e-06, 4.8759745e-07, 4.3976044e-07, 0.00016094849, 3.5653954e-07, 0.0760521, 0.8927447, 0.0011777573, 0.00265573, 0.027189083, 4.1892267e-06, 1.329405e-05, 1.8564688e-06, 1.3373891e-06, 1.0251247e-07, 8.651912e-09, 4.458202e-06, 1.4646349e-05, 1.260957e-06, 1.046087e-08, 0.9998946, 8.332438e-05, 3.900894e-07, 6.53852e-05, 3.012202e-08, 1.0247197e-07, 1.8824371e-06, 0.0004958526, 3.533475e-05, 2.739997e-07, 0.99939275, 4.840305e-06, 3.5346695e-06, 0.0005518078, 3.1597017e-07, 0.99902296, 0.00031509742, 8.07886e-07, 1.6366084e-06, 2.795575e-06, 6.112367e-06, 9.817249e-05, 2.602709e-07, 0.0004561966, 5.360607e-06, 2.8656412e-05, 0.000116040654, 6.881144e-05, 8.844774e-06, 4.4655946e-05, 3.5564542e-05, 0.006564381, 0.9926715, 0.007300911, 1.766928e-06, 3.0520596e-07, 0.026906287, 1.3769699e-06, 0.00027539674, 5.583593e-06, 3.792553e-06, 0.0003876767, 0.9651169, 0.18114138, 2.8360228e-05, 0.00019927241, 0.007685872, 0.00014663498, 3.9361137e-05, 5.941682e-05, 7.36174e-05, 0.79936546, 0.01126067, 2.3992783e-11, 7.6336457e-16, 1.4644799e-15, 1, 2.4652159e-14, 1.1786078e-10, 1.9402116e-13, 4.2408636e-15, 1.209294e-15, 2.9042784e-15, 1.5366902e-08, 1.2476195e-09, 1.3560152e-07, 0.999997, 4.3113017e-11, 2.8163534e-08, 2.4494727e-06, 1.3122828e-10, 3.8081083e-07, 2.1628158e-11, 0.0004926238, 6.9424555e-06, 2.827196e-05, 0.92534137, 9.500486e-06, 0.00036133997, 0.072713904, 1.2831057e-07, 0.0010457055, 2.8514464e-07], 'name': 'fc10', 'shape': [20, 10], 'datatype': 'FP32'}, {'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'name': 'is_outlier', 'shape': [1, 20], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}]}
+
+```
+
+```bash
+seldon pipeline inspect cifar10-production.cifar10-drift.outputs.is_drift
+```
+
+```
+seldon.default.model.cifar10-drift.outputs	cifeij8fh5ss738i5bp0	{"name":"is_drift", "datatype":"INT64", "shape":["1", "1"], "parameters":{"content_type":{"stringParam":"np"}}, "contents":{"int64Contents":["0"]}}
+
+```
+
+```python
+infer("cifar10-production.pipeline",20, "drift")
+```
+
+```
+![png](infer_files/infer_16_0.png)
+
+```
+
+```
+![png](infer_files/infer_16_1.png)
+
+```
+
+```
+![png](infer_files/infer_16_2.png)
+
+```
+
+```
+![png](infer_files/infer_16_3.png)
+
+```
+
+```
+![png](infer_files/infer_16_4.png)
+
+```
+
+```
+![png](infer_files/infer_16_5.png)
+
+```
+
+```
+![png](infer_files/infer_16_6.png)
+
+```
+
+```
+![png](infer_files/infer_16_7.png)
+
+```
+
+```
+![png](infer_files/infer_16_8.png)
+
+```
+
+```
+![png](infer_files/infer_16_9.png)
+
+```
+
+```
+![png](infer_files/infer_16_10.png)
+
+```
+
+```
+![png](infer_files/infer_16_11.png)
+
+```
+
+```
+![png](infer_files/infer_16_12.png)
+
+```
+
+```
+![png](infer_files/infer_16_13.png)
+
+```
+
+```
+![png](infer_files/infer_16_14.png)
+
+```
+
+```
+![png](infer_files/infer_16_15.png)
+
+```
+
+```
+![png](infer_files/infer_16_16.png)
+
+```
+
+```
+![png](infer_files/infer_16_17.png)
+
+```
+
+```
+![png](infer_files/infer_16_18.png)
+
+```
+
+```
+![png](infer_files/infer_16_19.png)
+
+```
+
+```
+<Response [200]>
+{'model_name': '', 'outputs': [{'data': [8.080701e-09, 2.3025173e-12, 2.2681688e-09, 1, 4.1828953e-11, 4.48467e-09, 3.216822e-08, 2.8404365e-13, 5.217064e-09, 3.3497323e-13, 0.96965235, 4.7030144e-06, 1.6964266e-07, 1.7355454e-05, 2.6667e-06, 1.9505828e-06, 1.1363079e-07, 3.3352034e-08, 0.030320557, 1.7086056e-07, 0.03725602, 6.8623276e-06, 7.5557014e-05, 0.00018132397, 2.2838503e-05, 0.000110639296, 2.3732607e-06, 2.1210687e-06, 0.9623351, 7.131072e-06, 0.999079, 4.207448e-09, 1.5788535e-08, 2.723756e-08, 2.6555508e-11, 2.1526697e-10, 2.7599315e-10, 2.0737433e-10, 0.0009210062, 3.0885383e-09, 6.665241e-07, 1.7765576e-09, 1.4911559e-07, 0.9765331, 1.9476123e-07, 2.8244015e-06, 0.023463126, 5.8030287e-09, 3.243206e-09, 1.12179785e-08, 4.4123663e-06, 4.7628927e-09, 1.1727273e-08, 0.9761534, 1.1409252e-08, 8.922882e-05, 0.023752932, 3.1563903e-08, 2.7916305e-09, 8.7746266e-10, 1.0166265e-05, 0.999703, 4.5408615e-05, 0.00022673907, 1.7365853e-07, 1.0147362e-06, 6.253448e-06, 2.9711526e-07, 7.811687e-07, 6.183683e-06, 0.86618125, 5.47548e-07, 0.00038408802, 0.013155022, 3.6916779e-06, 0.0006137024, 0.11965008, 3.6425424e-06, 6.7638084e-06, 1.2372367e-06, 1.9545263e-05, 1.1281859e-13, 1.6811868e-14, 0.9999777, 1.9805435e-11, 2.7563674e-06, 2.9651657e-09, 1.1363432e-12, 2.9902746e-13, 1.220973e-12, 2.9895918e-05, 3.4964305e-07, 1.1331837e-08, 1.7012125e-06, 3.6088227e-07, 3.035954e-08, 2.2102333e-06, 1.7414077e-08, 0.9999455, 1.9921794e-05, 0.9999999, 5.3446598e-11, 6.3188843e-10, 1.0956511e-07, 1.1538642e-10, 8.113561e-10, 4.7179572e-08, 1.4544753e-11, 5.490219e-08, 1.3347151e-10, 1.5363307e-07, 6.604881e-09, 2.424105e-10, 9.963063e-09, 3.9349533e-09, 1.5709017e-09, 7.705774e-10, 4.8085802e-08, 1.8885139e-05, 0.9999809, 7.147243e-08, 3.143131e-13, 2.1447092e-13, 0.00042652222, 6.945973e-12, 0.9995734, 6.174434e-09, 4.1128205e-11, 3.4031404e-13, 8.573159e-15, 1.2226405e-09, 2.3768018e-10, 2.822187e-07, 8.016278e-08, 4.0692296e-08, 6.8023346e-06, 2.3926754e-07, 0.9999925, 6.652648e-09, 7.743497e-09, 7.6360675e-06, 5.9386625e-09, 1.5675019e-09, 2.136716e-07, 1.3074002e-06, 3.700079e-10, 1.0984521e-09, 6.2138824e-08, 0.9609078, 0.03908287, 0.0008332255, 7.696685e-08, 2.4428939e-09, 7.186676e-05, 1.4520063e-09, 1.4521317e-08, 1.09093e-06, 1.2531165e-10, 0.9990938, 5.798501e-09, 5.785368e-05, 3.82365e-09, 7.404351e-08, 0.008338481, 8.048078e-10, 0.99157715, 1.1663455e-05, 1.4583546e-05, 8.3543476e-08, 3.274394e-08, 2.4682688e-05, 1.3951502e-09, 1.0260489e-08, 0.9998845, 1.9418138e-08, 8.667954e-07, 2.1851054e-07, 8.917964e-05, 4.4437223e-07, 1.1292918e-07, 4.5302792e-07, 5.631744e-08, 2.9086214e-08, 3.1013877e-07, 7.695681e-09, 2.1452344e-09, 1.1493902e-08, 6.1980093e-10, 0.99999917, 1.1436694e-08, 2.42685e-05, 8.557389e-08, 0.024081504, 0.0073837163, 4.8152968e-05, 5.128531e-07, 0.9684405, 9.630179e-08, 2.1060101e-05, 1.901065e-07], 'name': 'fc10', 'shape': [20, 10], 'datatype': 'FP32'}, {'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'name': 'is_outlier', 'shape': [1, 20], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}]}
+
+```
+
+```bash
+seldon pipeline inspect cifar10-production.cifar10-drift.outputs.is_drift
+```
+
+```
+seldon.default.model.cifar10-drift.outputs	cifeimgfh5ss738i5bpg	{"name":"is_drift", "datatype":"INT64", "shape":["1", "1"], "parameters":{"content_type":{"stringParam":"np"}}, "contents":{"int64Contents":["1"]}}
+
+```
+
+```python
+infer("cifar10-production.pipeline",1, "outlier")
+```
+
+```
+![png](infer_files/infer_18_0.png)
+
+```
+
+```
+<Response [200]>
+{'model_name': '', 'outputs': [{'data': [6.3606867e-06, 0.0006106364, 0.0054279356, 0.6536454, 1.4738829e-05, 2.6104701e-06, 0.3397848, 1.3538776e-05, 0.0004458526, 4.807229e-05], 'name': 'fc10', 'shape': [1, 10], 'datatype': 'FP32'}, {'data': [1], 'name': 'is_outlier', 'shape': [1, 1], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}]}
+
+```
+
+```python
+infer("cifar10-production.pipeline",1, "ok")
+```
+
+```
+![png](infer_files/infer_19_0.png)
+
+```
+
+```
+<Response [200]>
+{'model_name': '', 'outputs': [{'data': [1.45001495e-08, 1.2525752e-09, 1.6298458e-07, 0.11529388, 1.7431412e-07, 6.1856604e-06, 0.8846994, 6.0739285e-09, 7.43792e-08, 4.7317337e-09], 'name': 'fc10', 'shape': [1, 10], 'datatype': 'FP32'}, {'data': [0], 'name': 'is_outlier', 'shape': [1, 1], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}]}
+
+```
+
+Use the seldon CLI to look at the outputs from the CIFAR10 model. It will decode the Triton
+binary outputs for us.
+
+```bash
+seldon pipeline inspect cifar10-production.cifar10.outputs
+```
+
+```
+seldon.default.model.cifar10.outputs	cifeiq8fh5ss738i5bqg	{"modelName":"cifar10_1", "modelVersion":"1", "outputs":[{"name":"fc10", "datatype":"FP32", "shape":["1", "10"], "contents":{"fp32Contents":[1.45001495e-8, 1.2525752e-9, 1.6298458e-7, 0.11529388, 1.7431412e-7, 0.0000061856604, 0.8846994, 6.0739285e-9, 7.43792e-8, 4.7317337e-9]}}]}
+
+```
+
+```bash
+seldon pipeline unload cifar10-production
+```
+
+```bash
+seldon model unload cifar10
+seldon model unload cifar10-outlier
+seldon model unload cifar10-drift
 ```
diff --git a/docs/examples/custom-servers.md b/docs/examples/custom-servers.md
index 13b87a59f2..70f60fde52 100644
--- a/docs/examples/custom-servers.md
+++ b/docs/examples/custom-servers.md
@@ -1,8 +1,276 @@
-# Custom Servers
+# Seldon V2 Kubernetes Custom Examples
 
-Run these examples from the `samples` folder.
+```python
+import os
+os.environ["NAMESPACE"] = "seldon-mesh"
+```
+
+```python
+MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+MESH_IP=MESH_IP[0]
+import os
+os.environ['MESH_IP'] = MESH_IP
+MESH_IP
+```
+
+```
+'172.18.255.2'
+
+```
+
+## Custom Server with Capabilities
+
+The `capabilities` field replaces the capabilities from the ServerConfig.
+
+```bash
+cat ./servers/custom-mlserver-capabilities.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Server
+metadata:
+  name: mlserver-134
+spec:
+  serverConfig: mlserver
+  capabilities:
+  - mlserver-1.3.4
+  podSpec:
+    containers:
+    - image: seldonio/mlserver:1.3.4
+      name: mlserver
+
+```
+
+```bash
+kubectl create -f ./servers/custom-mlserver-capabilities.yaml -n ${NAMESPACE}
+```
+
+```
+server.mlops.seldon.io/mlserver-134 created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s server --all -n ${NAMESPACE}
+```
+
+```
+server.mlops.seldon.io/mlserver condition met
+server.mlops.seldon.io/mlserver-134 condition met
+server.mlops.seldon.io/triton condition met
+
+```
+
+```bash
+cat ./models/iris-custom-requirements.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - mlserver-1.3.4
+
+```
+
+```bash
+kubectl create -f ./models/iris-custom-requirements.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris condition met
+
+```
+
+```bash
+seldon model infer iris --inference-host ${MESH_IP}:80 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```json
+{
+	"model_name": "iris_1",
+	"model_version": "1",
+	"id": "057ae95c-e6bc-4f57-babf-0817ff171729",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"parameters": {
+				"content_type": "np"
+			},
+			"data": [
+				2
+			]
+		}
+	]
+}
+
+```
+
+```bash
+kubectl delete -f ./models/iris-custom-server.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "iris" deleted
+
+```
+
+```bash
+kubectl delete -f ./servers/custom-mlserver.yaml -n ${NAMESPACE}
+```
 
+```
+server.mlops.seldon.io "mlserver-134" deleted
+
+```
+
+## Custom Server with Extra Capabilities
+
+The `extraCapabilities` field extends the existing list from the ServerConfig.
+
+```bash
+cat ./servers/custom-mlserver.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Server
+metadata:
+  name: mlserver-134
+spec:
+  serverConfig: mlserver
+  extraCapabilities:
+  - mlserver-1.3.4
+  podSpec:
+    containers:
+    - image: seldonio/mlserver:1.3.4
+      name: mlserver
+
+```
+
+```bash
+kubectl create -f ./servers/custom-mlserver.yaml -n ${NAMESPACE}
+```
+
+```
+server.mlops.seldon.io/mlserver-134 created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s server --all -n ${NAMESPACE}
+```
+
+```
+server.mlops.seldon.io/mlserver condition met
+server.mlops.seldon.io/mlserver-134 condition met
+server.mlops.seldon.io/triton condition met
+
+```
+
+```bash
+cat ./models/iris-custom-server.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  server: mlserver-134
+
+```
+
+```bash
+kubectl create -f ./models/iris-custom-server.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris condition met
+
+```
+
+```bash
+seldon model infer iris --inference-host ${MESH_IP}:80 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```json
+{
+	"model_name": "iris_1",
+	"model_version": "1",
+	"id": "a3e17c6c-ee3f-4a51-b890-6fb16385a757",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"parameters": {
+				"content_type": "np"
+			},
+			"data": [
+				2
+			]
+		}
+	]
+}
 
-```{include} ../../../../samples/k8s-custom-servers.md
 ```
 
+```bash
+kubectl delete -f ./models/iris-custom-server.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "iris" deleted
+
+```
+
+```bash
+kubectl delete -f ./servers/custom-mlserver.yaml -n ${NAMESPACE}
+```
+
+```
+server.mlops.seldon.io "mlserver-134" deleted
+
+```
+
+```python
+
+```
diff --git a/docs/examples/experiment-versions.md b/docs/examples/experiment-versions.md
index 28c288ba02..fdb3e3f060 100644
--- a/docs/examples/experiment-versions.md
+++ b/docs/examples/experiment-versions.md
@@ -1,8 +1,489 @@
-# Experiments Versions
+# Seldon V2 Experiment Version Tests
 
-Run these examples from the `samples` folder.
+This notebook will show how we can update running experiments.
 
+### Test change candidate for a model
+
+We will use three SKlearn Iris classification models to illustrate experiment updates.
+
+Load all models.
+
+```bash
+seldon model load -f ./models/sklearn1.yaml
+seldon model load -f ./models/sklearn2.yaml
+seldon model load -f ./models/sklearn3.yaml
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+```bash
+seldon model status iris -w ModelAvailable
+seldon model status iris2 -w ModelAvailable
+seldon model status iris3 -w ModelAvailable
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+Let's call all three models individually first.
+
+```bash
+seldon model infer iris -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+```bash
+seldon model infer iris2 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::50]
+
+```
+
+```bash
+seldon model infer iris3 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris3_1::50]
+
+```
+
+We will start an experiment to change the iris endpoint to split traffic with the `iris2` model.
+
+```bash
+cat ./experiments/ab-default-model.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: experiment-sample
+spec:
+  default: iris
+  candidates:
+  - name: iris
+    weight: 50
+  - name: iris2
+    weight: 50
+
+```
+
+```bash
+seldon experiment start -f ./experiments/ab-default-model.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon experiment status experiment-sample -w | jq -M .
+```
+
+```json
+{
+  "experimentName": "experiment-sample",
+  "active": true,
+  "candidatesReady": true,
+  "mirrorReady": true,
+  "statusDescription": "experiment active",
+  "kubernetesMeta": {}
+}
+
+```
+
+Now when we call the iris model we should see a roughly 50/50 split between the two models.
+
+```bash
+seldon model infer iris -i 100 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::48 :iris_1::52]
+
+```
+
+Now we update the experiment to change to a split with the `iris3` model.
+
+```bash
+cat ./experiments/ab-default-model2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: experiment-sample
+spec:
+  default: iris
+  candidates:
+  - name: iris
+    weight: 50
+  - name: iris3
+    weight: 50
+
+```
+
+```bash
+seldon experiment start -f ./experiments/ab-default-model2.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon experiment status experiment-sample -w | jq -M .
+```
+
+```json
+{
+  "experimentName": "experiment-sample",
+  "active": true,
+  "candidatesReady": true,
+  "mirrorReady": true,
+  "statusDescription": "experiment active",
+  "kubernetesMeta": {}
+}
+
+```
+
+Now we should see a split with the `iris3` model.
+
+```bash
+seldon model infer iris -i 100 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris3_1::42 :iris_1::58]
+
+```
+
+```bash
+seldon experiment stop experiment-sample
+```
+
+```json
+{}
+
+```
+
+Now the experiment has been stopped we check everything as before.
+
+```bash
+seldon model infer iris -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+```bash
+seldon model infer iris2 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::50]
+
+```
+
+```bash
+seldon model infer iris3 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris3_1::50]
+
+```
+
+```bash
+seldon model unload iris
+seldon model unload iris2
+seldon model unload iris3
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+### Test change default model in an experiment
+
+Here we test changing the model we want to split traffic on. We will use three SKlearn Iris classification models to illustrate.
+
+```bash
+seldon model load -f ./models/sklearn1.yaml
+seldon model load -f ./models/sklearn2.yaml
+seldon model load -f ./models/sklearn3.yaml
+```
+
+```json
+{}
+{}
+{}
 
-```{include} ../../../../samples/experiment_versions.md
 ```
 
+```bash
+seldon model status iris -w ModelAvailable
+seldon model status iris2 -w ModelAvailable
+seldon model status iris3 -w ModelAvailable
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+Let's call all three models to verify initial conditions.
+
+```bash
+seldon model infer iris -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+```bash
+seldon model infer iris2 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::50]
+
+```
+
+```bash
+seldon model infer iris3 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris3_1::50]
+
+```
+
+Now we start an experiment to change calls to the `iris` model to split with the `iris2` model.
+
+```bash
+cat ./experiments/ab-default-model.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: experiment-sample
+spec:
+  default: iris
+  candidates:
+  - name: iris
+    weight: 50
+  - name: iris2
+    weight: 50
+
+```
+
+```bash
+seldon experiment start -f ./experiments/ab-default-model.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon experiment status experiment-sample -w | jq -M .
+```
+
+```json
+{
+  "experimentName": "experiment-sample",
+  "active": true,
+  "candidatesReady": true,
+  "mirrorReady": true,
+  "statusDescription": "experiment active",
+  "kubernetesMeta": {}
+}
+
+```
+
+Run a set of calls and record which route the traffic took. There should be roughly a 50/50 split.
+
+```bash
+seldon model infer iris -i 100 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::51 :iris_1::49]
+
+```
+
+Now let's change the model we want to experiment to modify to the `iris3` model. Splitting between that and `iris2`.
+
+```bash
+cat ./experiments/ab-default-model3.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: experiment-sample
+spec:
+  default: iris3
+  candidates:
+  - name: iris3
+    weight: 50
+  - name: iris2
+    weight: 50
+
+```
+
+```bash
+seldon experiment start -f ./experiments/ab-default-model3.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon experiment status experiment-sample -w | jq -M .
+```
+
+```json
+{
+  "experimentName": "experiment-sample",
+  "active": true,
+  "candidatesReady": true,
+  "mirrorReady": true,
+  "statusDescription": "experiment active",
+  "kubernetesMeta": {}
+}
+
+```
+
+Let's check the iris model is now as before but the iris3 model has traffic split.
+
+```bash
+seldon model infer iris -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+```bash
+seldon model infer iris3 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::25 :iris3_1::25]
+
+```
+
+```bash
+seldon model infer iris2 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::50]
+
+```
+
+```bash
+seldon experiment stop experiment-sample
+```
+
+```json
+{}
+
+```
+
+Finally, let's check now the experiment has stopped as is as at the start.
+
+```bash
+seldon model infer iris -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+```bash
+seldon model infer iris2 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::50]
+
+```
+
+```bash
+seldon model infer iris3 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris3_1::50]
+
+```
+
+```bash
+seldon model unload iris
+seldon model unload iris2
+seldon model unload iris3
+```
+
+```json
+{}
+{}
+{}
+
+```
diff --git a/docs/examples/explainer-examples.md b/docs/examples/explainer-examples.md
index 68c5279654..47c7ddfa88 100644
--- a/docs/examples/explainer-examples.md
+++ b/docs/examples/explainer-examples.md
@@ -1,8 +1,318 @@
-# Explainer Examples
+# Anchor Tabular Explainer for SKLearn Income Model
 
-Run these examples from the `samples` folder.
+```bash
+cat ./models/income.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/classifier"
+  requirements:
+  - sklearn
+
+```
+
+```bash
+seldon model load -f ./models/income.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status income -w ModelAvailable
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model infer income \
+  '{"inputs": [{"name": "predict", "shape": [1, 12], "datatype": "FP32", "data": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}'
+```
+
+```json
+{
+	"model_name": "income_1",
+	"model_version": "1",
+	"id": "c65b8302-85af-4bac-aac5-91e3bedebee8",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"data": [
+				0
+			]
+		}
+	]
+}
+
+```
+
+```bash
+cat ./models/income-explainer.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/explainer"
+  explainer:
+    type: anchor_tabular
+    modelRef: income
+
+```
+
+```bash
+seldon model load -f ./models/income-explainer.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status income-explainer -w ModelAvailable
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model infer income-explainer \
+  '{"inputs": [{"name": "predict", "shape": [1, 12], "datatype": "FP32", "data": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}'
+```
+
+```json
+{
+	"model_name": "income-explainer_1",
+	"model_version": "1",
+	"id": "a22c3785-ff3b-4504-9b3c-199aa48a62d6",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "explanation",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "BYTES",
+			"parameters": {
+				"content_type": "str"
+			},
+			"data": [
+				"{\"meta\": {\"name\": \"AnchorTabular\", \"type\": [\"blackbox\"], \"explanations\": [\"local\"], \"params\": {\"seed\": 1, \"disc_perc\": [25, 50, 75], \"threshold\": 0.95, \"delta\": 0.1, \"tau\": 0.15, \"batch_size\": 100, \"coverage_samples\": 10000, \"beam_size\": 1, \"stop_on_first\": false, \"max_anchor_size\": null, \"min_samples_start\": 100, \"n_covered_ex\": 10, \"binary_cache_size\": 10000, \"cache_margin\": 1000, \"verbose\": false, \"verbose_every\": 1, \"kwargs\": {}}, \"version\": \"0.9.0\"}, \"data\": {\"anchor\": [\"Marital Status = Never-Married\", \"Relationship = Own-child\"], \"precision\": 0.9518716577540107, \"coverage\": 0.07165109034267912, \"raw\": {\"feature\": [3, 5], \"mean\": [0.7959381044487428, 0.9518716577540107], \"precision\": [0.7959381044487428, 0.9518716577540107], \"coverage\": [0.3037383177570093, 0.07165109034267912], \"examples\": [{\"covered_true\": [[52, 5, 5, 1, 8, 1, 2, 0, 0, 0, 50, 9], [49, 4, 1, 1, 4, 4, 1, 0, 0, 0, 40, 1], [23, 4, 1, 1, 6, 1, 4, 1, 0, 0, 40, 9], [55, 2, 1, 1, 5, 1, 4, 0, 0, 0, 48, 9], [22, 4, 1, 1, 2, 3, 4, 0, 0, 0, 15, 9], [51, 4, 2, 1, 5, 0, 1, 1, 0, 0, 99, 4], [40, 4, 1, 1, 5, 1, 4, 0, 0, 0, 40, 9], [40, 6, 1, 1, 2, 0, 4, 1, 0, 0, 50, 9], [50, 5, 5, 1, 6, 0, 4, 1, 0, 0, 55, 9], [41, 4, 1, 1, 6, 0, 4, 1, 0, 0, 40, 9]], \"covered_false\": [[42, 4, 1, 1, 8, 0, 4, 1, 0, 2415, 60, 9], [48, 6, 2, 1, 5, 4, 4, 0, 0, 0, 60, 9], [37, 4, 1, 1, 5, 0, 4, 1, 0, 0, 45, 9], [57, 4, 5, 1, 8, 0, 4, 1, 0, 0, 50, 9], [63, 7, 2, 1, 8, 0, 4, 1, 0, 1902, 50, 9], [51, 4, 5, 1, 8, 0, 4, 1, 0, 1887, 47, 9], [51, 2, 2, 1, 8, 1, 4, 0, 0, 0, 45, 9], [68, 7, 5, 1, 5, 0, 4, 1, 0, 2377, 42, 0], [45, 4, 1, 1, 8, 0, 4, 1, 15024, 0, 40, 9], [45, 4, 1, 1, 8, 0, 4, 1, 0, 1977, 60, 9]], \"uncovered_true\": [], \"uncovered_false\": []}, {\"covered_true\": [[44, 6, 5, 1, 8, 3, 4, 0, 0, 1902, 60, 9], [58, 7, 2, 1, 5, 3, 1, 1, 4064, 0, 40, 1], [50, 7, 1, 1, 1, 3, 2, 0, 0, 0, 37, 9], [34, 4, 2, 1, 5, 3, 4, 1, 0, 0, 45, 9], [45, 4, 1, 1, 5, 3, 4, 1, 0, 0, 40, 9], [33, 7, 5, 1, 5, 3, 1, 1, 0, 0, 30, 6], [61, 7, 2, 1, 5, 3, 4, 1, 0, 0, 40, 0], [35, 4, 5, 1, 1, 3, 4, 1, 0, 0, 40, 9], [71, 2, 1, 1, 5, 3, 4, 0, 0, 0, 6, 9], [44, 4, 1, 1, 8, 3, 2, 1, 0, 0, 35, 9]], \"covered_false\": [[30, 4, 5, 1, 5, 3, 4, 1, 10520, 0, 40, 9], [54, 7, 2, 1, 8, 3, 4, 1, 0, 1902, 50, 9], [66, 6, 2, 1, 6, 3, 4, 1, 0, 2377, 25, 9], [35, 4, 2, 1, 5, 3, 4, 1, 7298, 0, 40, 9], [44, 4, 1, 1, 8, 3, 4, 1, 7298, 0, 48, 9], [31, 4, 1, 1, 8, 3, 4, 0, 13550, 0, 50, 9], [35, 4, 1, 1, 8, 3, 4, 1, 8614, 0, 45, 9]], \"uncovered_true\": [], \"uncovered_false\": []}], \"all_precision\": 0, \"num_preds\": 1000000, \"success\": true, \"names\": [\"Marital Status = Never-Married\", \"Relationship = Own-child\"], \"prediction\": [0], \"instance\": [47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0], \"instances\": [[47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0]]}}}"
+			]
+		}
+	]
+}
+
+```
+
+```bash
+seldon model unload income-explainer
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model unload income
+```
+
+```json
+{}
+
+```
 
+## Anchor Text Explainer for SKLearn Movies Sentiment Model
 
-```{include} ../../../../samples/explainer-examples.md
+```bash
+cat ./models/moviesentiment.yaml
 ```
 
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/moviesentiment-sklearn"
+  requirements:
+  - sklearn
+
+```
+
+```bash
+seldon model load -f ./models/moviesentiment.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status sentiment -w ModelAvailable
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model infer sentiment \
+  '{"parameters": {"content_type": "str"}, "inputs": [{"name": "foo", "data": ["I am good"], "datatype": "BYTES","shape": [1]}]}'
+```
+
+```json
+{
+	"model_name": "sentiment_2",
+	"model_version": "1",
+	"id": "f5c07363-7e9d-4f09-aa30-228c81fdf4a4",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"parameters": {
+				"content_type": "np"
+			},
+			"data": [
+				0
+			]
+		}
+	]
+}
+
+```
+
+```bash
+cat ./models/moviesentiment-explainer.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: sentiment-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/moviesentiment-sklearn-explainer"
+  explainer:
+    type: anchor_text
+    modelRef: sentiment
+
+```
+
+```bash
+seldon model load -f ./models/moviesentiment-explainer.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status sentiment-explainer -w ModelAvailable
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model infer sentiment-explainer \
+  '{"parameters": {"content_type": "str"}, "inputs": [{"name": "foo", "data": ["I am good"], "datatype": "BYTES","shape": [1]}]}'
+```
+
+```
+Error: V2 server error: 500 Traceback (most recent call last):
+  File "/opt/conda/lib/python3.8/site-packages/starlette/middleware/errors.py", line 162, in __call__
+    await self.app(scope, receive, _send)
+  File "/opt/conda/lib/python3.8/site-packages/starlette_exporter/middleware.py", line 307, in __call__
+    await self.app(scope, receive, wrapped_send)
+  File "/opt/conda/lib/python3.8/site-packages/starlette/middleware/gzip.py", line 24, in __call__
+    await responder(scope, receive, send)
+  File "/opt/conda/lib/python3.8/site-packages/starlette/middleware/gzip.py", line 44, in __call__
+    await self.app(scope, receive, self.send_with_gzip)
+  File "/opt/conda/lib/python3.8/site-packages/starlette/middleware/exceptions.py", line 79, in __call__
+    raise exc
+  File "/opt/conda/lib/python3.8/site-packages/starlette/middleware/exceptions.py", line 68, in __call__
+    await self.app(scope, receive, sender)
+  File "/opt/conda/lib/python3.8/site-packages/fastapi/middleware/asyncexitstack.py", line 21, in __call__
+    raise e
+  File "/opt/conda/lib/python3.8/site-packages/fastapi/middleware/asyncexitstack.py", line 18, in __call__
+    await self.app(scope, receive, send)
+  File "/opt/conda/lib/python3.8/site-packages/starlette/routing.py", line 706, in __call__
+    await route.handle(scope, receive, send)
+  File "/opt/conda/lib/python3.8/site-packages/starlette/routing.py", line 276, in handle
+    await self.app(scope, receive, send)
+  File "/opt/conda/lib/python3.8/site-packages/starlette/routing.py", line 66, in app
+    response = await func(request)
+  File "/opt/conda/lib/python3.8/site-packages/mlserver/rest/app.py", line 42, in custom_route_handler
+    return await original_route_handler(request)
+  File "/opt/conda/lib/python3.8/site-packages/fastapi/routing.py", line 237, in app
+    raw_response = await run_endpoint_function(
+  File "/opt/conda/lib/python3.8/site-packages/fastapi/routing.py", line 163, in run_endpoint_function
+    return await dependant.call(**values)
+  File "/opt/conda/lib/python3.8/site-packages/mlserver/rest/endpoints.py", line 99, in infer
+    inference_response = await self._data_plane.infer(
+  File "/opt/conda/lib/python3.8/site-packages/mlserver/handlers/dataplane.py", line 103, in infer
+    prediction = await model.predict(payload)
+  File "/opt/conda/lib/python3.8/site-packages/mlserver_alibi_explain/runtime.py", line 86, in predict
+    output_data = await self._async_explain_impl(input_data, payload.parameters)
+  File "/opt/conda/lib/python3.8/site-packages/mlserver_alibi_explain/runtime.py", line 119, in _async_explain_impl
+    explanation = await loop.run_in_executor(self._executor, explain_call)
+  File "/opt/conda/lib/python3.8/concurrent/futures/thread.py", line 57, in run
+    result = self.fn(*self.args, **self.kwargs)
+  File "/opt/conda/lib/python3.8/site-packages/mlserver_alibi_explain/explainers/black_box_runtime.py", line 62, in _explain_impl
+    input_data = input_data[0]
+KeyError: 0
+
+```
+
+```bash
+seldon model unload sentiment-explainer
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model unload sentiment
+```
+
+```json
+{}
+
+```
diff --git a/docs/examples/huggingface.md b/docs/examples/huggingface.md
index 1522eb2fc6..750750d7c2 100644
--- a/docs/examples/huggingface.md
+++ b/docs/examples/huggingface.md
@@ -1,8 +1,189 @@
-# Huggingface Models
+# Huggingface Examples
 
-Run these examples from the `samples` folder.
+### Text Generation Model
 
+```bash
+cat ./models/hf-text-gen.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: text-gen
+spec:
+  storageUri: "gs://seldon-models/mlserver/huggingface/text-generation"
+  requirements:
+  - huggingface
+
+```
+
+Load the model
+
+```bash
+seldon model load -f ./models/hf-text-gen.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status text-gen -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model infer text-gen \
+  '{"inputs": [{"name": "args","shape": [1],"datatype": "BYTES","data": ["Once upon a time in a galaxy far away"]}]}'
+```
+
+```json
+{
+	"model_name": "text-gen_1",
+	"model_version": "1",
+	"id": "121ff5f4-1d4a-46d0-9a5e-4cd3b11040df",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "output",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "BYTES",
+			"parameters": {
+				"content_type": "hg_jsonlist"
+			},
+			"data": [
+				"{\"generated_text\": \"Once upon a time in a galaxy far away, the planet is full of strange little creatures. A very strange combination of creatures in that universe, that is. A strange combination of creatures in that universe, that is. A kind of creature that is\"}"
+			]
+		}
+	]
+}
+
+```
+
+```python
+res = !seldon model infer text-gen --inference-mode grpc \
+   '{"inputs":[{"name":"args","contents":{"bytes_contents":["T25jZSB1cG9uIGEgdGltZSBpbiBhIGdhbGF4eSBmYXIgYXdheQo="]},"datatype":"BYTES","shape":[1]}]}'
+```
+
+```python
+import json
+import base64
+r = json.loads(res[0])
+base64.b64decode(r["outputs"][0]["contents"]["bytesContents"][0])
+```
+
+```
+b'{"generated_text": "Once upon a time in a galaxy far away\\n\\nThe Universe is a big and massive place. How can you feel any of this? Your body doesn\'t make sense if the Universe is in full swing \\u2014 you don\'t have to remember whether the"}'
+
+```
+
+Unload the model
+
+```bash
+seldon model unload text-gen
+```
+
+### Custom Text Generation Model
+
+```bash
+cat ./models/hf-text-gen-custom-tiny-stories.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: custom-tiny-stories-text-gen
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/huggingface-text-gen-custom-tiny-stories"
+  requirements:
+    - huggingface
+
+```
+
+Load the model
+
+```bash
+seldon model load -f ./models/hf-text-gen-custom-tiny-stories.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status custom-tiny-stories-text-gen -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model infer custom-tiny-stories-text-gen \
+  '{"inputs": [{"name": "args","shape": [1],"datatype": "BYTES","data": ["Once upon a time in a galaxy far away"]}]}'
+```
+
+```json
+{
+	"model_name": "custom-tiny-stories-text-gen_1",
+	"model_version": "1",
+	"id": "d0fce59c-76e2-4f81-9711-1c93d08bcbf9",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "output",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "BYTES",
+			"parameters": {
+				"content_type": "hg_jsonlist"
+			},
+			"data": [
+				"{\"generated_text\": \"Once upon a time in a galaxy far away. It was a very special place to live.\\n\"}"
+			]
+		}
+	]
+}
+
+```
+
+```python
+res = !seldon model infer custom-tiny-stories-text-gen --inference-mode grpc \
+   '{"inputs":[{"name":"args","contents":{"bytes_contents":["T25jZSB1cG9uIGEgdGltZSBpbiBhIGdhbGF4eSBmYXIgYXdheQo="]},"datatype":"BYTES","shape":[1]}]}'
+```
+
+```python
+import json
+import base64
+r = json.loads(res[0])
+base64.b64decode(r["outputs"][0]["contents"]["bytesContents"][0])
+```
+
+```
+b'{"generated_text": "Once upon a time in a galaxy far away\\nOne night, a little girl named Lily went to"}'
+
+```
+
+Unload the model
 
-```{include} ../../../../samples/huggingface.md
+```bash
+seldon model unload custom-tiny-stories-text-gen
 ```
 
+````
+As a next step, why not try running a larger-scale model? You can find a definition for one in ./models/hf-text-gen-custom-gpt2.yaml. However, you may need to request and allocate more memory!
+````
diff --git a/docs/examples/income.md b/docs/examples/income.md
index 2192ac8bf4..82b4ba6dfb 100644
--- a/docs/examples/income.md
+++ b/docs/examples/income.md
@@ -1,7 +1,341 @@
-# Production Income Classifier with Drift, Outlier and Explanations 
+# Tabular Income Classifier Production Deployment
 
-Run these examples from the `samples/examples/income_classifier` folder.
+To run this notebook you need the inference data. This can be acquired in two ways:
 
+* Run `make train` or,
+* `gsutil cp -R gs://seldon-models/scv2/examples/income/infer-data .`
 
-```{include} ../../../../samples/examples/income_classifier/infer.md
+```python
+import numpy as np
+import json
+import requests
+```
+
+```python
+with open('./infer-data/test.npy', 'rb') as f:
+    x_ref = np.load(f)
+    x_h1 = np.load(f)
+    y_ref = np.load(f)
+    x_outlier = np.load(f)
+```
+
+```python
+reqJson = json.loads('{"inputs":[{"name":"input_1","data":[],"datatype":"FP32","shape":[]}]}')
+url = "http://0.0.0.0:9000/v2/models/model/infer"
+```
+
+```python
+def infer(resourceName: str, batchSz: int, requestType: str):
+    if requestType == "outlier":
+        rows = x_outlier[0:0+batchSz]
+    elif requestType == "drift":
+        rows = x_h1[0:0+batchSz]
+    else:
+        rows = x_ref[0:0+batchSz]
+    reqJson["inputs"][0]["data"] = rows.flatten().tolist()
+    reqJson["inputs"][0]["shape"] = [batchSz, rows.shape[1]]
+    headers = {"Content-Type": "application/json", "seldon-model":resourceName}
+    response_raw = requests.post(url, json=reqJson, headers=headers)
+    print(response_raw)
+    print(response_raw.json())
+```
+
+### Pipeline with model, drift detector and outlier detector
+
+```bash
+cat ../../models/income-preprocess.yaml
+echo "---"
+cat ../../models/income.yaml
+echo "---"
+cat ../../models/income-drift.yaml
+echo "---"
+cat ../../models/income-outlier.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-preprocess
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/preprocessor"
+  requirements:
+  - sklearn
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/classifier"
+  requirements:
+  - sklearn
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-drift
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/drift-detector"
+  requirements:
+    - mlserver
+    - alibi-detect
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-outlier
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/outlier-detector"
+  requirements:
+    - mlserver
+    - alibi-detect
+
+```
+
+```bash
+seldon model load -f ../../models/income-preprocess.yaml
+seldon model load -f ../../models/income.yaml
+seldon model load -f ../../models/income-drift.yaml
+seldon model load -f ../../models/income-outlier.yaml
+```
+
+```json
+{}
+{}
+{}
+{}
+
+```
+
+```bash
+seldon model status income-preprocess -w ModelAvailable | jq .
+seldon model status income -w ModelAvailable | jq .
+seldon model status income-drift -w ModelAvailable | jq .
+seldon model status income-outlier -w ModelAvailable | jq .
+```
+
+```json
+{}
+{}
+{}
+{}
+
+```
+
+```bash
+cat ../../pipelines/income.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: income-production
+spec:
+  steps:
+    - name: income
+    - name: income-preprocess
+    - name: income-outlier
+      inputs:
+      - income-preprocess
+    - name: income-drift
+      batch:
+        size: 20
+  output:
+    steps:
+    - income
+    - income-outlier.outputs.is_outlier
+
+```
+
+```bash
+seldon pipeline load -f ../../pipelines/income.yaml
+```
+
+```bash
+seldon pipeline status income-production -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "income-production",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "income-production",
+        "uid": "cifej8iufmbc73e5int0",
+        "version": 1,
+        "steps": [
+          {
+            "name": "income"
+          },
+          {
+            "name": "income-drift",
+            "batch": {
+              "size": 20
+            }
+          },
+          {
+            "name": "income-outlier",
+            "inputs": [
+              "income-preprocess.outputs"
+            ]
+          },
+          {
+            "name": "income-preprocess"
+          }
+        ],
+        "output": {
+          "steps": [
+            "income.outputs",
+            "income-outlier.outputs.is_outlier"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-30T14:41:38.343754921Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+Show predictions from reference set. Should not be drift or outliers.
+
+```python
+batchSz=20
+print(y_ref[0:batchSz])
+infer("income-production.pipeline",batchSz,"normal")
+```
+
+```
+[0 0 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1]
+<Response [200]>
+{'model_name': '', 'outputs': [{'data': [0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1], 'name': 'predict', 'shape': [20, 1], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}, {'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'name': 'is_outlier', 'shape': [1, 20], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}]}
+
+```
+
+```bash
+seldon pipeline inspect income-production.income-drift.outputs.is_drift
+```
+
+```
+seldon.default.model.income-drift.outputs	cifej9gfh5ss738i5br0	{"name":"is_drift", "datatype":"INT64", "shape":["1", "1"], "parameters":{"content_type":{"stringParam":"np"}}, "contents":{"int64Contents":["0"]}}
+
+```
+
+Show predictions from drift data. Should be drift and probably not outliers.
+
+```python
+batchSz=20
+print(y_ref[0:batchSz])
+infer("income-production.pipeline",batchSz,"drift")
+```
+
+```
+[0 0 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1]
+<Response [200]>
+{'model_name': '', 'outputs': [{'data': [0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1], 'name': 'predict', 'shape': [20, 1], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}, {'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'name': 'is_outlier', 'shape': [1, 20], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}]}
+
+```
+
+```bash
+seldon pipeline inspect income-production.income-drift.outputs.is_drift
+```
+
+```
+seldon.default.model.income-drift.outputs	cifejaofh5ss738i5brg	{"name":"is_drift", "datatype":"INT64", "shape":["1", "1"], "parameters":{"content_type":{"stringParam":"np"}}, "contents":{"int64Contents":["1"]}}
+
+```
+
+Show predictions from outlier data. Should be outliers and probably not drift.
+
+```python
+batchSz=20
+print(y_ref[0:batchSz])
+infer("income-production.pipeline",batchSz,"outlier")
+```
+
+```
+[0 0 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1]
+<Response [200]>
+{'model_name': '', 'outputs': [{'data': [0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1], 'name': 'predict', 'shape': [20, 1], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}, {'data': [1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1], 'name': 'is_outlier', 'shape': [1, 20], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}]}
+
+```
+
+```bash
+seldon pipeline inspect income-production.income-drift.outputs.is_drift
+```
+
+```
+seldon.default.model.income-drift.outputs	cifejb8fh5ss738i5bs0	{"name":"is_drift", "datatype":"INT64", "shape":["1", "1"], "parameters":{"content_type":{"stringParam":"np"}}, "contents":{"int64Contents":["0"]}}
+
+```
+
+### Explanations
+
+```bash
+cat ../../models/income-explainer.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/explainer"
+  explainer:
+    type: anchor_tabular
+    modelRef: income
+
+```
+
+```bash
+seldon model load -f ../../models/income-explainer.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status income-explainer -w ModelAvailable | jq .
+```
+
+```json
+{}
+
+```
+
+```python
+batchSz=1
+print(y_ref[0:batchSz])
+infer("income-explainer",batchSz,"normal")
+```
+
+```
+[0]
+<Response [200]>
+{'model_name': 'income-explainer_1', 'model_version': '1', 'id': 'cdd68ba5-c569-4930-886f-fbdc26e24866', 'parameters': {}, 'outputs': [{'name': 'explanation', 'shape': [1, 1], 'datatype': 'BYTES', 'parameters': {'content_type': 'str'}, 'data': ['{"meta": {"name": "AnchorTabular", "type": ["blackbox"], "explanations": ["local"], "params": {"seed": 1, "disc_perc": [25, 50, 75], "threshold": 0.95, "delta": 0.1, "tau": 0.15, "batch_size": 100, "coverage_samples": 10000, "beam_size": 1, "stop_on_first": false, "max_anchor_size": null, "min_samples_start": 100, "n_covered_ex": 10, "binary_cache_size": 10000, "cache_margin": 1000, "verbose": false, "verbose_every": 1, "kwargs": {}}, "version": "0.9.1"}, "data": {"anchor": ["Marital Status = Never-Married", "Relationship = Own-child", "Capital Gain <= 0.00"], "precision": 0.9942028985507246, "coverage": 0.0657, "raw": {"feature": [3, 5, 8], "mean": [0.7914951989026063, 0.9400749063670412, 0.9942028985507246], "precision": [0.7914951989026063, 0.9400749063670412, 0.9942028985507246], "coverage": [0.3043, 0.069, 0.0657], "examples": [{"covered_true": [[30, 0, 1, 1, 0, 1, 1, 0, 0, 0, 50, 2], [49, 4, 2, 1, 6, 0, 4, 1, 0, 0, 60, 9], [39, 2, 5, 1, 5, 0, 4, 1, 0, 0, 40, 9], [33, 4, 2, 1, 5, 0, 4, 1, 0, 0, 40, 9], [63, 4, 1, 1, 8, 1, 4, 0, 0, 0, 40, 9], [23, 4, 1, 1, 7, 1, 4, 1, 0, 0, 66, 8], [45, 4, 1, 1, 8, 0, 1, 1, 0, 0, 40, 1], [54, 4, 1, 1, 8, 4, 4, 1, 0, 0, 45, 9], [32, 6, 1, 1, 8, 4, 2, 0, 0, 0, 30, 9], [40, 5, 1, 1, 2, 0, 4, 1, 0, 0, 40, 9]], "covered_false": [[57, 4, 5, 1, 5, 0, 4, 1, 0, 1977, 45, 9], [53, 0, 5, 1, 0, 1, 4, 0, 8614, 0, 35, 9], [37, 4, 1, 1, 5, 0, 4, 1, 0, 0, 45, 9], [53, 4, 5, 1, 8, 0, 4, 1, 0, 1977, 55, 9], [35, 4, 1, 1, 8, 0, 4, 1, 7688, 0, 50, 9], [32, 4, 1, 1, 5, 1, 4, 1, 0, 0, 40, 9], [42, 4, 1, 1, 5, 0, 4, 1, 99999, 0, 40, 9], [32, 4, 1, 1, 8, 0, 4, 1, 15024, 0, 50, 9], [53, 7, 5, 1, 8, 0, 4, 1, 0, 0, 42, 9], [52, 1, 1, 1, 8, 0, 4, 1, 0, 0, 45, 9]], "uncovered_true": [], "uncovered_false": []}, {"covered_true": [[52, 7, 5, 1, 5, 3, 4, 1, 0, 0, 40, 9], [27, 4, 1, 1, 8, 3, 4, 1, 0, 0, 40, 9], [28, 4, 1, 1, 6, 3, 4, 1, 0, 0, 60, 9], [46, 6, 5, 1, 2, 3, 4, 1, 0, 0, 50, 9], [53, 2, 5, 1, 5, 3, 2, 0, 0, 1669, 35, 9], [27, 4, 5, 1, 8, 3, 4, 0, 0, 0, 40, 9], [25, 4, 1, 1, 8, 3, 4, 0, 0, 0, 40, 9], [29, 6, 5, 1, 2, 3, 4, 1, 0, 0, 30, 9], [64, 0, 1, 1, 0, 3, 4, 1, 0, 0, 50, 9], [63, 0, 5, 1, 0, 3, 4, 1, 0, 0, 30, 9]], "covered_false": [[50, 5, 1, 1, 8, 3, 4, 1, 15024, 0, 60, 9], [45, 6, 1, 1, 6, 3, 4, 1, 14084, 0, 45, 9], [37, 4, 1, 1, 8, 3, 4, 1, 15024, 0, 40, 9], [33, 4, 1, 1, 8, 3, 4, 1, 15024, 0, 60, 9], [41, 6, 5, 1, 8, 3, 4, 1, 7298, 0, 70, 9], [42, 6, 1, 1, 2, 3, 4, 1, 15024, 0, 60, 9]], "uncovered_true": [], "uncovered_false": []}, {"covered_true": [[41, 4, 1, 1, 1, 3, 4, 1, 0, 0, 40, 9], [55, 2, 5, 1, 8, 3, 4, 1, 0, 0, 50, 9], [35, 4, 5, 1, 5, 3, 4, 0, 0, 0, 32, 9], [31, 4, 1, 1, 2, 3, 4, 1, 0, 0, 40, 9], [47, 4, 1, 1, 1, 3, 4, 1, 0, 0, 40, 9], [33, 4, 5, 1, 5, 3, 4, 1, 0, 0, 40, 9], [58, 0, 1, 1, 0, 3, 4, 0, 0, 0, 50, 9], [44, 6, 1, 1, 2, 3, 4, 1, 0, 0, 90, 9], [30, 4, 1, 1, 6, 3, 4, 1, 0, 0, 40, 9], [25, 4, 1, 1, 5, 3, 4, 1, 0, 0, 40, 9]], "covered_false": [], "uncovered_true": [], "uncovered_false": []}], "all_precision": 0, "num_preds": 1000000, "success": true, "names": ["Marital Status = Never-Married", "Relationship = Own-child", "Capital Gain <= 0.00"], "prediction": [0], "instance": [47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0], "instances": [[47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0]]}}}']}]}
+
+```
+
+### Cleanup
+
+```bash
+seldon pipeline unload income-production
+seldon model unload income-preprocess
+seldon model unload income
+seldon model unload income-drift
+seldon model unload income-outlier
+seldon model unload income-explainer
 ```
diff --git a/docs/examples/inference.md b/docs/examples/inference.md
index 1872e23984..c486c2cc81 100644
--- a/docs/examples/inference.md
+++ b/docs/examples/inference.md
@@ -1,8 +1,359 @@
 # Inference Examples
 
-Run these examples from the `samples` folder.
+We will show:
 
+* Model inference to a Tensorflow model
+  * REST and gRPC using seldon CLI, curl and grpcurl
+* Pipeline inference
+  * REST and gRPC using seldon CLI, curl and grpcurl
+
+
+```python
+%env INFER_ENDPOINT=0.0.0.0:9000
+```
+
+```
+env: INFER_ENDPOINT=0.0.0.0:9000
+
+```
+
+### Tensorflow Model
+
+```bash
+cat ./models/tfsimple1.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+Load the model.
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+```
+
+```json
+{}
+
+```
+
+Wait for the model to be ready.
+
+```bash
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model infer tfsimple1 --inference-host ${INFER_ENDPOINT} \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{
+	"model_name": "tfsimple1_1",
+	"model_version": "1",
+	"outputs": [
+		{
+			"name": "OUTPUT0",
+			"datatype": "INT32",
+			"shape": [
+				1,
+				16
+			],
+			"data": [
+				2,
+				4,
+				6,
+				8,
+				10,
+				12,
+				14,
+				16,
+				18,
+				20,
+				22,
+				24,
+				26,
+				28,
+				30,
+				32
+			]
+		},
+		{
+			"name": "OUTPUT1",
+			"datatype": "INT32",
+			"shape": [
+				1,
+				16
+			],
+			"data": [
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0
+			]
+		}
+	]
+}
+
+```
+
+```bash
+seldon model infer tfsimple1 --inference-mode grpc  --inference-host ${INFER_ENDPOINT} \
+    '{"model_name":"tfsimple1","inputs":[{"name":"INPUT0","contents":{"intContents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"intContents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{"modelName":"tfsimple1_1","modelVersion":"1","outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}
+
+```
+
+```bash
+curl http://${INFER_ENDPOINT}/v2/models/tfsimple1/infer -H "Content-Type: application/json" -H "seldon-model: tfsimple1" \
+        -d '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{"model_name":"tfsimple1_1","model_version":"1","outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":[1,16],"data":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]},{"name":"OUTPUT1","datatype":"INT32","shape":[1,16],"data":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}]}
+
+```
+
+```bash
+grpcurl -d '{"model_name":"tfsimple1","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}' \
+    -plaintext \
+    -import-path ../apis \
+    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    -rpc-header seldon-model:tfsimple1 \
+    ${INFER_ENDPOINT} inference.GRPCInferenceService/ModelInfer
+```
+
+```json
+{
+  "modelName": "tfsimple1_1",
+  "modelVersion": "1",
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ]
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ]
+    }
+  ],
+  "rawOutputContents": [
+    "AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==",
+    "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="
+  ]
+}
 
-```{include} ../../../../samples/inference.md
 ```
 
+```bash
+cat ./pipelines/tfsimple.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple
+spec:
+  steps:
+    - name: tfsimple1
+  output:
+    steps:
+    - tfsimple1
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimple.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon pipeline status tfsimple -w PipelineReady
+```
+
+```json
+{"pipelineName":"tfsimple","versions":[{"pipeline":{"name":"tfsimple","uid":"cg5fm6c6dpcs73c4qhe0","version":1,"steps":[{"name":"tfsimple1"}],"output":{"steps":["tfsimple1.outputs"]},"kubernetesMeta":{}},"state":{"pipelineVersion":1,"status":"PipelineReady","reason":"created pipeline","lastChangeTimestamp":"2023-03-10T09:40:41.317797761Z","modelsReady":true}}]}
+
+```
+
+```bash
+seldon pipeline infer tfsimple  --inference-host ${INFER_ENDPOINT} \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{
+	"model_name": "",
+	"outputs": [
+		{
+			"data": [
+				2,
+				4,
+				6,
+				8,
+				10,
+				12,
+				14,
+				16,
+				18,
+				20,
+				22,
+				24,
+				26,
+				28,
+				30,
+				32
+			],
+			"name": "OUTPUT0",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		},
+		{
+			"data": [
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0,
+				0
+			],
+			"name": "OUTPUT1",
+			"shape": [
+				1,
+				16
+			],
+			"datatype": "INT32"
+		}
+	]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimple --inference-mode grpc  --inference-host ${INFER_ENDPOINT} \
+    '{"model_name":"tfsimple1","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{"outputs":[{"name":"OUTPUT0","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{"name":"OUTPUT1","datatype":"INT32","shape":["1","16"],"contents":{"intContents":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}
+
+```
+
+```bash
+curl http://${INFER_ENDPOINT}/v2/models/tfsimple1/infer -H "Content-Type: application/json" -H "seldon-model: tfsimple.pipeline" \
+        -d '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```json
+{"model_name":"","outputs":[{"data":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32],"name":"OUTPUT0","shape":[1,16],"datatype":"INT32"},{"data":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"name":"OUTPUT1","shape":[1,16],"datatype":"INT32"}]}
+
+```
+
+```bash
+grpcurl -d '{"model_name":"tfsimple1","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}' \
+    -plaintext \
+    -import-path ../apis \
+    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    -rpc-header seldon-model:tfsimple.pipeline \
+    ${INFER_ENDPOINT} inference.GRPCInferenceService/ModelInfer
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ]
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ]
+    }
+  ],
+  "rawOutputContents": [
+    "AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==",
+    "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="
+  ]
+}
+
+```
+
+```bash
+seldon pipeline unload tfsimple
+seldon model unload tfsimple1
+```
+
+```json
+{}
+{}
+
+```
+
+```python
+
+```
diff --git a/docs/examples/k8s-clusterwide.md b/docs/examples/k8s-clusterwide.md
index 5981fca689..07eae9091e 100644
--- a/docs/examples/k8s-clusterwide.md
+++ b/docs/examples/k8s-clusterwide.md
@@ -1,8 +1,663 @@
-# Kubernetes Clusterwide Example
+# Seldon V2 Multi-Namespace Kubernetes Example
 
-Run these examples from the `samples` folder.
+```bash
+helm upgrade --install seldon-core-v2-crds  ../k8s/helm-charts/seldon-core-v2-crds -n seldon-mesh
+```
+
+```
+Release "seldon-core-v2-crds" does not exist. Installing it now.
+NAME: seldon-core-v2-crds
+LAST DEPLOYED: Tue Aug 15 11:01:03 2023
+NAMESPACE: seldon-mesh
+STATUS: deployed
+REVISION: 1
+TEST SUITE: None
+
+```
+
+The below setup also illustrates using kafka specific prefixes for topics and consumerIds
+for isolation where the kafka cluster is shared with other applications and you want to
+enforce constraints. You would not strictly need this in this example as we install Kafka
+just for Seldon here.
+
+```bash
+helm upgrade --install seldon-v2 ../k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh \
+    --set controller.clusterwide=true \
+    --set kafka.topicPrefix=myorg \
+    --set kafka.consumerGroupIdPrefix=myorg
+```
+
+```
+Release "seldon-v2" does not exist. Installing it now.
+NAME: seldon-v2
+LAST DEPLOYED: Tue Aug 15 11:01:07 2023
+NAMESPACE: seldon-mesh
+STATUS: deployed
+REVISION: 1
+TEST SUITE: None
+
+```
+
+```bash
+kubectl create namespace ns1
+kubectl create namespace ns2
+```
+
+```
+namespace/ns1 created
+namespace/ns2 created
+
+```
+
+```bash
+helm install seldon-v2-runtime ../k8s/helm-charts/seldon-core-v2-runtime  -n ns1 --wait
+```
+
+```yaml
+NAME: seldon-v2-runtime
+LAST DEPLOYED: Tue Aug 15 11:01:11 2023
+NAMESPACE: ns1
+STATUS: deployed
+REVISION: 1
+TEST SUITE: None
+
+```
+
+```bash
+helm install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n ns1 --wait
+```
+
+```yaml
+NAME: seldon-v2-servers
+LAST DEPLOYED: Tue Aug 15 10:47:31 2023
+NAMESPACE: ns1
+STATUS: deployed
+REVISION: 1
+TEST SUITE: None
+
+```
+
+```bash
+helm install seldon-v2-runtime ../k8s/helm-charts/seldon-core-v2-runtime  -n ns2 --wait
+```
+
+```yaml
+NAME: seldon-v2-runtime
+LAST DEPLOYED: Tue Aug 15 10:53:12 2023
+NAMESPACE: ns2
+STATUS: deployed
+REVISION: 1
+TEST SUITE: None
+
+```
+
+```bash
+helm install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n ns2 --wait
+```
+
+```yaml
+NAME: seldon-v2-servers
+LAST DEPLOYED: Tue Aug 15 10:53:28 2023
+NAMESPACE: ns2
+STATUS: deployed
+REVISION: 1
+TEST SUITE: None
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s server --all -n ns1
+```
+
+```
+server.mlops.seldon.io/mlserver condition met
+server.mlops.seldon.io/triton condition met
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s server --all -n ns2
+```
+
+```
+server.mlops.seldon.io/mlserver condition met
+server.mlops.seldon.io/triton condition met
+
+```
+
+```python
+MESH_IP=!kubectl get svc seldon-mesh -n ns1 -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+MESH_IP_NS1=MESH_IP[0]
+import os
+os.environ['MESH_IP_NS1'] = MESH_IP_NS1
+MESH_IP_NS1
+```
+
+```
+'172.18.255.2'
+
+```
+
+```python
+MESH_IP=!kubectl get svc seldon-mesh -n ns2 -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+MESH_IP_NS2=MESH_IP[0]
+import os
+os.environ['MESH_IP_NS2'] = MESH_IP_NS2
+MESH_IP_NS2
+```
+
+```
+'172.18.255.4'
+
+```
+
+### Run Models in Different Namespaces
+
+```bash
+cat ./models/sklearn-iris-gs.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
+
+```
+
+```bash
+kubectl create -f ./models/sklearn-iris-gs.yaml -n ns1
+```
+
+```
+model.mlops.seldon.io/iris created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ns1
+```
+
+```
+model.mlops.seldon.io/iris condition met
+
+```
+
+```bash
+seldon model infer iris --inference-host ${MESH_IP_NS1}:80 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```json
+{
+	"model_name": "iris_1",
+	"model_version": "1",
+	"id": "3ca1757c-df02-4e57-87c1-38311bcc5943",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"parameters": {
+				"content_type": "np"
+			},
+			"data": [
+				2
+			]
+		}
+	]
+}
+
+```
+
+```bash
+seldon model infer iris --inference-mode grpc --inference-host ${MESH_IP_NS1}:80 \
+   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' | jq -M .
+```
+
+```json
+{
+  "modelName": "iris_1",
+  "modelVersion": "1",
+  "outputs": [
+    {
+      "name": "predict",
+      "datatype": "INT64",
+      "shape": [
+        "1",
+        "1"
+      ],
+      "parameters": {
+        "content_type": {
+          "stringParam": "np"
+        }
+      },
+      "contents": {
+        "int64Contents": [
+          "2"
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+kubectl create -f ./models/sklearn-iris-gs.yaml -n ns2
+```
+
+```
+model.mlops.seldon.io/iris created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ns2
+```
+
+```
+model.mlops.seldon.io/iris condition met
+
+```
+
+```bash
+seldon model infer iris --inference-host ${MESH_IP_NS2}:80 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```json
+{
+	"model_name": "iris_1",
+	"model_version": "1",
+	"id": "f706a23e-775f-4765-bd18-2e98d83bf7d5",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"parameters": {
+				"content_type": "np"
+			},
+			"data": [
+				2
+			]
+		}
+	]
+}
+
+```
+
+```bash
+seldon model infer iris --inference-mode grpc --inference-host ${MESH_IP_NS2}:80 \
+   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' | jq -M .
+```
+
+```json
+{
+  "modelName": "iris_1",
+  "modelVersion": "1",
+  "outputs": [
+    {
+      "name": "predict",
+      "datatype": "INT64",
+      "shape": [
+        "1",
+        "1"
+      ],
+      "parameters": {
+        "content_type": {
+          "stringParam": "np"
+        }
+      },
+      "contents": {
+        "int64Contents": [
+          "2"
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+kubectl delete -f ./models/sklearn-iris-gs.yaml -n ns1
+kubectl delete -f ./models/sklearn-iris-gs.yaml -n ns2
+```
+
+```
+model.mlops.seldon.io "iris" deleted
+model.mlops.seldon.io "iris" deleted
+
+```
+
+## Pipelines
+
+```bash
+kubectl create -f ./models/tfsimple1.yaml -n ns1
+kubectl create -f ./models/tfsimple2.yaml -n ns1
+kubectl create -f ./models/tfsimple1.yaml -n ns2
+kubectl create -f ./models/tfsimple2.yaml -n ns2
+```
+
+```
+model.mlops.seldon.io/tfsimple1 created
+model.mlops.seldon.io/tfsimple2 created
+model.mlops.seldon.io/tfsimple1 created
+model.mlops.seldon.io/tfsimple2 created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ns1
+kubectl wait --for condition=ready --timeout=300s model --all -n ns2
+```
+
+```
+model.mlops.seldon.io/tfsimple1 condition met
+model.mlops.seldon.io/tfsimple2 condition met
+model.mlops.seldon.io/tfsimple1 condition met
+model.mlops.seldon.io/tfsimple2 condition met
 
+```
+
+```bash
+kubectl create -f ./pipelines/tfsimples.yaml -n ns1
+kubectl create -f ./pipelines/tfsimples.yaml -n ns2
+```
+
+```
+pipeline.mlops.seldon.io/tfsimples created
+pipeline.mlops.seldon.io/tfsimples created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s pipeline --all -n ns1
+kubectl wait --for condition=ready --timeout=300s pipeline --all -n ns2
+```
+
+```
+pipeline.mlops.seldon.io/tfsimples condition met
+pipeline.mlops.seldon.io/tfsimples condition met
+
+```
+
+```bash
+seldon pipeline infer tfsimples --inference-mode grpc --inference-host ${MESH_IP_NS1}:80 \
+    '{"model_name":"simple","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimples --inference-mode grpc --inference-host ${MESH_IP_NS2}:80 \
+    '{"model_name":"simple","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+If you have installed Kafka via the ansible playbook setup-ecosystem then you can use the
+following command to see the consumer group ids which are reflecting the settings we created.
+
+```bash
+kubectl exec seldon-kafka-0 -n seldon-mesh -- bin/kafka-consumer-groups.sh --list --bootstrap-server localhost:9092
+```
 
-```{include} ../../../../samples/k8s-clusterwide.md
 ```
+myorg-ns2-seldon-pipelinegateway-dfd61b49-4bb9-4684-adce-0b7cc215d3af
+myorg-ns2-seldon-modelgateway-17
+myorg-ns1-seldon-pipelinegateway-d4fc83e6-29cb-442e-90cd-92a389961cfe
+myorg-ns2-seldon-modelgateway-60
+myorg-ns2-seldon-dataflow-73d465744b7b1b5be20e88d6245e50bd
+myorg-ns1-seldon-modelgateway-60
+myorg-ns1-seldon-modelgateway-17
+myorg-ns1-seldon-dataflow-f563e04e093caa20c03e6eced084331b
 
+```
+
+We can similarly show the topics that have been created.
+
+```bash
+kubectl exec seldon-kafka-0 -n seldon-mesh -- bin/kafka-topics.sh --bootstrap-server=localhost:9092 --list
+```
+
+```
+__consumer_offsets
+myorg.ns1.errors.errors
+myorg.ns1.model.iris.inputs
+myorg.ns1.model.iris.outputs
+myorg.ns1.model.tfsimple1.inputs
+myorg.ns1.model.tfsimple1.outputs
+myorg.ns1.model.tfsimple2.inputs
+myorg.ns1.model.tfsimple2.outputs
+myorg.ns1.pipeline.tfsimples.inputs
+myorg.ns1.pipeline.tfsimples.outputs
+myorg.ns2.errors.errors
+myorg.ns2.model.iris.inputs
+myorg.ns2.model.iris.outputs
+myorg.ns2.model.tfsimple1.inputs
+myorg.ns2.model.tfsimple1.outputs
+myorg.ns2.model.tfsimple2.inputs
+myorg.ns2.model.tfsimple2.outputs
+myorg.ns2.pipeline.tfsimples.inputs
+myorg.ns2.pipeline.tfsimples.outputs
+
+```
+
+```bash
+kubectl delete -f ./pipelines/tfsimples.yaml -n ns1
+kubectl delete -f ./pipelines/tfsimples.yaml -n ns2
+```
+
+```
+pipeline.mlops.seldon.io "tfsimples" deleted
+pipeline.mlops.seldon.io "tfsimples" deleted
+
+```
+
+```bash
+kubectl delete -f ./models/tfsimple1.yaml -n ns1
+kubectl delete -f ./models/tfsimple2.yaml -n ns1
+kubectl delete -f ./models/tfsimple1.yaml -n ns2
+kubectl delete -f ./models/tfsimple2.yaml -n ns2
+```
+
+```
+model.mlops.seldon.io "tfsimple1" deleted
+model.mlops.seldon.io "tfsimple2" deleted
+model.mlops.seldon.io "tfsimple1" deleted
+model.mlops.seldon.io "tfsimple2" deleted
+
+```
+
+## TearDown
+
+```bash
+helm delete seldon-v2-servers -n ns1 --wait
+helm delete seldon-v2-servers -n ns2 --wait
+```
+
+```
+release "seldon-v2-servers" uninstalled
+release "seldon-v2-servers" uninstalled
+
+```
+
+```bash
+helm delete seldon-v2-runtime -n ns1 --wait
+helm delete seldon-v2-runtime -n ns2 --wait
+```
+
+```
+release "seldon-v2-runtime" uninstalled
+release "seldon-v2-runtime" uninstalled
+
+```
+
+```bash
+helm delete seldon-v2 -n seldon-mesh --wait
+```
+
+```
+release "seldon-v2" uninstalled
+
+```
+
+```bash
+helm delete seldon-core-v2-crds -n seldon-mesh
+```
+
+```
+release "seldon-core-v2-crds" uninstalled
+
+```
+
+```bash
+kubectl delete namespace ns1
+kubectl delete namespace ns2
+```
+
+```
+namespace "ns1" deleted
+namespace "ns2" deleted
+
+```
diff --git a/docs/examples/k8s-examples.md b/docs/examples/k8s-examples.md
index 32e03c58a4..4124cfbe28 100644
--- a/docs/examples/k8s-examples.md
+++ b/docs/examples/k8s-examples.md
@@ -1,8 +1,866 @@
-# Kubernetes Examples
+## Seldon V2 Kubernetes Examples
 
-Run these examples from the `samples` folder.
+```python
+import os
+os.environ["NAMESPACE"] = "seldon-mesh"
+```
+
+```python
+MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+MESH_IP=MESH_IP[0]
+import os
+os.environ['MESH_IP'] = MESH_IP
+MESH_IP
+```
+
+```
+'172.18.255.2'
+
+```
+
+### Model
+
+```bash
+cat ./models/sklearn-iris-gs.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
+
+```
+
+```bash
+kubectl create -f ./models/sklearn-iris-gs.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris condition met
+
+```
+
+```bash
+kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M .
+```
+
+```json
+{
+  "conditions": [
+    {
+      "lastTransitionTime": "2023-06-30T10:01:52Z",
+      "message": "ModelAvailable",
+      "status": "True",
+      "type": "ModelReady"
+    },
+    {
+      "lastTransitionTime": "2023-06-30T10:01:52Z",
+      "status": "True",
+      "type": "Ready"
+    }
+  ],
+  "replicas": 1
+}
+
+```
+
+```bash
+seldon model infer iris --inference-host ${MESH_IP}:80 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```json
+{
+	"model_name": "iris_1",
+	"model_version": "1",
+	"id": "7fd401e1-3dce-46f5-9668-902aea652b89",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"parameters": {
+				"content_type": "np"
+			},
+			"data": [
+				2
+			]
+		}
+	]
+}
+
+```
+
+```bash
+seldon model infer iris --inference-mode grpc --inference-host ${MESH_IP}:80 \
+   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' | jq -M .
+```
+
+```json
+{
+  "modelName": "iris_1",
+  "modelVersion": "1",
+  "outputs": [
+    {
+      "name": "predict",
+      "datatype": "INT64",
+      "shape": [
+        "1",
+        "1"
+      ],
+      "parameters": {
+        "content_type": {
+          "stringParam": "np"
+        }
+      },
+      "contents": {
+        "int64Contents": [
+          "2"
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+kubectl get server mlserver -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M .
+```
+
+```json
+{
+  "conditions": [
+    {
+      "lastTransitionTime": "2023-06-30T09:59:12Z",
+      "status": "True",
+      "type": "Ready"
+    },
+    {
+      "lastTransitionTime": "2023-06-30T09:59:12Z",
+      "reason": "StatefulSet replicas matches desired replicas",
+      "status": "True",
+      "type": "StatefulSetReady"
+    }
+  ],
+  "loadedModels": 1,
+  "replicas": 1,
+  "selector": "seldon-server-name=mlserver"
+}
+
+```
+
+```bash
+kubectl delete -f ./models/sklearn-iris-gs.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "iris" deleted
+
+```
+
+### Experiment
+
+```bash
+cat ./models/sklearn1.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
+
+```
+
+```bash
+cat ./models/sklearn2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris2
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
+
+```
+
+```bash
+kubectl create -f ./models/sklearn1.yaml -n ${NAMESPACE}
+kubectl create -f ./models/sklearn2.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris created
+model.mlops.seldon.io/iris2 created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/iris condition met
+model.mlops.seldon.io/iris2 condition met
+
+```
+
+```bash
+cat ./experiments/ab-default-model.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: experiment-sample
+spec:
+  default: iris
+  candidates:
+  - name: iris
+    weight: 50
+  - name: iris2
+    weight: 50
+
+```
+
+```bash
+kubectl create -f ./experiments/ab-default-model.yaml -n ${NAMESPACE}
+```
+
+```
+experiment.mlops.seldon.io/experiment-sample created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s experiment --all -n ${NAMESPACE}
+```
+
+```
+experiment.mlops.seldon.io/experiment-sample condition met
+
+```
+
+```bash
+seldon model infer --inference-host ${MESH_IP}:80 -i 50 iris \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::29 :iris_1::21]
+
+```
+
+```bash
+kubectl delete -f ./experiments/ab-default-model.yaml -n ${NAMESPACE}
+kubectl delete -f ./models/sklearn1.yaml -n ${NAMESPACE}
+kubectl delete -f ./models/sklearn2.yaml -n ${NAMESPACE}
+```
+
+```
+experiment.mlops.seldon.io "experiment-sample" deleted
+model.mlops.seldon.io "iris" deleted
+model.mlops.seldon.io "iris2" deleted
+
+```
+
+### Pipeline - model chain
+
+```bash
+cat ./models/tfsimple1.yaml
+cat ./models/tfsimple2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+kubectl create -f ./models/tfsimple1.yaml -n ${NAMESPACE}
+kubectl create -f ./models/tfsimple2.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/tfsimple1 created
+model.mlops.seldon.io/tfsimple2 created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/tfsimple1 condition met
+model.mlops.seldon.io/tfsimple2 condition met
+
+```
+
+```bash
+cat ./pipelines/tfsimples.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimples
+spec:
+  steps:
+    - name: tfsimple1
+    - name: tfsimple2
+      inputs:
+      - tfsimple1
+      tensorMap:
+        tfsimple1.outputs.OUTPUT0: INPUT0
+        tfsimple1.outputs.OUTPUT1: INPUT1
+  output:
+    steps:
+    - tfsimple2
+
+```
+
+```bash
+kubectl create -f ./pipelines/tfsimples.yaml -n ${NAMESPACE}
+```
+
+```
+pipeline.mlops.seldon.io/tfsimples created
+
+```
 
+```bash
+kubectl wait --for condition=ready --timeout=300s pipeline --all -n ${NAMESPACE}
+```
+
+```
+pipeline.mlops.seldon.io/tfsimples condition met
+
+```
+
+```bash
+seldon pipeline infer tfsimples --inference-mode grpc --inference-host ${MESH_IP}:80 \
+    '{"model_name":"simple","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+kubectl delete -f ./pipelines/tfsimples.yaml -n ${NAMESPACE}
+```
+
+```
+pipeline.mlops.seldon.io "tfsimples" deleted
+
+```
+
+```bash
+kubectl delete -f ./models/tfsimple1.yaml -n ${NAMESPACE}
+kubectl delete -f ./models/tfsimple2.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "tfsimple1" deleted
+model.mlops.seldon.io "tfsimple2" deleted
+
+```
+
+### Pipeline - model join
+
+```bash
+cat ./models/tfsimple1.yaml
+cat ./models/tfsimple2.yaml
+cat ./models/tfsimple3.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple3
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+kubectl create -f ./models/tfsimple1.yaml -n ${NAMESPACE}
+kubectl create -f ./models/tfsimple2.yaml -n ${NAMESPACE}
+kubectl create -f ./models/tfsimple3.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/tfsimple1 created
+model.mlops.seldon.io/tfsimple2 created
+model.mlops.seldon.io/tfsimple3 created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/tfsimple1 condition met
+model.mlops.seldon.io/tfsimple2 condition met
+model.mlops.seldon.io/tfsimple3 condition met
+
+```
+
+```bash
+cat ./pipelines/tfsimples-join.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: join
+spec:
+  steps:
+    - name: tfsimple1
+    - name: tfsimple2
+    - name: tfsimple3
+      inputs:
+      - tfsimple1.outputs.OUTPUT0
+      - tfsimple2.outputs.OUTPUT1
+      tensorMap:
+        tfsimple1.outputs.OUTPUT0: INPUT0
+        tfsimple2.outputs.OUTPUT1: INPUT1
+  output:
+    steps:
+    - tfsimple3
+
+```
+
+```bash
+kubectl create -f ./pipelines/tfsimples-join.yaml -n ${NAMESPACE}
+```
+
+```
+pipeline.mlops.seldon.io/join created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s pipeline --all -n ${NAMESPACE}
+```
+
+```
+pipeline.mlops.seldon.io/join condition met
+
+```
+
+```bash
+seldon pipeline infer join --inference-mode grpc --inference-host ${MESH_IP}:80 \
+    '{"model_name":"simple","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+kubectl delete -f ./pipelines/tfsimples-join.yaml -n ${NAMESPACE}
+```
+
+```
+pipeline.mlops.seldon.io "join" deleted
 
-```{include} ../../../../samples/k8s-examples.md
 ```
 
+```bash
+kubectl delete -f ./models/tfsimple1.yaml -n ${NAMESPACE}
+kubectl delete -f ./models/tfsimple2.yaml -n ${NAMESPACE}
+kubectl delete -f ./models/tfsimple3.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "tfsimple1" deleted
+model.mlops.seldon.io "tfsimple2" deleted
+model.mlops.seldon.io "tfsimple3" deleted
+
+```
+
+## Explainer
+
+```bash
+cat ./models/income.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/classifier"
+  requirements:
+  - sklearn
+
+```
+
+```bash
+kubectl create -f ./models/income.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/income created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/income condition met
+
+```
+
+```bash
+kubectl get model income -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M .
+```
+
+```json
+{
+  "conditions": [
+    {
+      "lastTransitionTime": "2023-06-30T10:02:53Z",
+      "message": "ModelAvailable",
+      "status": "True",
+      "type": "ModelReady"
+    },
+    {
+      "lastTransitionTime": "2023-06-30T10:02:53Z",
+      "status": "True",
+      "type": "Ready"
+    }
+  ],
+  "replicas": 1
+}
+
+```
+
+```bash
+seldon model infer income --inference-host ${MESH_IP}:80 \
+     '{"inputs": [{"name": "predict", "shape": [1, 12], "datatype": "FP32", "data": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}'
+```
+
+```json
+{
+	"model_name": "income_1",
+	"model_version": "1",
+	"id": "f52acfeb-0f22-429f-8c7a-785ef17cd470",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"parameters": {
+				"content_type": "np"
+			},
+			"data": [
+				0
+			]
+		}
+	]
+}
+
+```
+
+```bash
+cat ./models/income-explainer.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-explainer
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/explainer"
+  explainer:
+    type: anchor_tabular
+    modelRef: income
+
+```
+
+```bash
+kubectl create -f ./models/income-explainer.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/income-explainer created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/income condition met
+model.mlops.seldon.io/income-explainer condition met
+
+```
+
+```bash
+kubectl get model income-explainer -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M .
+```
+
+```json
+{
+  "conditions": [
+    {
+      "lastTransitionTime": "2023-06-30T10:03:07Z",
+      "message": "ModelAvailable",
+      "status": "True",
+      "type": "ModelReady"
+    },
+    {
+      "lastTransitionTime": "2023-06-30T10:03:07Z",
+      "status": "True",
+      "type": "Ready"
+    }
+  ],
+  "replicas": 1
+}
+
+```
+
+```bash
+seldon model infer income-explainer --inference-host ${MESH_IP}:80 \
+     '{"inputs": [{"name": "predict", "shape": [1, 12], "datatype": "FP32", "data": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}'
+```
+
+```json
+{
+	"model_name": "income-explainer_1",
+	"model_version": "1",
+	"id": "3028a904-9bb3-42d7-bdb7-6e6993323ed7",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "explanation",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "BYTES",
+			"parameters": {
+				"content_type": "str"
+			},
+			"data": [
+				"{\"meta\": {\"name\": \"AnchorTabular\", \"type\": [\"blackbox\"], \"explanations\": [\"local\"], \"params\": {\"seed\": 1, \"disc_perc\": [25, 50, 75], \"threshold\": 0.95, \"delta\": 0.1, \"tau\": 0.15, \"batch_size\": 100, \"coverage_samples\": 10000, \"beam_size\": 1, \"stop_on_first\": false, \"max_anchor_size\": null, \"min_samples_start\": 100, \"n_covered_ex\": 10, \"binary_cache_size\": 10000, \"cache_margin\": 1000, \"verbose\": false, \"verbose_every\": 1, \"kwargs\": {}}, \"version\": \"0.9.1\"}, \"data\": {\"anchor\": [\"Marital Status = Never-Married\", \"Relationship = Own-child\"], \"precision\": 0.9705882352941176, \"coverage\": 0.0699, \"raw\": {\"feature\": [3, 5], \"mean\": [0.8094218415417559, 0.9705882352941176], \"precision\": [0.8094218415417559, 0.9705882352941176], \"coverage\": [0.3036, 0.0699], \"examples\": [{\"covered_true\": [[23, 4, 1, 1, 5, 1, 4, 0, 0, 0, 40, 9], [44, 4, 1, 1, 8, 0, 4, 1, 0, 0, 40, 9], [60, 2, 5, 1, 5, 1, 4, 0, 0, 0, 25, 9], [52, 4, 1, 1, 2, 0, 4, 1, 0, 0, 50, 9], [66, 6, 1, 1, 8, 0, 4, 1, 0, 0, 8, 9], [52, 4, 1, 1, 8, 0, 4, 1, 0, 0, 40, 9], [27, 4, 1, 1, 1, 1, 4, 1, 0, 0, 35, 9], [48, 4, 1, 1, 6, 0, 4, 1, 0, 0, 45, 9], [45, 6, 1, 1, 5, 0, 4, 1, 0, 0, 40, 9], [40, 2, 1, 1, 5, 4, 4, 0, 0, 0, 45, 9]], \"covered_false\": [[42, 6, 5, 1, 6, 0, 4, 1, 99999, 0, 80, 9], [29, 4, 1, 1, 8, 1, 4, 1, 0, 0, 50, 9], [49, 4, 1, 1, 8, 0, 4, 1, 0, 0, 50, 9], [34, 4, 5, 1, 8, 0, 4, 1, 0, 0, 40, 9], [38, 2, 1, 1, 5, 5, 4, 0, 7688, 0, 40, 9], [45, 7, 5, 1, 5, 0, 4, 1, 0, 0, 45, 9], [43, 4, 2, 1, 5, 0, 4, 1, 99999, 0, 55, 9], [47, 4, 5, 1, 6, 1, 4, 1, 27828, 0, 60, 9], [42, 6, 1, 1, 2, 0, 4, 1, 15024, 0, 60, 9], [56, 4, 1, 1, 6, 0, 2, 1, 7688, 0, 45, 9]], \"uncovered_true\": [], \"uncovered_false\": []}, {\"covered_true\": [[23, 4, 1, 1, 4, 3, 4, 1, 0, 0, 40, 9], [50, 2, 5, 1, 8, 3, 2, 1, 0, 0, 45, 9], [24, 4, 1, 1, 7, 3, 4, 0, 0, 0, 40, 3], [62, 4, 5, 1, 5, 3, 4, 1, 0, 0, 40, 9], [22, 4, 1, 1, 5, 3, 4, 1, 0, 0, 40, 9], [44, 4, 1, 1, 1, 3, 4, 0, 0, 0, 40, 9], [46, 4, 1, 1, 4, 3, 4, 1, 0, 0, 40, 9], [44, 4, 1, 1, 2, 3, 4, 1, 0, 0, 40, 9], [25, 4, 5, 1, 5, 3, 4, 1, 0, 0, 35, 9], [32, 2, 5, 1, 5, 3, 4, 1, 0, 0, 50, 9]], \"covered_false\": [[57, 5, 5, 1, 6, 3, 4, 1, 99999, 0, 40, 9], [44, 4, 1, 1, 8, 3, 4, 1, 7688, 0, 60, 9], [43, 2, 5, 1, 4, 3, 2, 0, 8614, 0, 47, 9], [56, 5, 2, 1, 5, 3, 4, 1, 99999, 0, 70, 9]], \"uncovered_true\": [], \"uncovered_false\": []}], \"all_precision\": 0, \"num_preds\": 1000000, \"success\": true, \"names\": [\"Marital Status = Never-Married\", \"Relationship = Own-child\"], \"prediction\": [0], \"instance\": [47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0], \"instances\": [[47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0]]}}}"
+			]
+		}
+	]
+}
+
+```
+
+```bash
+kubectl delete -f ./models/income.yaml -n ${NAMESPACE}
+kubectl delete -f ./models/income-explainer.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "income" deleted
+model.mlops.seldon.io "income-explainer" deleted
+
+```
diff --git a/docs/examples/k8s-pvc.md b/docs/examples/k8s-pvc.md
index f4bb229d18..0b49be50a1 100644
--- a/docs/examples/k8s-pvc.md
+++ b/docs/examples/k8s-pvc.md
@@ -1,4 +1,269 @@
 # Kubernetes Server with PVC
 
-```{include} ../../../../samples/examples/k8s-pvc/k8s-pvc.md
+```python
+import os
+os.environ["NAMESPACE"] = "seldon-mesh"
 ```
+
+
+```python
+MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+MESH_IP=MESH_IP[0]
+import os
+os.environ['MESH_IP'] = MESH_IP
+MESH_IP
+```
+
+
+
+
+    '172.19.255.1'
+
+
+
+### Kind cluster setup
+
+To run this example in Kind we need to start Kind with access to a local folder where are
+models are location. In this example we will use a folder in `/tmp` and associate that with
+a path in the container.
+
+
+```python
+!cat kind-config.yaml
+```
+
+    apiVersion: kind.x-k8s.io/v1alpha4
+    kind: Cluster
+    nodes:
+    - role: control-plane
+      extraMounts:
+        - hostPath: /tmp/models
+          containerPath: /models
+
+
+
+To start a Kind cluster with these settings using our ansible script you can run from the project root folder
+
+```
+ansible-playbook ansible/playbooks/kind-cluster.yaml -e kind_config_file=${PWD}/samples/examples/local-pvc/kind-config.yaml
+```
+
+[**Now you should finish the Seldon install following the docs.**](https://docs.seldon.io/projects/seldon-core/en/v2/contents/getting-started/index.html)
+
+Create the local folder we will use for our models and copy an example iris sklearn model to it.
+
+
+```python
+!mkdir -p /tmp/models
+!gsutil cp -r gs://seldon-models/mlserver/iris /tmp/models
+```
+
+### Create Server with PVC
+
+Here we create a storage class and associated persistent colume referencing the `/models` folder
+where our models are stored.
+
+
+```python
+!cat pvc.yaml
+```
+
+    apiVersion: storage.k8s.io/v1
+    kind: StorageClass
+    metadata:
+      name: local-path-immediate
+    provisioner: rancher.io/local-path
+    reclaimPolicy: Delete
+    mountOptions:
+      - debug
+    volumeBindingMode: Immediate
+    ---
+    kind: PersistentVolume
+    apiVersion: v1
+    metadata:
+      name: ml-models-pv
+      namespace: seldon-mesh
+      labels:
+        type: local
+    spec:
+      storageClassName: local-path-immediate
+      capacity:
+        storage: 1Gi
+      accessModes:
+        - ReadWriteOnce
+      hostPath:
+        path: "/models"
+    ---
+    kind: PersistentVolumeClaim
+    apiVersion: v1
+    metadata:
+      name: ml-models-pvc
+      namespace: seldon-mesh
+    spec:
+      storageClassName: local-path-immediate
+      accessModes:
+        - ReadWriteOnce
+      resources:
+        requests:
+          storage: 1Gi
+      selector:
+        matchLabels:
+          type: local
+
+
+Now we create a new Server based on the provided MLServer configuration but extend it with our
+PVC by adding this to the rclone container which will allow rclone to move models from this PVC
+onto the server.
+
+We also add a new capability `pvc` to allow us to schedule models to this server that has the PVC.
+
+
+```python
+!cat server.yaml
+```
+
+    apiVersion: mlops.seldon.io/v1alpha1
+    kind: Server
+    metadata:
+      name: mlserver-pvc
+    spec:
+      serverConfig: mlserver
+      extraCapabilities:
+      - "pvc"
+      podSpec:
+        volumes:
+        - name: models-pvc
+          persistentVolumeClaim:
+            claimName: ml-models-pvc
+        containers:
+        - name: rclone
+          volumeMounts:
+          - name: models-pvc
+            mountPath: /var/models
+
+
+### SKLearn Model
+
+We use a simple sklearn iris classification model with the added `pvc` requirement so our
+MLServer with the PVC will be targeted during scheduling.
+
+
+```python
+!cat ./iris.yaml
+```
+
+    apiVersion: mlops.seldon.io/v1alpha1
+    kind: Model
+    metadata:
+      name: iris
+    spec:
+      storageUri: "/var/models/iris"
+      requirements:
+      - sklearn
+      - pvc
+
+
+
+```python
+!kubectl create -f iris.yaml -n ${NAMESPACE}
+```
+
+    model.mlops.seldon.io/iris created
+
+
+
+```python
+!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+    model.mlops.seldon.io/iris condition met
+
+
+
+```python
+!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M .
+```
+
+    {
+      "conditions": [
+        {
+          "lastTransitionTime": "2022-12-24T11:04:37Z",
+          "status": "True",
+          "type": "ModelReady"
+        },
+        {
+          "lastTransitionTime": "2022-12-24T11:04:37Z",
+          "status": "True",
+          "type": "Ready"
+        }
+      ],
+      "replicas": 1
+    }
+
+
+
+```python
+!seldon model infer iris --inference-host ${MESH_IP}:80 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+    {
+    	"model_name": "iris_1",
+    	"model_version": "1",
+    	"id": "dc032bcc-3f4e-4395-a2e4-7c1e3ef56e9e",
+    	"parameters": {
+    		"content_type": null,
+    		"headers": null
+    	},
+    	"outputs": [
+    		{
+    			"name": "predict",
+    			"shape": [
+    				1,
+    				1
+    			],
+    			"datatype": "INT64",
+    			"parameters": null,
+    			"data": [
+    				2
+    			]
+    		}
+    	]
+    }
+
+
+Do a gRPC inference call
+
+
+```python
+!seldon model infer iris --inference-mode grpc --inference-host ${MESH_IP}:80 \
+   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' | jq -M .
+```
+
+    {
+      "modelName": "iris_1",
+      "modelVersion": "1",
+      "outputs": [
+        {
+          "name": "predict",
+          "datatype": "INT64",
+          "shape": [
+            "1",
+            "1"
+          ],
+          "contents": {
+            "int64Contents": [
+              "2"
+            ]
+          }
+        }
+      ]
+    }
+
+
+
+```python
+!kubectl delete -f ./iris.yaml -n ${NAMESPACE}
+```
+
+    model.mlops.seldon.io "iris" deleted
diff --git a/docs/examples/local-examples.md b/docs/examples/local-examples.md
index a6e44d86ad..ab94301b27 100644
--- a/docs/examples/local-examples.md
+++ b/docs/examples/local-examples.md
@@ -1,8 +1,632 @@
-# Local Examples
+# Seldon V2 Non Kubernetes Local Examples
 
-Run these examples from the `samples` folder.
+### SKLearn Model
 
+We use a simple sklearn iris classification model
 
-```{include} ../../../../samples/local-examples.md
+```bash
+cat ./models/sklearn-iris-gs.yaml
 ```
 
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
+
+```
+
+Load the model
+
+```bash
+seldon model load -f ./models/sklearn-iris-gs.yaml
+```
+
+```json
+{}
+
+```
+
+Wait for the model to be ready
+
+```bash
+seldon model status iris -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+Do a REST inference call
+
+```bash
+seldon model infer iris \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```json
+{
+	"model_name": "iris_1",
+	"model_version": "1",
+	"id": "983bd95f-4b4d-4ff1-95b2-df9d6d089164",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"parameters": {
+				"content_type": "np"
+			},
+			"data": [
+				2
+			]
+		}
+	]
+}
+
+```
+
+Do a gRPC inference call
+
+```bash
+seldon model infer iris --inference-mode grpc \
+   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' | jq -M .
+```
+
+```json
+{
+  "modelName": "iris_1",
+  "modelVersion": "1",
+  "outputs": [
+    {
+      "name": "predict",
+      "datatype": "INT64",
+      "shape": [
+        "1",
+        "1"
+      ],
+      "parameters": {
+        "content_type": {
+          "stringParam": "np"
+        }
+      },
+      "contents": {
+        "int64Contents": [
+          "2"
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+Unload the model
+
+```bash
+seldon model unload iris
+```
+
+### Tensorflow Model
+
+We run a simple tensorflow model. Note the requirements section specifying `tensorflow`.
+
+```bash
+cat ./models/tfsimple1.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+Load the model.
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+```
+
+```json
+{}
+
+```
+
+Wait for the model to be ready.
+
+```bash
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+Get model metadata
+
+```bash
+seldon model metadata tfsimple1
+```
+
+```json
+{
+	"name": "tfsimple1_1",
+	"versions": [
+		"1"
+	],
+	"platform": "tensorflow_graphdef",
+	"inputs": [
+		{
+			"name": "INPUT0",
+			"datatype": "INT32",
+			"shape": [
+				-1,
+				16
+			]
+		},
+		{
+			"name": "INPUT1",
+			"datatype": "INT32",
+			"shape": [
+				-1,
+				16
+			]
+		}
+	],
+	"outputs": [
+		{
+			"name": "OUTPUT0",
+			"datatype": "INT32",
+			"shape": [
+				-1,
+				16
+			]
+		},
+		{
+			"name": "OUTPUT1",
+			"datatype": "INT32",
+			"shape": [
+				-1,
+				16
+			]
+		}
+	]
+}
+
+```
+
+Do a REST inference call.
+
+```bash
+seldon model infer tfsimple1 \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "tfsimple1_1",
+  "model_version": "1",
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        1,
+        16
+      ],
+      "data": [
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+        14,
+        16,
+        18,
+        20,
+        22,
+        24,
+        26,
+        28,
+        30,
+        32
+      ]
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        1,
+        16
+      ],
+      "data": [
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0
+      ]
+    }
+  ]
+}
+
+```
+
+Do a gRPC inference call
+
+```bash
+seldon model infer tfsimple1 --inference-mode grpc \
+    '{"model_name":"tfsimple1","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "modelName": "tfsimple1_1",
+  "modelVersion": "1",
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0,
+          0
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+Unload the model
+
+```bash
+seldon model unload tfsimple1
+```
+
+### Experiment
+
+We will use two SKlearn Iris classification models to illustrate an experiment.
+
+```bash
+cat ./models/sklearn1.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
+
+```
+
+```bash
+cat ./models/sklearn2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris2
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
+
+```
+
+Load both models.
+
+```bash
+seldon model load -f ./models/sklearn1.yaml
+seldon model load -f ./models/sklearn2.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+Wait for both models to be ready.
+
+```bash
+seldon model status iris | jq -M .
+seldon model status iris2 | jq -M .
+```
+
+```json
+{
+  "modelName": "iris",
+  "versions": [
+    {
+      "version": 1,
+      "serverName": "mlserver",
+      "kubernetesMeta": {},
+      "modelReplicaState": {
+        "0": {
+          "state": "Available",
+          "lastChangeTimestamp": "2023-06-29T14:01:41.362720538Z"
+        }
+      },
+      "state": {
+        "state": "ModelAvailable",
+        "availableReplicas": 1,
+        "lastChangeTimestamp": "2023-06-29T14:01:41.362720538Z"
+      },
+      "modelDefn": {
+        "meta": {
+          "name": "iris",
+          "kubernetesMeta": {}
+        },
+        "modelSpec": {
+          "uri": "gs://seldon-models/mlserver/iris",
+          "requirements": [
+            "sklearn"
+          ]
+        },
+        "deploymentSpec": {
+          "replicas": 1
+        }
+      }
+    }
+  ]
+}
+{
+  "modelName": "iris2",
+  "versions": [
+    {
+      "version": 1,
+      "serverName": "mlserver",
+      "kubernetesMeta": {},
+      "modelReplicaState": {
+        "0": {
+          "state": "Available",
+          "lastChangeTimestamp": "2023-06-29T14:01:41.362845079Z"
+        }
+      },
+      "state": {
+        "state": "ModelAvailable",
+        "availableReplicas": 1,
+        "lastChangeTimestamp": "2023-06-29T14:01:41.362845079Z"
+      },
+      "modelDefn": {
+        "meta": {
+          "name": "iris2",
+          "kubernetesMeta": {}
+        },
+        "modelSpec": {
+          "uri": "gs://seldon-models/mlserver/iris",
+          "requirements": [
+            "sklearn"
+          ]
+        },
+        "deploymentSpec": {
+          "replicas": 1
+        }
+      }
+    }
+  ]
+}
+
+```
+
+Create an experiment that modifies the iris model to add a second model splitting traffic 50/50 between the two.
+
+```bash
+cat ./experiments/ab-default-model.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: experiment-sample
+spec:
+  default: iris
+  candidates:
+  - name: iris
+    weight: 50
+  - name: iris2
+    weight: 50
+
+```
+
+Start the experiment.
+
+```bash
+seldon experiment start -f ./experiments/ab-default-model.yaml
+```
+
+Wait for the experiment to be ready.
+
+```bash
+seldon experiment status experiment-sample -w | jq -M .
+```
+
+```json
+{
+  "experimentName": "experiment-sample",
+  "active": true,
+  "candidatesReady": true,
+  "mirrorReady": true,
+  "statusDescription": "experiment active",
+  "kubernetesMeta": {}
+}
+
+```
+
+Run a set of calls and record which route the traffic took. There should be roughly a 50/50 split.
+
+```bash
+seldon model infer iris -i 100 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::57 :iris_1::43]
+
+```
+
+Run one more request
+
+```bash
+seldon model infer iris \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```json
+{
+	"model_name": "iris_1",
+	"model_version": "1",
+	"id": "fa425bdf-737c-41fe-894d-58868f70fe5d",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"parameters": {
+				"content_type": "np"
+			},
+			"data": [
+				2
+			]
+		}
+	]
+}
+
+```
+
+Use sticky session key passed by last infer request to ensure same route is taken each time.
+We will test REST and gRPC.
+
+```bash
+seldon model infer iris -s -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+```bash
+seldon model infer iris --inference-mode grpc -s -i 50\
+   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+Stop the experiment
+
+```bash
+seldon experiment stop experiment-sample
+```
+
+Show the requests all go to original model now.
+
+```bash
+seldon model infer iris -i 100 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::100]
+
+```
+
+Unload both models.
+
+```bash
+seldon model unload iris
+seldon model unload iris2
+```
diff --git a/docs/examples/local-experiments.md b/docs/examples/local-experiments.md
index a309977b4a..b8b6bfe8ad 100644
--- a/docs/examples/local-experiments.md
+++ b/docs/examples/local-experiments.md
@@ -1,8 +1,1072 @@
-# Local Experiments
+# Seldon V2 Non Kubernetes Local Experiment Examples
 
-Run these examples from the `samples` folder.
+### Model Experiment
 
+We will use two SKlearn Iris classification models to illustrate experiments.
 
-```{include} ../../../../samples/local-experiments.md
+```bash
+cat ./models/sklearn1.yaml
 ```
 
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
+
+```
+
+```bash
+cat ./models/sklearn2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris2
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
+
+```
+
+Load both models.
+
+```bash
+seldon model load -f ./models/sklearn1.yaml
+seldon model load -f ./models/sklearn2.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+Wait for both models to be ready.
+
+```bash
+seldon model status iris -w ModelAvailable
+seldon model status iris2 -w ModelAvailable
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model infer iris -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+```bash
+seldon model infer iris2 -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::50]
+
+```
+
+Create an experiment that modifies the iris model to add a second model splitting traffic 50/50 between the two.
+
+```bash
+cat ./experiments/ab-default-model.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: experiment-sample
+spec:
+  default: iris
+  candidates:
+  - name: iris
+    weight: 50
+  - name: iris2
+    weight: 50
+
+```
+
+Start the experiment.
+
+```bash
+seldon experiment start -f ./experiments/ab-default-model.yaml
+```
+
+Wait for the experiment to be ready.
+
+```bash
+seldon experiment status experiment-sample -w | jq -M .
+```
+
+```json
+{
+  "experimentName": "experiment-sample",
+  "active": true,
+  "candidatesReady": true,
+  "mirrorReady": true,
+  "statusDescription": "experiment active",
+  "kubernetesMeta": {}
+}
+
+```
+
+Run a set of calls and record which route the traffic took. There should be roughly a 50/50 split.
+
+```bash
+seldon model infer iris -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris2_1::27 :iris_1::23]
+
+```
+
+Show sticky session header `x-seldon-route` that is returned
+
+```bash
+seldon model infer iris --show-headers \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+> POST /v2/models/iris/infer HTTP/1.1
+> Host: localhost:9000
+> Content-Type:[application/json]
+> Seldon-Model:[iris]
+
+< X-Seldon-Route:[:iris_1:]
+< Ce-Id:[463e96ad-645f-4442-8890-4c340b58820b]
+< Traceparent:[00-fe9e87fcbe4be98ed82fb76166e15ceb-d35e7ac96bd8b718-01]
+< X-Envoy-Upstream-Service-Time:[3]
+< Ce-Specversion:[0.3]
+< Date:[Thu, 29 Jun 2023 14:03:03 GMT]
+< Ce-Source:[io.seldon.serving.deployment.mlserver]
+< Content-Type:[application/json]
+< Server:[envoy]
+< X-Request-Id:[cieou5ofh5ss73fbjdu0]
+< Ce-Endpoint:[iris_1]
+< Ce-Modelid:[iris_1]
+< Ce-Type:[io.seldon.serving.inference.response]
+< Content-Length:[213]
+< Ce-Inferenceservicename:[mlserver]
+< Ce-Requestid:[463e96ad-645f-4442-8890-4c340b58820b]
+
+{
+	"model_name": "iris_1",
+	"model_version": "1",
+	"id": "463e96ad-645f-4442-8890-4c340b58820b",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"parameters": {
+				"content_type": "np"
+			},
+			"data": [
+				2
+			]
+		}
+	]
+}
+
+```
+
+Use sticky session key passed by last infer request to ensure same route is taken each time.
+
+```bash
+seldon model infer iris -s -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+```bash
+seldon model infer iris --inference-mode grpc -s -i 50\
+   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+Stop the experiment
+
+```bash
+seldon experiment stop experiment-sample
+```
+
+Unload both models.
+
+```bash
+seldon model unload iris
+seldon model unload iris2
+```
+
+### Pipeline Experiment
+
+```bash
+cat ./models/add10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: add10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/add10"
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+cat ./models/mul10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mul10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mul10"
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+seldon model load -f ./models/add10.yaml
+seldon model load -f ./models/mul10.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status add10 -w ModelAvailable
+seldon model status mul10 -w ModelAvailable
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+cat ./pipelines/mul10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: pipeline-mul10
+spec:
+  steps:
+    - name: mul10
+  output:
+    steps:
+    - mul10
+
+```
+
+```bash
+cat ./pipelines/add10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: pipeline-add10
+spec:
+  steps:
+    - name: add10
+  output:
+    steps:
+    - add10
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/add10.yaml
+seldon pipeline load -f ./pipelines/mul10.yaml
+```
+
+```bash
+seldon pipeline status pipeline-add10 -w PipelineReady
+seldon pipeline status pipeline-mul10 -w PipelineReady
+```
+
+```json
+{"pipelineName":"pipeline-add10", "versions":[{"pipeline":{"name":"pipeline-add10", "uid":"cieov47l80lc739juklg", "version":1, "steps":[{"name":"add10"}], "output":{"steps":["add10.outputs"]}, "kubernetesMeta":{}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T14:05:04.460868091Z", "modelsReady":true}}]}
+{"pipelineName":"pipeline-mul10", "versions":[{"pipeline":{"name":"pipeline-mul10", "uid":"cieov47l80lc739jukm0", "version":1, "steps":[{"name":"mul10"}], "output":{"steps":["mul10.outputs"]}, "kubernetesMeta":{}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T14:05:04.631980330Z", "modelsReady":true}}]}
+
+```
+
+```bash
+seldon pipeline infer pipeline-add10 --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          11,
+          12,
+          13,
+          14
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer pipeline-mul10 --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          10,
+          20,
+          30,
+          40
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+cat ./experiments/addmul10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: addmul10
+spec:
+  default: pipeline-add10
+  resourceType: pipeline
+  candidates:
+  - name: pipeline-add10
+    weight: 50
+  - name: pipeline-mul10
+    weight: 50
+
+```
+
+```bash
+seldon experiment start -f ./experiments/addmul10.yaml
+```
+
+```bash
+seldon experiment status addmul10 -w | jq -M .
+```
+
+```json
+{
+  "experimentName": "addmul10",
+  "active": true,
+  "candidatesReady": true,
+  "mirrorReady": true,
+  "statusDescription": "experiment active",
+  "kubernetesMeta": {}
+}
+
+```
+
+```bash
+seldon pipeline infer pipeline-add10 -i 50 --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```
+Success: map[:add10_1::28 :mul10_1::22 :pipeline-add10.pipeline::28 :pipeline-mul10.pipeline::22]
+
+```
+
+Use sticky session key passed by last infer request to ensure same route is taken each time.
+
+```bash
+seldon pipeline infer pipeline-add10 --show-headers --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```
+> /inference.GRPCInferenceService/ModelInfer HTTP/2
+> Host: localhost:9000
+> seldon-model:[pipeline-add10.pipeline]
+
+< x-envoy-expected-rq-timeout-ms:[60000]
+< x-request-id:[cieov8ofh5ss739277i0]
+< date:[Thu, 29 Jun 2023 14:05:23 GMT]
+< server:[envoy]
+< content-type:[application/grpc]
+< x-envoy-upstream-service-time:[6]
+< x-seldon-route:[:add10_1: :pipeline-add10.pipeline:]
+< x-forwarded-proto:[http]
+
+{"outputs":[{"name":"OUTPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[11, 12, 13, 14]}}]}
+
+```
+
+```bash
+seldon pipeline infer pipeline-add10 -s --show-headers --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```
+> /inference.GRPCInferenceService/ModelInfer HTTP/2
+> Host: localhost:9000
+> x-seldon-route:[:add10_1: :pipeline-add10.pipeline:]
+> seldon-model:[pipeline-add10.pipeline]
+
+< content-type:[application/grpc]
+< x-forwarded-proto:[http]
+< x-envoy-expected-rq-timeout-ms:[60000]
+< x-seldon-route:[:add10_1: :pipeline-add10.pipeline: :pipeline-add10.pipeline:]
+< x-request-id:[cieov90fh5ss739277ig]
+< x-envoy-upstream-service-time:[7]
+< date:[Thu, 29 Jun 2023 14:05:24 GMT]
+< server:[envoy]
+
+{"outputs":[{"name":"OUTPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[11, 12, 13, 14]}}]}
+
+```
+
+```bash
+seldon pipeline infer pipeline-add10 -s -i 50 --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```
+Success: map[:add10_1::50 :pipeline-add10.pipeline::150]
+
+```
+
+```bash
+cat ./models/add20.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: add20
+spec:
+  storageUri: "gs://seldon-models/triton/add20"
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+seldon model load -f ./models/add20.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status add20 -w ModelAvailable
+```
+
+```json
+{}
+
+```
+
+```bash
+cat ./experiments/add1020.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: add1020
+spec:
+  default: add10
+  candidates:
+  - name: add10
+    weight: 50
+  - name: add20
+    weight: 50
+
+```
+
+```bash
+seldon experiment start -f ./experiments/add1020.yaml
+```
+
+```bash
+seldon experiment status add1020 -w | jq -M .
+```
+
+```json
+{
+  "experimentName": "add1020",
+  "active": true,
+  "candidatesReady": true,
+  "mirrorReady": true,
+  "statusDescription": "experiment active",
+  "kubernetesMeta": {}
+}
+
+```
+
+```bash
+seldon model infer add10 -i 50  --inference-mode grpc \
+  '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```
+Success: map[:add10_1::22 :add20_1::28]
+
+```
+
+```bash
+seldon pipeline infer pipeline-add10 -i 100 --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```
+Success: map[:add10_1::24 :add20_1::32 :mul10_1::44 :pipeline-add10.pipeline::56 :pipeline-mul10.pipeline::44]
+
+```
+
+```bash
+seldon pipeline infer pipeline-add10 --show-headers --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```
+> /inference.GRPCInferenceService/ModelInfer HTTP/2
+> Host: localhost:9000
+> seldon-model:[pipeline-add10.pipeline]
+
+< x-request-id:[cieovf0fh5ss739279u0]
+< x-envoy-upstream-service-time:[5]
+< x-seldon-route:[:add10_1: :pipeline-add10.pipeline:]
+< date:[Thu, 29 Jun 2023 14:05:48 GMT]
+< server:[envoy]
+< content-type:[application/grpc]
+< x-forwarded-proto:[http]
+< x-envoy-expected-rq-timeout-ms:[60000]
+
+{"outputs":[{"name":"OUTPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[11, 12, 13, 14]}}]}
+
+```
+
+```bash
+seldon pipeline infer pipeline-add10 -s --show-headers --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```
+> /inference.GRPCInferenceService/ModelInfer HTTP/2
+> Host: localhost:9000
+> x-seldon-route:[:add10_1: :pipeline-add10.pipeline:]
+> seldon-model:[pipeline-add10.pipeline]
+
+< x-forwarded-proto:[http]
+< x-envoy-expected-rq-timeout-ms:[60000]
+< x-request-id:[cieovf8fh5ss739279ug]
+< x-envoy-upstream-service-time:[6]
+< date:[Thu, 29 Jun 2023 14:05:49 GMT]
+< server:[envoy]
+< content-type:[application/grpc]
+< x-seldon-route:[:add10_1: :pipeline-add10.pipeline: :add20_1: :pipeline-add10.pipeline:]
+
+{"outputs":[{"name":"OUTPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[21, 22, 23, 24]}}]}
+
+```
+
+```bash
+seldon experiment stop addmul10
+seldon experiment stop add1020
+seldon pipeline unload pipeline-add10
+seldon pipeline unload pipeline-mul10
+seldon model unload add10
+seldon model unload add20
+seldon model unload mul10
+```
+
+### Model Mirror Experiment
+
+We will use two SKlearn Iris classification models to illustrate a model with a mirror.
+
+```bash
+cat ./models/sklearn1.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
+
+```
+
+```bash
+cat ./models/sklearn2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris2
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  requirements:
+  - sklearn
+
+```
+
+Load both models.
+
+```bash
+seldon model load -f ./models/sklearn1.yaml
+seldon model load -f ./models/sklearn2.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+Wait for both models to be ready.
+
+```bash
+seldon model status iris -w ModelAvailable
+seldon model status iris2 -w ModelAvailable
+```
+
+```json
+{}
+{}
+
+```
+
+Create an experiment that modifies in which we mirror traffic to iris also to iris2.
+
+```bash
+cat ./experiments/sklearn-mirror.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: sklearn-mirror
+spec:
+  default: iris
+  candidates:
+  - name: iris
+    weight: 100
+  mirror:
+    name: iris2
+    percent: 100
+
+```
+
+Start the experiment.
+
+```bash
+seldon experiment start -f ./experiments/sklearn-mirror.yaml
+```
+
+Wait for the experiment to be ready.
+
+```bash
+seldon experiment status sklearn-mirror -w | jq -M .
+```
+
+```json
+{
+  "experimentName": "sklearn-mirror",
+  "active": true,
+  "candidatesReady": true,
+  "mirrorReady": true,
+  "statusDescription": "experiment active",
+  "kubernetesMeta": {}
+}
+
+```
+
+We get responses from iris but all requests would also have been mirrored to iris2
+
+```bash
+seldon model infer iris -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+We can check the local prometheus port from the agent to validate requests went to iris2
+
+```bash
+curl -s 0.0.0:9006/metrics | grep seldon_model_infer_total | grep iris2_1
+```
+
+```
+seldon_model_infer_total{code="200",method_type="rest",model="iris",model_internal="iris2_1",server="mlserver",server_replica="0"} 50
+
+```
+
+Stop the experiment
+
+```bash
+seldon experiment stop sklearn-mirror
+```
+
+Unload both models.
+
+```bash
+seldon model unload iris
+seldon model unload iris2
+```
+
+## Pipeline Mirror Experiment
+
+```bash
+cat ./models/add10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: add10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/add10"
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+cat ./models/mul10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mul10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mul10"
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+seldon model load -f ./models/add10.yaml
+seldon model load -f ./models/mul10.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status add10 -w ModelAvailable
+seldon model status mul10 -w ModelAvailable
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+cat ./pipelines/mul10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: pipeline-mul10
+spec:
+  steps:
+    - name: mul10
+  output:
+    steps:
+    - mul10
+
+```
+
+```bash
+cat ./pipelines/add10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: pipeline-add10
+spec:
+  steps:
+    - name: add10
+  output:
+    steps:
+    - add10
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/add10.yaml
+seldon pipeline load -f ./pipelines/mul10.yaml
+```
+
+```bash
+seldon pipeline status pipeline-add10 -w PipelineReady
+seldon pipeline status pipeline-mul10 -w PipelineReady
+```
+
+```json
+{"pipelineName":"pipeline-add10", "versions":[{"pipeline":{"name":"pipeline-add10", "uid":"ciep072i8ufs73flaipg", "version":1, "steps":[{"name":"add10"}], "output":{"steps":["add10.outputs"]}, "kubernetesMeta":{}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T14:07:24.903503109Z", "modelsReady":true}}]}
+{"pipelineName":"pipeline-mul10", "versions":[{"pipeline":{"name":"pipeline-mul10", "uid":"ciep072i8ufs73flaiq0", "version":1, "steps":[{"name":"mul10"}], "output":{"steps":["mul10.outputs"]}, "kubernetesMeta":{}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T14:07:25.082642153Z", "modelsReady":true}}]}
+
+```
+
+```bash
+seldon pipeline infer pipeline-add10 --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```json
+{"outputs":[{"name":"OUTPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[11, 12, 13, 14]}}]}
+
+```
+
+```bash
+seldon pipeline infer pipeline-mul10 --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```json
+{"outputs":[{"name":"OUTPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[10, 20, 30, 40]}}]}
+
+```
+
+```bash
+cat ./experiments/addmul10-mirror.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: addmul10-mirror
+spec:
+  default: pipeline-add10
+  resourceType: pipeline
+  candidates:
+  - name: pipeline-add10
+    weight: 100
+  mirror:
+    name: pipeline-mul10
+    percent: 100
+
+```
+
+```bash
+seldon experiment start -f ./experiments/addmul10-mirror.yaml
+```
+
+```bash
+seldon experiment status addmul10-mirror -w | jq -M .
+```
+
+```json
+{
+  "experimentName": "addmul10-mirror",
+  "active": true,
+  "candidatesReady": true,
+  "mirrorReady": true,
+  "statusDescription": "experiment active",
+  "kubernetesMeta": {}
+}
+
+```
+
+```bash
+seldon pipeline infer pipeline-add10 -i 1 --inference-mode grpc \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}'
+```
+
+```json
+{"outputs":[{"name":"OUTPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[11, 12, 13, 14]}}]}
+
+```
+
+Let's check that the mul10 model was called.
+
+```bash
+curl -s 0.0.0:9007/metrics | grep seldon_model_infer_total | grep mul10_1
+```
+
+```
+seldon_model_infer_total{code="OK",method_type="grpc",model="mul10",model_internal="mul10_1",server="triton",server_replica="0"} 2
+
+```
+
+```bash
+curl -s 0.0.0:9007/metrics | grep seldon_model_infer_total | grep add10_1
+```
+
+```
+seldon_model_infer_total{code="OK",method_type="grpc",model="add10",model_internal="add10_1",server="triton",server_replica="0"} 2
+
+```
+
+Let's do an http call and check agaib the two models
+
+```bash
+seldon pipeline infer pipeline-add10 -i 1 \
+ '{"model_name":"add10","inputs":[{"name":"INPUT","data":[1,2,3,4],"datatype":"FP32","shape":[4]}]}'
+```
+
+```json
+{
+	"model_name": "",
+	"outputs": [
+		{
+			"data": [
+				11,
+				12,
+				13,
+				14
+			],
+			"name": "OUTPUT",
+			"shape": [
+				4
+			],
+			"datatype": "FP32"
+		}
+	]
+}
+
+```
+
+```bash
+curl -s 0.0.0:9007/metrics | grep seldon_model_infer_total | grep mul10_1
+```
+
+```
+seldon_model_infer_total{code="OK",method_type="grpc",model="mul10",model_internal="mul10_1",server="triton",server_replica="0"} 3
+
+```
+
+```bash
+curl -s 0.0.0:9007/metrics | grep seldon_model_infer_total | grep add10_1
+```
+
+```
+seldon_model_infer_total{code="OK",method_type="grpc",model="add10",model_internal="add10_1",server="triton",server_replica="0"} 3
+
+```
+
+```bash
+seldon pipeline inspect pipeline-mul10
+```
+
+```
+seldon.default.model.mul10.inputs	ciep0bofh5ss73dpdiq0	{"inputs":[{"name":"INPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[1, 2, 3, 4]}}]}
+seldon.default.model.mul10.outputs	ciep0bofh5ss73dpdiq0	{"modelName":"mul10_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[10, 20, 30, 40]}}]}
+seldon.default.pipeline.pipeline-mul10.inputs	ciep0bofh5ss73dpdiq0	{"inputs":[{"name":"INPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[1, 2, 3, 4]}}]}
+seldon.default.pipeline.pipeline-mul10.outputs	ciep0bofh5ss73dpdiq0	{"outputs":[{"name":"OUTPUT", "datatype":"FP32", "shape":["4"], "contents":{"fp32Contents":[10, 20, 30, 40]}}]}
+
+```
+
+```bash
+seldon experiment stop addmul10-mirror
+seldon pipeline unload pipeline-add10
+seldon pipeline unload pipeline-mul10
+seldon model unload add10
+seldon model unload mul10
+```
+
+```python
+
+```
diff --git a/docs/examples/local-overcommit-examples.md b/docs/examples/local-overcommit-examples.md
index 0d62550011..467d32d4a5 100644
--- a/docs/examples/local-overcommit-examples.md
+++ b/docs/examples/local-overcommit-examples.md
@@ -1,8 +1,1674 @@
 # Local Multi-model Serving with Overcommit Examples
 
-Run these examples from the `samples` folder.
 
+Note: this notebook requires access to internal services, so either `make start-all-host`
+(under `scheduler` sub directory) or expose the relevant ports.
+
+## `iris` model on `MLServer`
+
+```python
+%env INFER_ENDPOINT=0.0.0.0:9000
+%env SCHEDULER_ENDPOINT=0.0.0.0:9004
+%env MLSERVER_DEBUG=0.0.0.0:7777
+%env TRITON_DEBUG=0.0.0.0:7778
+```
+
+```
+env: INFER_ENDPOINT=0.0.0.0:9000
+env: SCHEDULER_ENDPOINT=0.0.0.0:9004
+env: MLSERVER_DEBUG=0.0.0.0:7777
+env: TRITON_DEBUG=0.0.0.0:7778
+
+```
+
+By default if running locally there is 1 replica of `mlserver` with memory slots up to 10MB
+and 20% overcommit budget. We will load 11 `iris` models each requiring 1MB worth of memory
+slots as an example. These numbers allow for 10 models to be active at the same time and 1
+model to be evicted to disk.
+
+```bash
+%%bash
+for i in {1..11};
+do
+
+echo "loading model iris$i"
+
+data='{
+        "model":{
+            "meta": {"name":"iris'"$i"'"},
+            "modelSpec" : {
+                "uri":"gs://seldon-models/mlserver/iris",
+                "requirements":["sklearn"],
+                "memoryBytes":1000000},
+            "deploymentSpec": {"replicas":1}
+            }
+      }'
+
+grpcurl -d "$data" \
+-plaintext \
+-import-path ../apis \
+-proto ../apis/mlops/scheduler/scheduler.proto "$SCHEDULER_ENDPOINT" seldon.mlops.scheduler.Scheduler/LoadModel
+
+sleep 0.05
+done
+
+```
+
+```
+loading model iris1
+{
+
+}
+loading model iris2
+{
+
+}
+loading model iris3
+{
+
+}
+loading model iris4
+{
+
+}
+loading model iris5
+{
+
+}
+loading model iris6
+{
+
+}
+loading model iris7
+{
+
+}
+loading model iris8
+{
+
+}
+loading model iris9
+{
+
+}
+loading model iris10
+{
+
+}
+loading model iris11
+{
+
+}
+
+```
+
+Get the list of models on this mlserver replica and whether they are loaded in main memory
+
+```bash
+%%bash
+grpcurl -d '{}' \
+         -plaintext \
+         -import-path ../apis/ \
+         -proto ../apis/mlops/agent_debug/agent_debug.proto  ${MLSERVER_DEBUG} seldon.mlops.agent_debug.AgentDebugService/ReplicaStatus
+
+```
+
+```json
+{
+  "models": [
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:45:40Z",
+      "name": "iris10_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:44:48Z",
+      "name": "iris3_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:44:48Z",
+      "name": "iris6_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:44:48Z",
+      "name": "iris1_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "iris11_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:44:48Z",
+      "name": "iris9_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:44:48Z",
+      "name": "iris7_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:44:48Z",
+      "name": "iris8_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:44:48Z",
+      "name": "iris5_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:44:48Z",
+      "name": "iris4_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:44:48Z",
+      "name": "iris2_1"
+    }
+  ]
+}
+
+```
+
+```bash
+%%bash
+for i in {1..11};
+do
+
+url=http://${INFER_ENDPOINT}/v2/models/iris${i}/infer
+ret=`curl -s -o /dev/null -w "%{http_code}" "${url}" -H "Content-Type: application/json" \
+        -d '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'`
+if [ $ret -ne 200 ]; then
+    echo "Failed with code ${ret}"
+    exit
+fi
+
+done
+echo "All succeeded"
+
+```
+
+```
+All succeeded
+
+```
+
+Doing inference for all models succeeds as swapping in and out models is handled automatically
+
+```bash
+%%bash
+for i in {1..10};
+do
+
+for j in {1..11};
+do
+curl -s -o /dev/null http://"$INFER_ENDPOINT"/v2/models/iris$j/infer -H "Content-Type: application/json"  \
+        -d '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}' &
+done
+
+done
+
+```
+
+Unload models
+
+```bash
+%%bash
+
+for i in {1..11};
+do
+
+grpcurl -d '{"model": {"name" : "iris'"$i"'"}}' \
+         -plaintext \
+         -import-path ../apis/ \
+         -proto ../apis/mlops/scheduler/scheduler.proto "$SCHEDULER_ENDPOINT" seldon.mlops.scheduler.Scheduler/UnloadModel
+
+done
+
+```
+
+```json
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+
+```
+
+## `tfsimple` model on `triton`
+
+With `tfsimple` on `triton` we will reduce the memory slot required to 100KB, which will allow us to load at least 100 models on the server in memory. The remaining models (10) will have to be evicted.
+
+```bash
+%%bash
+for i in {1..110};
+do
+
+echo "loading model tfsimple$i"
+
+data='{
+        "model":{
+            "meta": {"name":"tfsimple'"$i"'"},
+            "modelSpec" : {
+                "uri":"gs://seldon-models/triton/simple",
+                "requirements":["tensorflow"],
+                "memoryBytes":100000},
+            "deploymentSpec": {"replicas":1}
+            }
+      }'
+
+grpcurl -d "$data" \
+-plaintext \
+-import-path ../apis \
+-proto ../apis/mlops/scheduler/scheduler.proto "$SCHEDULER_ENDPOINT" seldon.mlops.scheduler.Scheduler/LoadModel
+
+sleep 0.05
+done
+
+```
+
+```
+loading model tfsimple1
+{
+
+}
+loading model tfsimple2
+{
+
+}
+loading model tfsimple3
+{
+
+}
+loading model tfsimple4
+{
+
+}
+loading model tfsimple5
+{
+
+}
+loading model tfsimple6
+{
+
+}
+loading model tfsimple7
+{
+
+}
+loading model tfsimple8
+{
+
+}
+loading model tfsimple9
+{
+
+}
+loading model tfsimple10
+{
+
+}
+loading model tfsimple11
+{
+
+}
+loading model tfsimple12
+{
+
+}
+loading model tfsimple13
+{
+
+}
+loading model tfsimple14
+{
+
+}
+loading model tfsimple15
+{
+
+}
+loading model tfsimple16
+{
+
+}
+loading model tfsimple17
+{
+
+}
+loading model tfsimple18
+{
+
+}
+loading model tfsimple19
+{
+
+}
+loading model tfsimple20
+{
+
+}
+loading model tfsimple21
+{
+
+}
+loading model tfsimple22
+{
+
+}
+loading model tfsimple23
+{
+
+}
+loading model tfsimple24
+{
+
+}
+loading model tfsimple25
+{
+
+}
+loading model tfsimple26
+{
+
+}
+loading model tfsimple27
+{
+
+}
+loading model tfsimple28
+{
+
+}
+loading model tfsimple29
+{
+
+}
+loading model tfsimple30
+{
+
+}
+loading model tfsimple31
+{
+
+}
+loading model tfsimple32
+{
+
+}
+loading model tfsimple33
+{
+
+}
+loading model tfsimple34
+{
+
+}
+loading model tfsimple35
+{
+
+}
+loading model tfsimple36
+{
+
+}
+loading model tfsimple37
+{
+
+}
+loading model tfsimple38
+{
+
+}
+loading model tfsimple39
+{
+
+}
+loading model tfsimple40
+{
+
+}
+loading model tfsimple41
+{
+
+}
+loading model tfsimple42
+{
+
+}
+loading model tfsimple43
+{
+
+}
+loading model tfsimple44
+{
+
+}
+loading model tfsimple45
+{
+
+}
+loading model tfsimple46
+{
+
+}
+loading model tfsimple47
+{
+
+}
+loading model tfsimple48
+{
+
+}
+loading model tfsimple49
+{
+
+}
+loading model tfsimple50
+{
+
+}
+loading model tfsimple51
+{
+
+}
+loading model tfsimple52
+{
+
+}
+loading model tfsimple53
+{
+
+}
+loading model tfsimple54
+{
+
+}
+loading model tfsimple55
+{
+
+}
+loading model tfsimple56
+{
+
+}
+loading model tfsimple57
+{
+
+}
+loading model tfsimple58
+{
+
+}
+loading model tfsimple59
+{
+
+}
+loading model tfsimple60
+{
+
+}
+loading model tfsimple61
+{
+
+}
+loading model tfsimple62
+{
+
+}
+loading model tfsimple63
+{
+
+}
+loading model tfsimple64
+{
+
+}
+loading model tfsimple65
+{
+
+}
+loading model tfsimple66
+{
+
+}
+loading model tfsimple67
+{
+
+}
+loading model tfsimple68
+{
+
+}
+loading model tfsimple69
+{
+
+}
+loading model tfsimple70
+{
+
+}
+loading model tfsimple71
+{
+
+}
+loading model tfsimple72
+{
+
+}
+loading model tfsimple73
+{
+
+}
+loading model tfsimple74
+{
+
+}
+loading model tfsimple75
+{
+
+}
+loading model tfsimple76
+{
+
+}
+loading model tfsimple77
+{
+
+}
+loading model tfsimple78
+{
+
+}
+loading model tfsimple79
+{
+
+}
+loading model tfsimple80
+{
+
+}
+loading model tfsimple81
+{
+
+}
+loading model tfsimple82
+{
+
+}
+loading model tfsimple83
+{
+
+}
+loading model tfsimple84
+{
+
+}
+loading model tfsimple85
+{
+
+}
+loading model tfsimple86
+{
+
+}
+loading model tfsimple87
+{
+
+}
+loading model tfsimple88
+{
+
+}
+loading model tfsimple89
+{
+
+}
+loading model tfsimple90
+{
+
+}
+loading model tfsimple91
+{
+
+}
+loading model tfsimple92
+{
+
+}
+loading model tfsimple93
+{
+
+}
+loading model tfsimple94
+{
+
+}
+loading model tfsimple95
+{
+
+}
+loading model tfsimple96
+{
+
+}
+loading model tfsimple97
+{
+
+}
+loading model tfsimple98
+{
+
+}
+loading model tfsimple99
+{
+
+}
+loading model tfsimple100
+{
+
+}
+loading model tfsimple101
+{
+
+}
+loading model tfsimple102
+{
+
+}
+loading model tfsimple103
+{
+
+}
+loading model tfsimple104
+{
+
+}
+loading model tfsimple105
+{
+
+}
+loading model tfsimple106
+{
+
+}
+loading model tfsimple107
+{
+
+}
+loading model tfsimple108
+{
+
+}
+loading model tfsimple109
+{
+
+}
+loading model tfsimple110
+{
+
+}
+
+```
+
+```bash
+%%bash
+for i in {1..110};
+do
+
+url=http://${INFER_ENDPOINT}/v2/models/tfsimple${i}/infer
+ret=`curl -s -o /dev/null -w "%{http_code}" curl -s -o /dev/null -w "%{http_code}" "${url}" -H "Content-Type: application/json" \
+        -d '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'`
+if [ $ret -ne 200 ]; then
+    echo "Failed with code ${ret}"
+    exit
+fi
+done
+echo "All succeeded"
+
+```
+
+```
+All succeeded
+
+```
+
+```bash
+%%bash
+grpcurl -d '{}' \
+         -plaintext \
+         -import-path ../apis/ \
+         -proto ../apis/mlops/agent_debug/agent_debug.proto  ${TRITON_DEBUG} seldon.mlops.agent_debug.AgentDebugService/ReplicaStatus
+
+```
+
+```json
+{
+  "models": [
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple89_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple34_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple59_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple76_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple45_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple60_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "tfsimple8_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple19_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple31_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple85_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple107_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple27_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple51_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple91_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "tfsimple2_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple24_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple53_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple98_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple17_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple35_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple38_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple12_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple83_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple99_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple20_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple58_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple101_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple37_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple63_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple87_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple40_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple72_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple94_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "tfsimple4_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple80_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple95_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple26_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple48_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple55_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple75_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple100_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple41_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple43_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple56_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple73_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple74_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple21_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple42_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple82_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple86_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple97_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple106_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple108_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "tfsimple10_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple15_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple67_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple105_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "tfsimple7_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple22_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple61_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple25_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple54_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple64_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "tfsimple6_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple90_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple96_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple16_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple62_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple71_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple28_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple103_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "tfsimple5_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple93_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple14_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple69_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple84_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple36_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple104_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple70_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple110_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "tfsimple9_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple57_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple65_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple109_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple30_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple66_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple68_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple39_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple44_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple88_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple13_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple23_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple29_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple50_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple79_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "tfsimple3_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple18_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:29Z",
+      "name": "tfsimple102_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple92_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple49_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple77_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple78_1"
+    },
+    {
+      "lastAccessed": "0001-01-01T00:00:00Z",
+      "name": "tfsimple1_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple47_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:28Z",
+      "name": "tfsimple81_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple11_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple33_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple46_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:27Z",
+      "name": "tfsimple52_1"
+    },
+    {
+      "state": "InMemory",
+      "lastAccessed": "2023-01-20T18:48:26Z",
+      "name": "tfsimple32_1"
+    }
+  ]
+}
 
-```{include} ../../../../samples/local-over-commit-test.md
 ```
 
+```bash
+%%bash
+
+for i in {1..110};
+do
+
+grpcurl -d '{"model": {"name" : "tfsimple'"$i"'"}}' \
+         -plaintext \
+         -import-path ../apis/ \
+         -proto ../apis/mlops/scheduler/scheduler.proto "$SCHEDULER_ENDPOINT" seldon.mlops.scheduler.Scheduler/UnloadModel
+
+done
+
+```
+
+```json
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+{
+
+}
+
+```
diff --git a/docs/examples/model-zoo.md b/docs/examples/model-zoo.md
index b0240d16ff..b57b8f4a3d 100644
--- a/docs/examples/model-zoo.md
+++ b/docs/examples/model-zoo.md
@@ -1,8 +1,707 @@
-# Model Zoo
+# Seldon Model Zoo
 
-Run these examples from the `samples` folder.
+Examples of various model artifact types from various frameworks running under Seldon Core V2.
 
+* SKlearn
+* Tensorflow
+* XGBoost
+* ONNX
+* Lightgbm
+* MLFlow
+* PyTorch
+
+Python requirements in `model-zoo-requirements.txt`
+
+### SKLearn Iris Classification Model
+
+The training code for this model can be found at `scripts/models/iris` in SCv2 repo.
+
+```bash
+cat ./models/sklearn-iris-gs.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
+
+```
+
+```bash
+seldon model load -f ./models/sklearn-iris-gs.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status iris -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model infer iris \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```json
+{
+	"model_name": "iris_1",
+	"model_version": "1",
+	"id": "09263298-ca66-49c5-acb9-0ca75b06f825",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "INT64",
+			"data": [
+				2
+			]
+		}
+	]
+}
+
+```
+
+```bash
+seldon model unload iris
+```
+
+```json
+{}
+
+```
+
+### Tensorflow CIFAR10 Image Classification Model
+
+```python
+import requests
+import json
+from typing import Dict, List
+import numpy as np
+import os
+import tensorflow as tf
+from alibi_detect.utils.perturbation import apply_mask
+from alibi_detect.datasets import fetch_cifar10c
+import matplotlib.pyplot as plt
+tf.keras.backend.clear_session()
+```
+
+```
+2023-03-09 19:43:43.637892: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2023-03-09 19:43:43.637906: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+
+```
+
+```python
+train, test = tf.keras.datasets.cifar10.load_data()
+X_train, y_train = train
+X_test, y_test = test
+
+X_train = X_train.astype('float32') / 255
+X_test = X_test.astype('float32') / 255
+print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
+classes = (
+    "plane",
+    "car",
+    "bird",
+    "cat",
+    "deer",
+    "dog",
+    "frog",
+    "horse",
+    "ship",
+    "truck",
+)
+
+```
+
+```
+(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)
+
+```
+
+```python
+reqJson = json.loads('{"inputs":[{"name":"input_1","data":[],"datatype":"FP32","shape":[]}]}')
+url = "http://0.0.0.0:9000/v2/models/model/infer"
+
+def infer(resourceName: str, idx: int):
+    rows = X_train[idx:idx+1]
+    show(rows[0])
+    reqJson["inputs"][0]["data"] = rows.flatten().tolist()
+    reqJson["inputs"][0]["shape"] = [1, 32, 32, 3]
+    headers = {"Content-Type": "application/json", "seldon-model":resourceName}
+    response_raw = requests.post(url, json=reqJson, headers=headers)
+    probs = np.array(response_raw.json()["outputs"][0]["data"])
+    print(classes[probs.argmax(axis=0)])
+
+
+def show(X):
+    plt.imshow(X.reshape(32, 32, 3))
+    plt.axis("off")
+    plt.show()
+
+```
+
+```bash
+cat ./models/cifar10-no-config.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: cifar10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/tensorflow/cifar10"
+  requirements:
+  - tensorflow
+
+```
+
+```bash
+seldon model load -f ./models/cifar10-no-config.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status cifar10 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+```python
+infer("cifar10",4)
+```
+
+```
+![png](model-zoo_files/model-zoo_14_0.png)
+
+```
+
+```
+car
+
+```
+
+```bash
+seldon model unload cifar10
+```
+
+```json
+{}
+
+```
+
+### XGBoost Model
+
+The training code for this model can be found at `./scripts/models/income-xgb`
+
+```bash
+cat ./models/income-xgb.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-xgb
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/income-xgb"
+  requirements:
+  - xgboost
+
+```
+
+```bash
+seldon model load -f ./models/income-xgb.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status income-xgb -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model infer income-xgb \
+  '{ "parameters": {"content_type": "pd"}, "inputs": [{"name": "Age", "shape": [1, 1], "datatype": "INT64", "data": [47]},{"name": "Workclass", "shape": [1, 1], "datatype": "INT64", "data": [4]},{"name": "Education", "shape": [1, 1], "datatype": "INT64", "data": [1]},{"name": "Marital Status", "shape": [1, 1], "datatype": "INT64", "data": [1]},{"name": "Occupation", "shape": [1, 1], "datatype": "INT64", "data": [1]},{"name": "Relationship", "shape": [1, 1], "datatype": "INT64", "data": [3]},{"name": "Race", "shape": [1, 1], "datatype": "INT64", "data": [4]},{"name": "Sex", "shape": [1, 1], "datatype": "INT64", "data": [1]},{"name": "Capital Gain", "shape": [1, 1], "datatype": "INT64", "data": [0]},{"name": "Capital Loss", "shape": [1, 1], "datatype": "INT64", "data": [0]},{"name": "Hours per week", "shape": [1, 1], "datatype": "INT64", "data": [40]},{"name": "Country", "shape": [1, 1], "datatype": "INT64", "data": [9]}]}'
+```
+
+```json
+{
+	"model_name": "income-xgb_1",
+	"model_version": "1",
+	"id": "e30c3b44-fa14-4e5f-88f5-d6f4d287da20",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "FP32",
+			"data": [
+				-1.8380107879638672
+			]
+		}
+	]
+}
+
+```
+
+```bash
+seldon model unload income-xgb
+```
+
+```json
+{}
+
+```
+
+## ONNX MNIST Model
+
+This model is a pretrained model as defined in `./scripts/models/Makefile` target `mnist-onnx`
+
+```python
+import matplotlib.pyplot as plt
+import json
+import requests
+from torchvision.datasets import MNIST
+from torchvision.transforms import ToTensor
+from torchvision import transforms
+from torch.utils.data import DataLoader
+import numpy as np
+training_data = MNIST(
+    root=".",
+    download=True,
+    train=False,
+    transform = transforms.Compose([
+              transforms.ToTensor()
+          ])
+)
+
+```
+
+```python
+reqJson = json.loads('{"inputs":[{"name":"Input3","data":[],"datatype":"FP32","shape":[]}]}')
+url = "http://0.0.0.0:9000/v2/models/model/infer"
+dl = DataLoader(training_data, batch_size=1, shuffle=False)
+dlIter = iter(dl)
+
+def infer_mnist():
+    x, y = next(dlIter)
+    data = x.cpu().numpy()
+    reqJson["inputs"][0]["data"] = data.flatten().tolist()
+    reqJson["inputs"][0]["shape"] = [1, 1, 28, 28]
+    headers = {"Content-Type": "application/json", "seldon-model":"mnist-onnx"}
+    response_raw = requests.post(url, json=reqJson, headers=headers)
+    show_mnist(x)
+    probs = np.array(response_raw.json()["outputs"][0]["data"])
+    print(probs.argmax(axis=0))
+
+
+def show_mnist(X):
+    plt.imshow(X.reshape(28, 28))
+    plt.axis("off")
+    plt.show()
+```
+
+```bash
+cat ./models/mnist-onnx.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mnist-onnx
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mnist-onnx"
+  requirements:
+  - onnx
 
-```{include} ../../../../samples/model-zoo.md
 ```
 
+```bash
+seldon model load -f ./models/mnist-onnx.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status mnist-onnx -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+```python
+infer_mnist()
+```
+
+```
+![png](model-zoo_files/model-zoo_28_0.png)
+
+```
+
+```
+7
+
+```
+
+```bash
+seldon model unload mnist-onnx
+```
+
+```json
+{}
+
+```
+
+### LightGBM Model
+
+The training code for this model can be found at `./scripts/models/income-lgb`
+
+```bash
+cat ./models/income-lgb.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: income-lgb
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/income-lgb"
+  requirements:
+  - lightgbm
+
+```
+
+```bash
+seldon model load -f ./models/income-lgb.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status income-lgb -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model infer income-lgb \
+  '{ "parameters": {"content_type": "pd"}, "inputs": [{"name": "Age", "shape": [1, 1], "datatype": "INT64", "data": [47]},{"name": "Workclass", "shape": [1, 1], "datatype": "INT64", "data": [4]},{"name": "Education", "shape": [1, 1], "datatype": "INT64", "data": [1]},{"name": "Marital Status", "shape": [1, 1], "datatype": "INT64", "data": [1]},{"name": "Occupation", "shape": [1, 1], "datatype": "INT64", "data": [1]},{"name": "Relationship", "shape": [1, 1], "datatype": "INT64", "data": [3]},{"name": "Race", "shape": [1, 1], "datatype": "INT64", "data": [4]},{"name": "Sex", "shape": [1, 1], "datatype": "INT64", "data": [1]},{"name": "Capital Gain", "shape": [1, 1], "datatype": "INT64", "data": [0]},{"name": "Capital Loss", "shape": [1, 1], "datatype": "INT64", "data": [0]},{"name": "Hours per week", "shape": [1, 1], "datatype": "INT64", "data": [40]},{"name": "Country", "shape": [1, 1], "datatype": "INT64", "data": [9]}]}'
+```
+
+```json
+{
+	"model_name": "income-lgb_1",
+	"model_version": "1",
+	"id": "4437a71e-9af1-4e3b-aa4b-cb95d2cd86b9",
+	"parameters": {},
+	"outputs": [
+		{
+			"name": "predict",
+			"shape": [
+				1,
+				1
+			],
+			"datatype": "FP64",
+			"data": [
+				0.06279460120044741
+			]
+		}
+	]
+}
+
+```
+
+```bash
+seldon model unload income-lgb
+```
+
+```json
+{}
+
+```
+
+### MLFlow Wine Model
+
+The training code for this model can be found at `./scripts/models/wine-mlflow`
+
+```bash
+cat ./models/wine-mlflow.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: wine
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/wine-mlflow"
+  requirements:
+  - mlflow
+
+```
+
+```bash
+seldon model load -f ./models/wine-mlflow.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status wine -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+```python
+import requests
+url = "http://0.0.0.0:9000/v2/models/model/infer"
+inference_request = {
+    "inputs": [
+        {
+          "name": "fixed acidity",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [7.4],
+        },
+        {
+          "name": "volatile acidity",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [0.7000],
+        },
+        {
+          "name": "citric acid",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [0],
+        },
+        {
+          "name": "residual sugar",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [1.9],
+        },
+        {
+          "name": "chlorides",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [0.076],
+        },
+        {
+          "name": "free sulfur dioxide",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [11],
+        },
+        {
+          "name": "total sulfur dioxide",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [34],
+        },
+        {
+          "name": "density",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [0.9978],
+        },
+        {
+          "name": "pH",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [3.51],
+        },
+        {
+          "name": "sulphates",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [0.56],
+        },
+        {
+          "name": "alcohol",
+          "shape": [1],
+          "datatype": "FP32",
+          "data": [9.4],
+        },
+    ]
+}
+headers = {"Content-Type": "application/json", "seldon-model":"wine"}
+response_raw = requests.post(url, json=inference_request, headers=headers)
+print(response_raw.json())
+```
+
+```json
+{'model_name': 'wine_1', 'model_version': '1', 'id': '0d7e44f8-b46c-4438-b8af-a749e6aa6039', 'parameters': {}, 'outputs': [{'name': 'output-1', 'shape': [1, 1], 'datatype': 'FP64', 'data': [5.576883936610762]}]}
+
+```
+
+```bash
+seldon model unload wine
+```
+
+```json
+{}
+
+```
+
+## Pytorch MNIST Model
+
+This example model is downloaded and trained in `./scripts/models/Makefile` target `mnist-pytorch`
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+import json
+import requests
+from torchvision.datasets import MNIST
+from torchvision.transforms import ToTensor
+from torchvision import transforms
+from torch.utils.data import DataLoader
+training_data = MNIST(
+    root=".",
+    download=True,
+    train=False,
+    transform = transforms.Compose([
+              transforms.ToTensor()
+          ])
+)
+
+```
+
+```python
+reqJson = json.loads('{"inputs":[{"name":"x__0","data":[],"datatype":"FP32","shape":[]}]}')
+url = "http://0.0.0.0:9000/v2/models/model/infer"
+dl = DataLoader(training_data, batch_size=1, shuffle=False)
+dlIter = iter(dl)
+
+def infer_mnist():
+    x, y = next(dlIter)
+    data = x.cpu().numpy()
+    reqJson["inputs"][0]["data"] = data.flatten().tolist()
+    reqJson["inputs"][0]["shape"] = [1, 1, 28, 28]
+    headers = {"Content-Type": "application/json", "seldon-model":"mnist-pytorch"}
+    response_raw = requests.post(url, json=reqJson, headers=headers)
+    show_mnist(x)
+    probs = np.array(response_raw.json()["outputs"][0]["data"])
+    print(probs.argmax(axis=0))
+
+
+def show_mnist(X):
+    plt.imshow(X.reshape(28, 28))
+    plt.axis("off")
+    plt.show()
+```
+
+```bash
+cat ./models/mnist-pytorch.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mnist-pytorch
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mnist-pytorch"
+  requirements:
+  - pytorch
+
+```
+
+```bash
+seldon model load -f ./models/mnist-pytorch.yaml
+```
+
+```json
+{}
+
+```
+
+```bash
+seldon model status mnist-pytorch -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+
+```
+
+```python
+infer_mnist()
+```
+
+```
+![png](../images/model-zoo_48_0.png)
+
+```
+
+```
+7
+
+```
+
+```bash
+seldon model unload mnist-pytorch
+```
+
+```json
+{}
+
+```
diff --git a/docs/examples/multi-version.md b/docs/examples/multi-version.md
index b8d25653e6..836929217f 100644
--- a/docs/examples/multi-version.md
+++ b/docs/examples/multi-version.md
@@ -1,8 +1,175 @@
 # Artifact Versions
 
-Run these examples from the `samples` folder.
+## Seldon V2 Kubernetes Multi Version Artifact Examples
 
+We have a Triton model that has two version folders
 
-```{include} ../../../../samples/k8s-multi-version.md
+Model 1 adds 10 to input, Model 2 multiples by 10 the input. The structure of the artifact repo is shown below:
+
+```sh
+config.pbtxt
+1/model.py <add 10>
+2/model.py <mul 10>
+
+```
+
+```python
+import os
+os.environ["NAMESPACE"] = "seldon-mesh"
+```
+
+```python
+MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+MESH_IP=MESH_IP[0]
+import os
+os.environ['MESH_IP'] = MESH_IP
+MESH_IP
+```
+
+```
+'172.19.255.1'
+
+```
+
+### Model
+
+```bash
+cat ./models/multi-version-1.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: math
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/multi-version"
+  artifactVersion: 1
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+kubectl apply -f ./models/multi-version-1.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/math created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/math condition met
+
+```
+
+```bash
+seldon model infer math --inference-mode grpc --inference-host ${MESH_IP}:80 \
+  '{"model_name":"math","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}' | jq -M .
 ```
 
+```json
+{
+  "modelName": "math_1",
+  "modelVersion": "1",
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          11,
+          12,
+          13,
+          14
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+cat ./models/multi-version-2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: math
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/multi-version"
+  artifactVersion: 2
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+kubectl apply -f ./models/multi-version-2.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/math configured
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/math condition met
+
+```
+
+```bash
+seldon model infer math --inference-mode grpc --inference-host ${MESH_IP}:80 \
+  '{"model_name":"math","inputs":[{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}' | jq -M .
+```
+
+```json
+{
+  "modelName": "math_2",
+  "modelVersion": "1",
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          10,
+          20,
+          30,
+          40
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+kubectl delete -f ./models/multi-version-1.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "math" deleted
+
+```
diff --git a/docs/examples/notebooks/ambassador.ipynb b/docs/examples/notebooks/ambassador.ipynb
new file mode 100644
index 0000000000..242eb59afc
--- /dev/null
+++ b/docs/examples/notebooks/ambassador.ipynb
@@ -0,0 +1,583 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fallen-score",
+   "metadata": {},
+   "source": [
+    "## Ambassador Notebook Example\n",
+    "\n",
+    "Assumes\n",
+    "\n",
+    " * You have installed emissary as per their docs\n",
+    " \n",
+    " Tested with\n",
+    " \n",
+    " emissary-ingress-7.3.2 insatlled via [helm](https://www.getambassador.io/docs/emissary/latest/tutorials/getting-started/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "imperial-playing",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.21.255.1'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "INGRESS_IP=!kubectl get svc emissary-ingress -n emissary -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "INGRESS_IP=INGRESS_IP[0]\n",
+    "import os\n",
+    "os.environ['INGRESS_IP'] = INGRESS_IP\n",
+    "INGRESS_IP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lesser-equity",
+   "metadata": {},
+   "source": [
+    "### Ambassador Single Model Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "typical-billy",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: getambassador.io/v3alpha1\r\n",
+      "kind: Host\r\n",
+      "metadata:\r\n",
+      "  name: wildcard\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  hostname: '*'\r\n",
+      "  requestPolicy:\r\n",
+      "    insecure:\r\n",
+      "      action: Route\r\n",
+      "---\r\n",
+      "apiVersion: getambassador.io/v3alpha1\r\n",
+      "kind: Listener\r\n",
+      "metadata:\r\n",
+      "  name: emissary-ingress-listener-8080\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  hostBinding:\r\n",
+      "    namespace:\r\n",
+      "      from: ALL\r\n",
+      "  port: 8080\r\n",
+      "  protocol: HTTP\r\n",
+      "  securityModel: INSECURE\r\n",
+      "---\r\n",
+      "apiVersion: getambassador.io/v3alpha1\r\n",
+      "kind: Mapping\r\n",
+      "metadata:\r\n",
+      "  name: iris-grpc\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  add_request_headers:\r\n",
+      "    seldon-model:\r\n",
+      "      value: iris\r\n",
+      "  grpc: true\r\n",
+      "  hostname: '*'\r\n",
+      "  prefix: /inference.GRPCInferenceService\r\n",
+      "  rewrite: \"\"\r\n",
+      "  service: seldon-mesh:80\r\n",
+      "---\r\n",
+      "apiVersion: getambassador.io/v3alpha1\r\n",
+      "kind: Mapping\r\n",
+      "metadata:\r\n",
+      "  name: iris-http\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  add_request_headers:\r\n",
+      "    seldon-model:\r\n",
+      "      value: iris\r\n",
+      "  hostname: '*'\r\n",
+      "  prefix: /v2/\r\n",
+      "  rewrite: \"\"\r\n",
+      "  service: seldon-mesh:80\r\n",
+      "---\r\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  storageUri: gs://seldon-models/mlserver/iris\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/single-model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "welsh-tribune",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "host.getambassador.io/wildcard created\n",
+      "listener.getambassador.io/emissary-ingress-listener-8080 created\n",
+      "mapping.getambassador.io/iris-grpc created\n",
+      "mapping.getambassador.io/iris-http created\n",
+      "model.mlops.seldon.io/iris created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/single-model | kubectl apply --validate=false -f -"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "reverse-pantyhose",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aquatic-suspect",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*   Trying 172.21.255.1...\n",
+      "* Connected to 172.21.255.1 (172.21.255.1) port 80 (#0)\n",
+      "> POST /v2/models/iris/infer HTTP/1.1\n",
+      "> Host: 172.21.255.1\n",
+      "> User-Agent: curl/7.47.0\n",
+      "> Accept: */*\n",
+      "> Content-Type: application/json\n",
+      "> Content-Length: 94\n",
+      "> \n",
+      "* upload completely sent off: 94 out of 94 bytes\n",
+      "< HTTP/1.1 200 OK\n",
+      "< content-length: 196\n",
+      "< content-type: application/json\n",
+      "< date: Sat, 16 Apr 2022 15:45:43 GMT\n",
+      "< server: envoy\n",
+      "< x-envoy-upstream-service-time: 792\n",
+      "< seldon-route: iris_1\n",
+      "< \n",
+      "* Connection #0 to host 172.21.255.1 left intact\n",
+      "{\"model_name\":\"iris_1\",\"model_version\":\"1\",\"id\":\"72ac79f5-b355-4be3-b8c5-2ebedaa39f60\",\"parameters\":null,\"outputs\":[{\"name\":\"predict\",\"shape\":[1],\"datatype\":\"INT64\",\"parameters\":null,\"data\":[2]}]}"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -v http://${INGRESS_IP}/v2/models/iris/infer -H \"Content-Type: application/json\"\\\n",
+    "        -d '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "consecutive-active",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"iris_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"int64Contents\": [\r\n",
+      "          \"2\"\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"model_name\":\"iris\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../../apis \\\n",
+    "    -proto ../../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    ${INGRESS_IP}:80 inference.GRPCInferenceService/ModelInfer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "compound-steel",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "host.getambassador.io \"wildcard\" deleted\n",
+      "listener.getambassador.io \"emissary-ingress-listener-8080\" deleted\n",
+      "mapping.getambassador.io \"iris-grpc\" deleted\n",
+      "mapping.getambassador.io \"iris-http\" deleted\n",
+      "model.mlops.seldon.io \"iris\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/single-model | kubectl delete -f -"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "attended-storm",
+   "metadata": {},
+   "source": [
+    "### Traffic Split Two Models\n",
+    "\n",
+    "***Currently not working due to this [issue](https://github.com/emissary-ingress/emissary/issues/4062)***\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ahead-samoa",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: getambassador.io/v3alpha1\r\n",
+      "kind: Host\r\n",
+      "metadata:\r\n",
+      "  name: wildcard\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  hostname: '*'\r\n",
+      "  requestPolicy:\r\n",
+      "    insecure:\r\n",
+      "      action: Route\r\n",
+      "---\r\n",
+      "apiVersion: getambassador.io/v3alpha1\r\n",
+      "kind: Listener\r\n",
+      "metadata:\r\n",
+      "  name: emissary-ingress-listener-8080\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  hostBinding:\r\n",
+      "    namespace:\r\n",
+      "      from: ALL\r\n",
+      "  port: 8080\r\n",
+      "  protocol: HTTP\r\n",
+      "  securityModel: INSECURE\r\n",
+      "---\r\n",
+      "apiVersion: getambassador.io/v3alpha1\r\n",
+      "kind: Mapping\r\n",
+      "metadata:\r\n",
+      "  name: iris1-grpc\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  add_request_headers:\r\n",
+      "    seldon-model:\r\n",
+      "      value: iris1\r\n",
+      "  grpc: true\r\n",
+      "  hostname: '*'\r\n",
+      "  prefix: /inference.GRPCInferenceService\r\n",
+      "  rewrite: \"\"\r\n",
+      "  service: seldon-mesh:80\r\n",
+      "---\r\n",
+      "apiVersion: getambassador.io/v3alpha1\r\n",
+      "kind: Mapping\r\n",
+      "metadata:\r\n",
+      "  name: iris1-http\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  add_request_headers:\r\n",
+      "    seldon-model:\r\n",
+      "      value: iris1\r\n",
+      "  add_response_headers:\r\n",
+      "    seldon_model:\r\n",
+      "      value: iris1\r\n",
+      "  hostname: '*'\r\n",
+      "  prefix: /v2\r\n",
+      "  rewrite: \"\"\r\n",
+      "  service: seldon-mesh:80\r\n",
+      "---\r\n",
+      "apiVersion: getambassador.io/v3alpha1\r\n",
+      "kind: Mapping\r\n",
+      "metadata:\r\n",
+      "  name: iris2-grpc\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  add_request_headers:\r\n",
+      "    seldon-model:\r\n",
+      "      value: iris2\r\n",
+      "  grpc: true\r\n",
+      "  hostname: '*'\r\n",
+      "  prefix: /inference.GRPCInferenceService\r\n",
+      "  rewrite: \"\"\r\n",
+      "  service: seldon-mesh:80\r\n",
+      "  weight: 50\r\n",
+      "---\r\n",
+      "apiVersion: getambassador.io/v3alpha1\r\n",
+      "kind: Mapping\r\n",
+      "metadata:\r\n",
+      "  name: iris2-http\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  add_request_headers:\r\n",
+      "    seldon-model:\r\n",
+      "      value: iris2\r\n",
+      "  add_response_headers:\r\n",
+      "    seldon_model:\r\n",
+      "      value: iris2\r\n",
+      "  hostname: '*'\r\n",
+      "  prefix: /v2\r\n",
+      "  rewrite: \"\"\r\n",
+      "  service: seldon-mesh:80\r\n",
+      "  weight: 50\r\n",
+      "---\r\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris1\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  storageUri: gs://seldon-models/mlserver/iris\r\n",
+      "---\r\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris2\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  storageUri: gs://seldon-models/mlserver/iris\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/traffic-split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "comprehensive-guide",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "host.getambassador.io/wildcard created\n",
+      "listener.getambassador.io/emissary-ingress-listener-8080 created\n",
+      "mapping.getambassador.io/iris1-grpc created\n",
+      "mapping.getambassador.io/iris1-http created\n",
+      "mapping.getambassador.io/iris2-grpc created\n",
+      "mapping.getambassador.io/iris2-http created\n",
+      "model.mlops.seldon.io/iris1 created\n",
+      "model.mlops.seldon.io/iris2 created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/traffic-split | kubectl apply -f -"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "explicit-plasma",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris1 condition met\r\n",
+      "model.mlops.seldon.io/iris2 condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "organizational-spokesman",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*   Trying 172.21.255.1...\n",
+      "* Connected to 172.21.255.1 (172.21.255.1) port 80 (#0)\n",
+      "> POST /v2/models/iris/infer HTTP/1.1\n",
+      "> Host: 172.21.255.1\n",
+      "> User-Agent: curl/7.47.0\n",
+      "> Accept: */*\n",
+      "> Content-Type: application/json\n",
+      "> Content-Length: 94\n",
+      "> \n",
+      "* upload completely sent off: 94 out of 94 bytes\n",
+      "< HTTP/1.1 200 OK\n",
+      "< content-length: 197\n",
+      "< content-type: application/json\n",
+      "< date: Sat, 16 Apr 2022 15:46:17 GMT\n",
+      "< server: envoy\n",
+      "< x-envoy-upstream-service-time: 920\n",
+      "< seldon-route: iris2_1\n",
+      "< seldon_model: iris2\n",
+      "< \n",
+      "* Connection #0 to host 172.21.255.1 left intact\n",
+      "{\"model_name\":\"iris2_1\",\"model_version\":\"1\",\"id\":\"ed521c32-cd85-4cb8-90eb-7c896803f271\",\"parameters\":null,\"outputs\":[{\"name\":\"predict\",\"shape\":[1],\"datatype\":\"INT64\",\"parameters\":null,\"data\":[2]}]}"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -v http://${INGRESS_IP}/v2/models/iris/infer -H \"Content-Type: application/json\" \\\n",
+    "        -d '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "thorough-surgery",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"iris2_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"int64Contents\": [\r\n",
+      "          \"2\"\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"model_name\":\"iris1\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../../apis \\\n",
+    "    -proto ../../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    ${INGRESS_IP}:80 inference.GRPCInferenceService/ModelInfer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "mechanical-diagnosis",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "host.getambassador.io \"wildcard\" deleted\n",
+      "listener.getambassador.io \"emissary-ingress-listener-8080\" deleted\n",
+      "mapping.getambassador.io \"iris1-grpc\" deleted\n",
+      "mapping.getambassador.io \"iris1-http\" deleted\n",
+      "mapping.getambassador.io \"iris2-grpc\" deleted\n",
+      "mapping.getambassador.io \"iris2-http\" deleted\n",
+      "model.mlops.seldon.io \"iris1\" deleted\n",
+      "model.mlops.seldon.io \"iris2\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/traffic-split | kubectl delete -f -"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/cli-tests.ipynb b/docs/examples/notebooks/cli-tests.ipynb
new file mode 100644
index 0000000000..37c0e36698
--- /dev/null
+++ b/docs/examples/notebooks/cli-tests.ipynb
@@ -0,0 +1,577 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## CLI Tests\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfa9ccc9",
+   "metadata": {},
+   "source": [
+    "## List resources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5fd9e443",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/sklearn-iris-gs.yaml\n",
+    "!seldon model load -f ./models/tfsimple1.yaml\n",
+    "!seldon model load -f ./models/sklearn2.yaml\n",
+    "!seldon model load -f ./models/cifar10.yaml\n",
+    "!seldon experiment start -f ./experiments/ab-default-model.yaml \n",
+    "!seldon pipeline load -f ./pipelines/cifar10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8e34a00a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model\t\tstate\t\treason\r\n",
+      "-----\t\t-----\t\t------\r\n",
+      "iris\t\tModelAvailable\t\r\n",
+      "tfsimple1\tModelAvailable\t\r\n",
+      "iris2\t\tModelAvailable\t\r\n",
+      "cifar10\t\tModelAvailable\t\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b7b66dfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"name\": \"cifar10_1\",\r\n",
+      "\t\"versions\": [\r\n",
+      "\t\t\"1\"\r\n",
+      "\t],\r\n",
+      "\t\"platform\": \"tensorflow_savedmodel\",\r\n",
+      "\t\"inputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"input_1\",\r\n",
+      "\t\t\t\"datatype\": \"FP32\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t-1,\r\n",
+      "\t\t\t\t32,\r\n",
+      "\t\t\t\t32,\r\n",
+      "\t\t\t\t3\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t],\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"fc10\",\r\n",
+      "\t\t\t\"datatype\": \"FP32\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t-1,\r\n",
+      "\t\t\t\t10\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model metadata cifar10 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8241d2cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server\t\treplicas\tmodels\r\n",
+      "------\t\t--------\t------\r\n",
+      "mlserver\t1\t\t2\r\n",
+      "triton\t\t1\t\t2\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon server list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ba809650",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "experiment\t\tactive\t\r\n",
+      "----------\t\t------\t\r\n",
+      "experiment-sample\ttrue\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5cc2ca16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline\t\tstate\t\treason\r\n",
+      "--------\t\t-----\t\t------\r\n",
+      "cifar10-production\tPipelineReady\tcreated pipeline\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c486937c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload iris\n",
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload iris2\n",
+    "!seldon model unload cifar10\n",
+    "!seldon experiment stop experiment-sample\n",
+    "!seldon pipeline unload cifar10-production"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b1925df",
+   "metadata": {},
+   "source": [
+    "### Resource Errors from CLI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8b4be911",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storagUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/error-bad-spec.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "checked-cream",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: json: unknown field \"storagUri\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/error-bad-spec.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cb9f8f62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple-conditional\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "  - name: conditional\r\n",
+      "  - name: mul10\r\n",
+      "    input:\r\n",
+      "    - conditional.outputs.OUTPUT0\r\n",
+      "    tensorMap:\r\n",
+      "      conditional.outputs.OUTPUT0: INPUT\r\n",
+      "  - name: add10\r\n",
+      "    inputs:\r\n",
+      "    - conditional.outputs.OUTPUT1\r\n",
+      "    tensorMap:\r\n",
+      "      conditional.outputs.OUTPUT1: INPUT\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - mul10\r\n",
+      "    - add10\r\n",
+      "    stepsJoin: any\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/error-bad-spec.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f229a839",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: json: unknown field \"input\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/error-bad-spec.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e5988e99",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: experiment-sample\r\n",
+      "spec:\r\n",
+      "  default: iris\r\n",
+      "  candidate:\r\n",
+      "  - name: iris\r\n",
+      "    weight: 50\r\n",
+      "  - name: iris2\r\n",
+      "    weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/error-bad-spec.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "13058853",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: json: unknown field \"candidate\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment start -f ./experiments/error-bad-spec.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "08c65f5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: iris\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - iris\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/error-step-name.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "dfa15efb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: rpc error: code = FailedPrecondition desc = pipeline iris must not have a step name with the same name as pipeline name\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/error-step-name.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "da863b1c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: iris-pipeline\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: first\r\n",
+      "    - name: second\r\n",
+      "      inputs:\r\n",
+      "        -\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - iris\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/error-empty-input.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d5e4ff75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: rpc error: code = FailedPrecondition desc = pipeline iris-pipeline step second has an empty input\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/error-empty-input.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6d7b65d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: iris-pipeline\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: first\r\n",
+      "    - name: second\r\n",
+      "    - name: third\r\n",
+      "      inputs:\r\n",
+      "        - first\r\n",
+      "      triggers:\r\n",
+      "        -\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - iris\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/error-empty-trigger.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e643dd64",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: rpc error: code = FailedPrecondition desc = pipeline iris-pipeline step third has an empty trigger\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/error-empty-trigger.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e476335",
+   "metadata": {},
+   "source": [
+    "## Failed scheduling errors from CLI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "81f6ff20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: rpc error: code = FailedPrecondition desc = failed to schedule model badcapabilities. [failed replica filter RequirementsReplicaFilter for server replica mlserver:0 : model requirements [foobar] replica capabilities [mlserver alibi-detect alibi-explain huggingface lightgbm mlflow python sklearn spark-mlib xgboost] failed replica filter RequirementsReplicaFilter for server replica triton:0 : model requirements [foobar] replica capabilities [triton dali fil onnx openvino python pytorch tensorflow tensorrt]]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/error-bad-capabilities.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a1874007",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload badcapabilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "109cf2e1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/experiment_versions.ipynb b/docs/examples/notebooks/experiment_versions.ipynb
new file mode 100644
index 0000000000..fe3761662f
--- /dev/null
+++ b/docs/examples/notebooks/experiment_versions.ipynb
@@ -0,0 +1,965 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Experiment Version Tests\n",
+    "\n",
+    "This notebook will show how we can update running experiments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amber-inclusion",
+   "metadata": {},
+   "source": [
+    "### Test change candidate for a model\n",
+    "\n",
+    "We will use three SKlearn Iris classification models to illustrate experiment updates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "359b456e",
+   "metadata": {},
+   "source": [
+    "Load all models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "elect-samba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/sklearn1.yaml\n",
+    "!seldon model load -f ./models/sklearn2.yaml\n",
+    "!seldon model load -f ./models/sklearn3.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "floral-moisture",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status iris -w ModelAvailable\n",
+    "!seldon model status iris2 -w ModelAvailable\n",
+    "!seldon model status iris3 -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14a99e65",
+   "metadata": {},
+   "source": [
+    "Let's call all three models individually first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0a7cfc57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "570c4769",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris2 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a102ca45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris3_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris3 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89efd99a",
+   "metadata": {},
+   "source": [
+    "We will start an experiment to change the iris endpoint to split traffic with the `iris2` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "10fe4447",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: experiment-sample\r\n",
+      "spec:\r\n",
+      "  default: iris\r\n",
+      "  candidates:\r\n",
+      "  - name: iris\r\n",
+      "    weight: 50\r\n",
+      "  - name: iris2\r\n",
+      "    weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/ab-default-model.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "differential-array",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment start -f ./experiments/ab-default-model.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "overhead-banks",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"experimentName\": \"experiment-sample\",\r\n",
+      "  \"active\": true,\r\n",
+      "  \"candidatesReady\": true,\r\n",
+      "  \"mirrorReady\": true,\r\n",
+      "  \"statusDescription\": \"experiment active\",\r\n",
+      "  \"kubernetesMeta\": {}\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment status experiment-sample -w | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05cdc618",
+   "metadata": {},
+   "source": [
+    "Now when we call the iris model we should see a roughly 50/50 split between the two models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "finite-significance",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::48 :iris_1::52]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 100 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b1c7ed7",
+   "metadata": {},
+   "source": [
+    "Now we update the experiment to change to a split with the `iris3` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2f614d4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: experiment-sample\r\n",
+      "spec:\r\n",
+      "  default: iris\r\n",
+      "  candidates:\r\n",
+      "  - name: iris\r\n",
+      "    weight: 50\r\n",
+      "  - name: iris3\r\n",
+      "    weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/ab-default-model2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "03aada00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment start -f ./experiments/ab-default-model2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "64e0ac04",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"experimentName\": \"experiment-sample\",\r\n",
+      "  \"active\": true,\r\n",
+      "  \"candidatesReady\": true,\r\n",
+      "  \"mirrorReady\": true,\r\n",
+      "  \"statusDescription\": \"experiment active\",\r\n",
+      "  \"kubernetesMeta\": {}\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment status experiment-sample -w | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56fbcbf4",
+   "metadata": {},
+   "source": [
+    "Now we should see a split with the `iris3` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "73e85d3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris3_1::42 :iris_1::58]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 100 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "downtown-construction",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment stop experiment-sample"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51ef922f",
+   "metadata": {},
+   "source": [
+    "Now the experiment has been stopped we check everything as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8ca25a85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d6c20826",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris2 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a695dd79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris3_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris3 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "077bbce8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload iris\n",
+    "!seldon model unload iris2\n",
+    "!seldon model unload iris3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdd680a2",
+   "metadata": {},
+   "source": [
+    "### Test change default model in an experiment\n",
+    "\n",
+    "Here we test changing the model we want to split traffic on. We will use three SKlearn Iris classification models to illustrate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0c477529",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/sklearn1.yaml\n",
+    "!seldon model load -f ./models/sklearn2.yaml\n",
+    "!seldon model load -f ./models/sklearn3.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "440a0751",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status iris -w ModelAvailable\n",
+    "!seldon model status iris2 -w ModelAvailable\n",
+    "!seldon model status iris3 -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8e89353",
+   "metadata": {},
+   "source": [
+    "Let's call all three models to verify initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "acebcac3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2c58faba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris2 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5d0ebaa8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris3_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris3 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a42a568e",
+   "metadata": {},
+   "source": [
+    "Now we start an experiment to change calls to the `iris` model to split with the `iris2` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "6ed52032",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: experiment-sample\r\n",
+      "spec:\r\n",
+      "  default: iris\r\n",
+      "  candidates:\r\n",
+      "  - name: iris\r\n",
+      "    weight: 50\r\n",
+      "  - name: iris2\r\n",
+      "    weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/ab-default-model.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "92c1044f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment start -f ./experiments/ab-default-model.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "4499a50c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"experimentName\": \"experiment-sample\",\r\n",
+      "  \"active\": true,\r\n",
+      "  \"candidatesReady\": true,\r\n",
+      "  \"mirrorReady\": true,\r\n",
+      "  \"statusDescription\": \"experiment active\",\r\n",
+      "  \"kubernetesMeta\": {}\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment status experiment-sample -w | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abc376cb",
+   "metadata": {},
+   "source": [
+    "Run a set of calls and record which route the traffic took. There should be roughly a 50/50 split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f09e5168",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::51 :iris_1::49]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 100 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb0d3927",
+   "metadata": {},
+   "source": [
+    "Now let's change the model we want to experiment to modify to the `iris3` model. Splitting between that and `iris2`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "c26e67ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: experiment-sample\r\n",
+      "spec:\r\n",
+      "  default: iris3\r\n",
+      "  candidates:\r\n",
+      "  - name: iris3\r\n",
+      "    weight: 50\r\n",
+      "  - name: iris2\r\n",
+      "    weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/ab-default-model3.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "71484348",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment start -f ./experiments/ab-default-model3.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "82c0da83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"experimentName\": \"experiment-sample\",\r\n",
+      "  \"active\": true,\r\n",
+      "  \"candidatesReady\": true,\r\n",
+      "  \"mirrorReady\": true,\r\n",
+      "  \"statusDescription\": \"experiment active\",\r\n",
+      "  \"kubernetesMeta\": {}\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment status experiment-sample -w | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b1768bb",
+   "metadata": {},
+   "source": [
+    "Let's check the iris model is now as before but the iris3 model has traffic split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "112c92dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "07033575",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::25 :iris3_1::25]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris3 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "29ae8890",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris2 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "d4aced42",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment stop experiment-sample"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0efd0a45",
+   "metadata": {},
+   "source": [
+    "Finally, let's check now the experiment has stopped as is as at the start."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "832aab89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "2b846b96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris2 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a239c6ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris3_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris3 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "d0fe31cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload iris\n",
+    "!seldon model unload iris2\n",
+    "!seldon model unload iris3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2aec7ac8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/explainer-examples.ipynb b/docs/examples/notebooks/explainer-examples.ipynb
new file mode 100644
index 0000000000..6d8e9b1c1c
--- /dev/null
+++ b/docs/examples/notebooks/explainer-examples.ipynb
@@ -0,0 +1,542 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8f9344b4",
+   "metadata": {},
+   "source": [
+    "## Anchor Tabular Explainer for SKLearn Income Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bed5b232",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: income\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/examples/mlserver_1.3.5/income/classifier\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/income.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e65edf6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/income.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b1a65e4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status income -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1729d991",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"income_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"c65b8302-85af-4bac-aac5-91e3bedebee8\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer income \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 12], \"datatype\": \"FP32\", \"data\": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "843a5a70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: income-explainer\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/examples/mlserver_1.3.5/income/explainer\"\r\n",
+      "  explainer:\r\n",
+      "    type: anchor_tabular\r\n",
+      "    modelRef: income\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/income-explainer.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "73de58f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/income-explainer.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "10e50de8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status income-explainer -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6191c534",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"income-explainer_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"a22c3785-ff3b-4504-9b3c-199aa48a62d6\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"explanation\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"BYTES\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"str\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t\"{\\\"meta\\\": {\\\"name\\\": \\\"AnchorTabular\\\", \\\"type\\\": [\\\"blackbox\\\"], \\\"explanations\\\": [\\\"local\\\"], \\\"params\\\": {\\\"seed\\\": 1, \\\"disc_perc\\\": [25, 50, 75], \\\"threshold\\\": 0.95, \\\"delta\\\": 0.1, \\\"tau\\\": 0.15, \\\"batch_size\\\": 100, \\\"coverage_samples\\\": 10000, \\\"beam_size\\\": 1, \\\"stop_on_first\\\": false, \\\"max_anchor_size\\\": null, \\\"min_samples_start\\\": 100, \\\"n_covered_ex\\\": 10, \\\"binary_cache_size\\\": 10000, \\\"cache_margin\\\": 1000, \\\"verbose\\\": false, \\\"verbose_every\\\": 1, \\\"kwargs\\\": {}}, \\\"version\\\": \\\"0.9.0\\\"}, \\\"data\\\": {\\\"anchor\\\": [\\\"Marital Status = Never-Married\\\", \\\"Relationship = Own-child\\\"], \\\"precision\\\": 0.9518716577540107, \\\"coverage\\\": 0.07165109034267912, \\\"raw\\\": {\\\"feature\\\": [3, 5], \\\"mean\\\": [0.7959381044487428, 0.9518716577540107], \\\"precision\\\": [0.7959381044487428, 0.9518716577540107], \\\"coverage\\\": [0.3037383177570093, 0.07165109034267912], \\\"examples\\\": [{\\\"covered_true\\\": [[52, 5, 5, 1, 8, 1, 2, 0, 0, 0, 50, 9], [49, 4, 1, 1, 4, 4, 1, 0, 0, 0, 40, 1], [23, 4, 1, 1, 6, 1, 4, 1, 0, 0, 40, 9], [55, 2, 1, 1, 5, 1, 4, 0, 0, 0, 48, 9], [22, 4, 1, 1, 2, 3, 4, 0, 0, 0, 15, 9], [51, 4, 2, 1, 5, 0, 1, 1, 0, 0, 99, 4], [40, 4, 1, 1, 5, 1, 4, 0, 0, 0, 40, 9], [40, 6, 1, 1, 2, 0, 4, 1, 0, 0, 50, 9], [50, 5, 5, 1, 6, 0, 4, 1, 0, 0, 55, 9], [41, 4, 1, 1, 6, 0, 4, 1, 0, 0, 40, 9]], \\\"covered_false\\\": [[42, 4, 1, 1, 8, 0, 4, 1, 0, 2415, 60, 9], [48, 6, 2, 1, 5, 4, 4, 0, 0, 0, 60, 9], [37, 4, 1, 1, 5, 0, 4, 1, 0, 0, 45, 9], [57, 4, 5, 1, 8, 0, 4, 1, 0, 0, 50, 9], [63, 7, 2, 1, 8, 0, 4, 1, 0, 1902, 50, 9], [51, 4, 5, 1, 8, 0, 4, 1, 0, 1887, 47, 9], [51, 2, 2, 1, 8, 1, 4, 0, 0, 0, 45, 9], [68, 7, 5, 1, 5, 0, 4, 1, 0, 2377, 42, 0], [45, 4, 1, 1, 8, 0, 4, 1, 15024, 0, 40, 9], [45, 4, 1, 1, 8, 0, 4, 1, 0, 1977, 60, 9]], \\\"uncovered_true\\\": [], \\\"uncovered_false\\\": []}, {\\\"covered_true\\\": [[44, 6, 5, 1, 8, 3, 4, 0, 0, 1902, 60, 9], [58, 7, 2, 1, 5, 3, 1, 1, 4064, 0, 40, 1], [50, 7, 1, 1, 1, 3, 2, 0, 0, 0, 37, 9], [34, 4, 2, 1, 5, 3, 4, 1, 0, 0, 45, 9], [45, 4, 1, 1, 5, 3, 4, 1, 0, 0, 40, 9], [33, 7, 5, 1, 5, 3, 1, 1, 0, 0, 30, 6], [61, 7, 2, 1, 5, 3, 4, 1, 0, 0, 40, 0], [35, 4, 5, 1, 1, 3, 4, 1, 0, 0, 40, 9], [71, 2, 1, 1, 5, 3, 4, 0, 0, 0, 6, 9], [44, 4, 1, 1, 8, 3, 2, 1, 0, 0, 35, 9]], \\\"covered_false\\\": [[30, 4, 5, 1, 5, 3, 4, 1, 10520, 0, 40, 9], [54, 7, 2, 1, 8, 3, 4, 1, 0, 1902, 50, 9], [66, 6, 2, 1, 6, 3, 4, 1, 0, 2377, 25, 9], [35, 4, 2, 1, 5, 3, 4, 1, 7298, 0, 40, 9], [44, 4, 1, 1, 8, 3, 4, 1, 7298, 0, 48, 9], [31, 4, 1, 1, 8, 3, 4, 0, 13550, 0, 50, 9], [35, 4, 1, 1, 8, 3, 4, 1, 8614, 0, 45, 9]], \\\"uncovered_true\\\": [], \\\"uncovered_false\\\": []}], \\\"all_precision\\\": 0, \\\"num_preds\\\": 1000000, \\\"success\\\": true, \\\"names\\\": [\\\"Marital Status = Never-Married\\\", \\\"Relationship = Own-child\\\"], \\\"prediction\\\": [0], \\\"instance\\\": [47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0], \\\"instances\\\": [[47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0]]}}}\"\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer income-explainer \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 12], \"datatype\": \"FP32\", \"data\": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a8d8b077",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload income-explainer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bce80764",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload income"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "167c25aa",
+   "metadata": {},
+   "source": [
+    "## Anchor Text Explainer for SKLearn Movies Sentiment Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cc92f3fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: sentiment\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/moviesentiment-sklearn\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/moviesentiment.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "179d9d68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/moviesentiment.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1dce2d5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status sentiment -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9e82d700",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"sentiment_2\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"f5c07363-7e9d-4f09-aa30-228c81fdf4a4\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer sentiment \\\n",
+    "  '{\"parameters\": {\"content_type\": \"str\"}, \"inputs\": [{\"name\": \"foo\", \"data\": [\"I am good\"], \"datatype\": \"BYTES\",\"shape\": [1]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "120af99f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: sentiment-explainer\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/moviesentiment-sklearn-explainer\"\r\n",
+      "  explainer:\r\n",
+      "    type: anchor_text\r\n",
+      "    modelRef: sentiment\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/moviesentiment-explainer.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d8fbec66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/moviesentiment-explainer.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c21e49f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status sentiment-explainer -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ed7d4892",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: V2 server error: 500 Traceback (most recent call last):\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/starlette/middleware/errors.py\", line 162, in __call__\r\n",
+      "    await self.app(scope, receive, _send)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/starlette_exporter/middleware.py\", line 307, in __call__\r\n",
+      "    await self.app(scope, receive, wrapped_send)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/starlette/middleware/gzip.py\", line 24, in __call__\r\n",
+      "    await responder(scope, receive, send)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/starlette/middleware/gzip.py\", line 44, in __call__\r\n",
+      "    await self.app(scope, receive, self.send_with_gzip)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/starlette/middleware/exceptions.py\", line 79, in __call__\r\n",
+      "    raise exc\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/starlette/middleware/exceptions.py\", line 68, in __call__\r\n",
+      "    await self.app(scope, receive, sender)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/fastapi/middleware/asyncexitstack.py\", line 21, in __call__\r\n",
+      "    raise e\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/fastapi/middleware/asyncexitstack.py\", line 18, in __call__\r\n",
+      "    await self.app(scope, receive, send)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/starlette/routing.py\", line 706, in __call__\r\n",
+      "    await route.handle(scope, receive, send)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/starlette/routing.py\", line 276, in handle\r\n",
+      "    await self.app(scope, receive, send)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/starlette/routing.py\", line 66, in app\r\n",
+      "    response = await func(request)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/mlserver/rest/app.py\", line 42, in custom_route_handler\r\n",
+      "    return await original_route_handler(request)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/fastapi/routing.py\", line 237, in app\r\n",
+      "    raw_response = await run_endpoint_function(\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/fastapi/routing.py\", line 163, in run_endpoint_function\r\n",
+      "    return await dependant.call(**values)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/mlserver/rest/endpoints.py\", line 99, in infer\r\n",
+      "    inference_response = await self._data_plane.infer(\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/mlserver/handlers/dataplane.py\", line 103, in infer\r\n",
+      "    prediction = await model.predict(payload)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/mlserver_alibi_explain/runtime.py\", line 86, in predict\r\n",
+      "    output_data = await self._async_explain_impl(input_data, payload.parameters)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/mlserver_alibi_explain/runtime.py\", line 119, in _async_explain_impl\r\n",
+      "    explanation = await loop.run_in_executor(self._executor, explain_call)\r\n",
+      "  File \"/opt/conda/lib/python3.8/concurrent/futures/thread.py\", line 57, in run\r\n",
+      "    result = self.fn(*self.args, **self.kwargs)\r\n",
+      "  File \"/opt/conda/lib/python3.8/site-packages/mlserver_alibi_explain/explainers/black_box_runtime.py\", line 62, in _explain_impl\r\n",
+      "    input_data = input_data[0]\r\n",
+      "KeyError: 0\r\n",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer sentiment-explainer \\\n",
+    "  '{\"parameters\": {\"content_type\": \"str\"}, \"inputs\": [{\"name\": \"foo\", \"data\": [\"I am good\"], \"datatype\": \"BYTES\",\"shape\": [1]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "55e7a86b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload sentiment-explainer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4ce494ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload sentiment"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e08638e9ca1552ac93b876f0aeccba35125afc40c35f738854ef3f566228f7ad"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/huggingface.ipynb b/docs/examples/notebooks/huggingface.ipynb
new file mode 100644
index 0000000000..4c46e84384
--- /dev/null
+++ b/docs/examples/notebooks/huggingface.ipynb
@@ -0,0 +1,385 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Huggingface Examples\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b1925df",
+   "metadata": {},
+   "source": [
+    "### Text Generation Model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8b4be911",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: text-gen\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/huggingface/text-generation\"\r\n",
+      "  requirements:\r\n",
+      "  - huggingface\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/hf-text-gen.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26c11833",
+   "metadata": {},
+   "source": [
+    "Load the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "checked-cream",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/hf-text-gen.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "interracial-secret",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status text-gen -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "upper-cholesterol",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"text-gen_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"121ff5f4-1d4a-46d0-9a5e-4cd3b11040df\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"output\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"BYTES\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"hg_jsonlist\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t\"{\\\"generated_text\\\": \\\"Once upon a time in a galaxy far away, the planet is full of strange little creatures. A very strange combination of creatures in that universe, that is. A strange combination of creatures in that universe, that is. A kind of creature that is\\\"}\"\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer text-gen \\\n",
+    "  '{\"inputs\": [{\"name\": \"args\",\"shape\": [1],\"datatype\": \"BYTES\",\"data\": [\"Once upon a time in a galaxy far away\"]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "right-talent",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = !seldon model infer text-gen --inference-mode grpc \\\n",
+    "   '{\"inputs\":[{\"name\":\"args\",\"contents\":{\"bytes_contents\":[\"T25jZSB1cG9uIGEgdGltZSBpbiBhIGdhbGF4eSBmYXIgYXdheQo=\"]},\"datatype\":\"BYTES\",\"shape\":[1]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "4a7d7108",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'{\"generated_text\": \"Once upon a time in a galaxy far away\\\\n\\\\nThe Universe is a big and massive place. How can you feel any of this? Your body doesn\\'t make sense if the Universe is in full swing \\\\u2014 you don\\'t have to remember whether the\"}'"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import base64\n",
+    "r = json.loads(res[0])\n",
+    "base64.b64decode(r[\"outputs\"][0][\"contents\"][\"bytesContents\"][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d95a5f03",
+   "metadata": {},
+   "source": [
+    "Unload the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "ceramic-illness",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload text-gen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ff63bb7",
+   "metadata": {},
+   "source": [
+    "### Custom Text Generation Model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "6d48e38c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: custom-tiny-stories-text-gen\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/huggingface-text-gen-custom-tiny-stories\"\r\n",
+      "  requirements:\r\n",
+      "    - huggingface\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/hf-text-gen-custom-tiny-stories.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62ec637f",
+   "metadata": {},
+   "source": [
+    "Load the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "abef8088",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/hf-text-gen-custom-tiny-stories.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "f25c54d4",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status custom-tiny-stories-text-gen -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "21f26bbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"custom-tiny-stories-text-gen_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"d0fce59c-76e2-4f81-9711-1c93d08bcbf9\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"output\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"BYTES\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"hg_jsonlist\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t\"{\\\"generated_text\\\": \\\"Once upon a time in a galaxy far away. It was a very special place to live.\\\\n\\\"}\"\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer custom-tiny-stories-text-gen \\\n",
+    "  '{\"inputs\": [{\"name\": \"args\",\"shape\": [1],\"datatype\": \"BYTES\",\"data\": [\"Once upon a time in a galaxy far away\"]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "30e8bb29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = !seldon model infer custom-tiny-stories-text-gen --inference-mode grpc \\\n",
+    "   '{\"inputs\":[{\"name\":\"args\",\"contents\":{\"bytes_contents\":[\"T25jZSB1cG9uIGEgdGltZSBpbiBhIGdhbGF4eSBmYXIgYXdheQo=\"]},\"datatype\":\"BYTES\",\"shape\":[1]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "eab4929b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'{\"generated_text\": \"Once upon a time in a galaxy far away\\\\nOne night, a little girl named Lily went to\"}'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import json\n",
+    "import base64\n",
+    "r = json.loads(res[0])\n",
+    "base64.b64decode(r[\"outputs\"][0][\"contents\"][\"bytesContents\"][0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "877e266c",
+   "metadata": {},
+   "source": [
+    "Unload the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "e1829439",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload custom-tiny-stories-text-gen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6b654e6",
+   "metadata": {},
+   "source": [
+    "````\n",
+    "As a next step, why not try running a larger-scale model? You can find a definition for one in ./models/hf-text-gen-custom-gpt2.yaml. However, you may need to request and allocate more memory!\n",
+    "````"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/infer.ipynb b/docs/examples/notebooks/infer.ipynb
new file mode 100644
index 0000000000..075a1d6434
--- /dev/null
+++ b/docs/examples/notebooks/infer.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5760f3c8",
+   "metadata": {},
+   "source": [
+    "## Conditional Pipeline using PandasQuery\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e5485c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: choice-is-one\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/examples/pandasquery\"\n",
+      "  requirements:\n",
+      "  - mlserver\n",
+      "  - python\n",
+      "  parameters:\n",
+      "  - name: query\n",
+      "    value: \"choice == 1\"\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: choice-is-two\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/examples/pandasquery\"\n",
+      "  requirements:\n",
+      "  - mlserver\n",
+      "  - python\n",
+      "  parameters:\n",
+      "  - name: query\n",
+      "    value: \"choice == 2\"\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: add10\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/add10\"\n",
+      "  requirements:\n",
+      "  - triton\n",
+      "  - python\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: mul10\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/mul10\"\n",
+      "  requirements:\n",
+      "  - triton\n",
+      "  - python\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ../../models/choice1.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ../../models/choice2.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ../../models/add10.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ../../models/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c31d41c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ../../models/choice1.yaml\n",
+    "!seldon model load -f ../../models/choice2.yaml\n",
+    "!seldon model load -f ../../models/add10.yaml\n",
+    "!seldon model load -f ../../models/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cb503272",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status choice-is-one -w ModelAvailable \n",
+    "!seldon model status choice-is-two -w ModelAvailable \n",
+    "!seldon model status add10 -w ModelAvailable \n",
+    "!seldon model status mul10 -w ModelAvailable "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "36300582",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: choice\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "  - name: choice-is-one\r\n",
+      "  - name: mul10\r\n",
+      "    inputs:\r\n",
+      "    - choice.inputs.INPUT\r\n",
+      "    triggers:\r\n",
+      "    - choice-is-one.outputs.choice\r\n",
+      "  - name: choice-is-two\r\n",
+      "  - name: add10\r\n",
+      "    inputs:\r\n",
+      "    - choice.inputs.INPUT\r\n",
+      "    triggers:\r\n",
+      "    - choice-is-two.outputs.choice\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - mul10\r\n",
+      "    - add10\r\n",
+      "    stepsJoin: any\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ../../pipelines/choice.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ba8bd865",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ../../pipelines/choice.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "50f88343",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"choice\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"choice\",\r\n",
+      "        \"uid\": \"cifel9aufmbc73e5intg\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"add10\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"choice.inputs.INPUT\"\r\n",
+      "            ],\r\n",
+      "            \"triggers\": [\r\n",
+      "              \"choice-is-two.outputs.choice\"\r\n",
+      "            ]\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"choice-is-one\"\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"choice-is-two\"\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"mul10\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"choice.inputs.INPUT\"\r\n",
+      "            ],\r\n",
+      "            \"triggers\": [\r\n",
+      "              \"choice-is-one.outputs.choice\"\r\n",
+      "            ]\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"mul10.outputs\",\r\n",
+      "            \"add10.outputs\"\r\n",
+      "          ],\r\n",
+      "          \"stepsJoin\": \"ANY\"\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-30T14:45:57.284684328Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status choice -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "209c00c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          50,\r\n",
+      "          60,\r\n",
+      "          70,\r\n",
+      "          80\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer choice --inference-mode grpc \\\n",
+    " '{\"model_name\":\"choice\",\"inputs\":[{\"name\":\"choice\",\"contents\":{\"int_contents\":[1]},\"datatype\":\"INT32\",\"shape\":[1]},{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[5,6,7,8]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "26fab83d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          15,\r\n",
+      "          16,\r\n",
+      "          17,\r\n",
+      "          18\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer choice --inference-mode grpc \\\n",
+    " '{\"model_name\":\"choice\",\"inputs\":[{\"name\":\"choice\",\"contents\":{\"int_contents\":[2]},\"datatype\":\"INT32\",\"shape\":[1]},{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[5,6,7,8]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a2d69d25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload choice-is-one\n",
+    "!seldon model unload choice-is-two\n",
+    "!seldon model unload add10\n",
+    "!seldon model unload mul10\n",
+    "!seldon pipeline unload choice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba3ba4ba",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/inference.ipynb b/docs/examples/notebooks/inference.ipynb
new file mode 100644
index 0000000000..0fb692e046
--- /dev/null
+++ b/docs/examples/notebooks/inference.ipynb
@@ -0,0 +1,557 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Inference Examples\n",
+    "\n",
+    "We will show:\n",
+    "\n",
+    " * Model inference to a Tensorflow model\n",
+    "   * REST and gRPC using seldon CLI, curl and grpcurl\n",
+    " * Pipeline inference\n",
+    "   * REST and gRPC using seldon CLI, curl and grpcurl\n",
+    " \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2cb9237c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: INFER_ENDPOINT=0.0.0.0:9000\n"
+     ]
+    }
+   ],
+   "source": [
+    "%env INFER_ENDPOINT=0.0.0.0:9000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30431134",
+   "metadata": {},
+   "source": [
+    "### Tensorflow Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "34165288",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple1\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\r\n",
+      "  requirements:\r\n",
+      "  - tensorflow\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "275a8445",
+   "metadata": {},
+   "source": [
+    "Load the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "726f2dd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b141e73",
+   "metadata": {},
+   "source": [
+    "Wait for the model to be ready."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2d9b631f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7abd240a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"tfsimple1_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"OUTPUT0\",\r\n",
+      "\t\t\t\"datatype\": \"INT32\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2,\r\n",
+      "\t\t\t\t4,\r\n",
+      "\t\t\t\t6,\r\n",
+      "\t\t\t\t8,\r\n",
+      "\t\t\t\t10,\r\n",
+      "\t\t\t\t12,\r\n",
+      "\t\t\t\t14,\r\n",
+      "\t\t\t\t16,\r\n",
+      "\t\t\t\t18,\r\n",
+      "\t\t\t\t20,\r\n",
+      "\t\t\t\t22,\r\n",
+      "\t\t\t\t24,\r\n",
+      "\t\t\t\t26,\r\n",
+      "\t\t\t\t28,\r\n",
+      "\t\t\t\t30,\r\n",
+      "\t\t\t\t32\r\n",
+      "\t\t\t]\r\n",
+      "\t\t},\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"OUTPUT1\",\r\n",
+      "\t\t\t\"datatype\": \"INT32\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer tfsimple1 --inference-host ${INFER_ENDPOINT} \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f86c494a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"modelName\":\"tfsimple1_1\",\"modelVersion\":\"1\",\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer tfsimple1 --inference-mode grpc  --inference-host ${INFER_ENDPOINT} \\\n",
+    "    '{\"model_name\":\"tfsimple1\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"intContents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"intContents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f4f1f4b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"model_name\":\"tfsimple1_1\",\"model_version\":\"1\",\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[1,16],\"data\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[1,16],\"data\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}]}"
+     ]
+    }
+   ],
+   "source": [
+    "!curl http://${INFER_ENDPOINT}/v2/models/tfsimple1/infer -H \"Content-Type: application/json\" -H \"seldon-model: tfsimple1\" \\\n",
+    "        -d '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a9471bff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"tfsimple1_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ]\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ]\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"rawOutputContents\": [\r\n",
+      "    \"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\",\r\n",
+      "    \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"model_name\":\"tfsimple1\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../apis \\\n",
+    "    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    -rpc-header seldon-model:tfsimple1 \\\n",
+    "    ${INFER_ENDPOINT} inference.GRPCInferenceService/ModelInfer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3720658f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple1\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "050ed2dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5fc982b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"pipelineName\":\"tfsimple\",\"versions\":[{\"pipeline\":{\"name\":\"tfsimple\",\"uid\":\"cg5fm6c6dpcs73c4qhe0\",\"version\":1,\"steps\":[{\"name\":\"tfsimple1\"}],\"output\":{\"steps\":[\"tfsimple1.outputs\"]},\"kubernetesMeta\":{}},\"state\":{\"pipelineVersion\":1,\"status\":\"PipelineReady\",\"reason\":\"created pipeline\",\"lastChangeTimestamp\":\"2023-03-10T09:40:41.317797761Z\",\"modelsReady\":true}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple -w PipelineReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "42efbedd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"\",\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2,\r\n",
+      "\t\t\t\t4,\r\n",
+      "\t\t\t\t6,\r\n",
+      "\t\t\t\t8,\r\n",
+      "\t\t\t\t10,\r\n",
+      "\t\t\t\t12,\r\n",
+      "\t\t\t\t14,\r\n",
+      "\t\t\t\t16,\r\n",
+      "\t\t\t\t18,\r\n",
+      "\t\t\t\t20,\r\n",
+      "\t\t\t\t22,\r\n",
+      "\t\t\t\t24,\r\n",
+      "\t\t\t\t26,\r\n",
+      "\t\t\t\t28,\r\n",
+      "\t\t\t\t30,\r\n",
+      "\t\t\t\t32\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT0\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t},\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT1\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple  --inference-host ${INFER_ENDPOINT} \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "db8adc42",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple --inference-mode grpc  --inference-host ${INFER_ENDPOINT} \\\n",
+    "    '{\"model_name\":\"tfsimple1\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23957098",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"model_name\":\"\",\"outputs\":[{\"data\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32],\"name\":\"OUTPUT0\",\"shape\":[1,16],\"datatype\":\"INT32\"},{\"data\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\"name\":\"OUTPUT1\",\"shape\":[1,16],\"datatype\":\"INT32\"}]}"
+     ]
+    }
+   ],
+   "source": [
+    "!curl http://${INFER_ENDPOINT}/v2/models/tfsimple1/infer -H \"Content-Type: application/json\" -H \"seldon-model: tfsimple.pipeline\" \\\n",
+    "        -d '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "21d5cfb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ]\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ]\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"rawOutputContents\": [\r\n",
+      "    \"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\",\r\n",
+      "    \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"model_name\":\"tfsimple1\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../apis \\\n",
+    "    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    -rpc-header seldon-model:tfsimple.pipeline \\\n",
+    "    ${INFER_ENDPOINT} inference.GRPCInferenceService/ModelInfer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8ee2b2fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline unload tfsimple\n",
+    "!seldon model unload tfsimple1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9331e253",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/istio.ipynb b/docs/examples/notebooks/istio.ipynb
new file mode 100644
index 0000000000..ed1d4836ec
--- /dev/null
+++ b/docs/examples/notebooks/istio.ipynb
@@ -0,0 +1,568 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ignored-upper",
+   "metadata": {},
+   "source": [
+    "## Istio Notebook Examples\n",
+    "\n",
+    "Assumes\n",
+    "\n",
+    " * You have installed istio as per their docs\n",
+    " * You have exposed the ingressgateway as an external loadbalancer\n",
+    " \n",
+    " \n",
+    "tested with:\n",
+    "\n",
+    "```\n",
+    "istioctl version\n",
+    "1.13.2\n",
+    "\n",
+    "istioctl install --set profile=demo -y\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "arabic-april",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.21.255.1'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "INGRESS_IP=!kubectl get svc istio-ingressgateway -n istio-system -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "INGRESS_IP=INGRESS_IP[0]\n",
+    "import os\n",
+    "os.environ['INGRESS_IP'] = INGRESS_IP\n",
+    "INGRESS_IP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "excessive-garlic",
+   "metadata": {},
+   "source": [
+    "### Istio Single Model Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "present-lightning",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  storageUri: gs://seldon-models/mlserver/iris\r\n",
+      "---\r\n",
+      "apiVersion: networking.istio.io/v1beta1\r\n",
+      "kind: Gateway\r\n",
+      "metadata:\r\n",
+      "  name: seldon-gateway\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  selector:\r\n",
+      "    app: istio-ingressgateway\r\n",
+      "    istio: ingressgateway\r\n",
+      "  servers:\r\n",
+      "  - hosts:\r\n",
+      "    - '*'\r\n",
+      "    port:\r\n",
+      "      name: http\r\n",
+      "      number: 80\r\n",
+      "      protocol: HTTP\r\n",
+      "  - hosts:\r\n",
+      "    - '*'\r\n",
+      "    port:\r\n",
+      "      name: https\r\n",
+      "      number: 443\r\n",
+      "      protocol: HTTPS\r\n",
+      "    tls:\r\n",
+      "      mode: SIMPLE\r\n",
+      "      privateKey: /etc/istio/ingressgateway-certs/tls.key\r\n",
+      "      serverCertificate: /etc/istio/ingressgateway-certs/tls.crt\r\n",
+      "---\r\n",
+      "apiVersion: networking.istio.io/v1beta1\r\n",
+      "kind: VirtualService\r\n",
+      "metadata:\r\n",
+      "  name: iris-route\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  gateways:\r\n",
+      "  - istio-system/seldon-gateway\r\n",
+      "  hosts:\r\n",
+      "  - '*'\r\n",
+      "  http:\r\n",
+      "  - match:\r\n",
+      "    - uri:\r\n",
+      "        prefix: /v2\r\n",
+      "    name: iris-http\r\n",
+      "    route:\r\n",
+      "    - destination:\r\n",
+      "        host: seldon-mesh.seldon-mesh.svc.cluster.local\r\n",
+      "      headers:\r\n",
+      "        request:\r\n",
+      "          set:\r\n",
+      "            seldon-model: iris\r\n",
+      "  - match:\r\n",
+      "    - uri:\r\n",
+      "        prefix: /inference.GRPCInferenceService\r\n",
+      "    name: iris-grpc\r\n",
+      "    route:\r\n",
+      "    - destination:\r\n",
+      "        host: seldon-mesh.seldon-mesh.svc.cluster.local\r\n",
+      "      headers:\r\n",
+      "        request:\r\n",
+      "          set:\r\n",
+      "            seldon-model: iris\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/single-model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "satellite-canadian",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris unchanged\r\n",
+      "gateway.networking.istio.io/seldon-gateway unchanged\r\n",
+      "virtualservice.networking.istio.io/iris-route configured\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/single-model | kubectl apply -f -"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "silent-objective",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "clean-primary",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*   Trying 172.21.255.1...\n",
+      "* Connected to 172.21.255.1 (172.21.255.1) port 80 (#0)\n",
+      "> POST /v2/models/iris/infer HTTP/1.1\n",
+      "> Host: 172.21.255.1\n",
+      "> User-Agent: curl/7.47.0\n",
+      "> Accept: */*\n",
+      "> Content-Type: application/json\n",
+      "> Content-Length: 94\n",
+      "> \n",
+      "* upload completely sent off: 94 out of 94 bytes\n",
+      "< HTTP/1.1 200 OK\n",
+      "< content-length: 196\n",
+      "< content-type: application/json\n",
+      "< date: Sat, 16 Apr 2022 15:34:11 GMT\n",
+      "< server: istio-envoy\n",
+      "< x-envoy-upstream-service-time: 802\n",
+      "< seldon-route: iris_1\n",
+      "< \n",
+      "* Connection #0 to host 172.21.255.1 left intact\n",
+      "{\"model_name\":\"iris_1\",\"model_version\":\"1\",\"id\":\"83520c4a-c7f1-4363-9bfd-60c5d8ee2dc5\",\"parameters\":null,\"outputs\":[{\"name\":\"predict\",\"shape\":[1],\"datatype\":\"INT64\",\"parameters\":null,\"data\":[2]}]}"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -v http://${INGRESS_IP}/v2/models/iris/infer -H \"Content-Type: application/json\" \\\n",
+    "        -d '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "greek-penny",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"iris_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"int64Contents\": [\r\n",
+      "          \"2\"\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"model_name\":\"iris\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../../apis \\\n",
+    "    -proto ../../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    ${INGRESS_IP}:80 inference.GRPCInferenceService/ModelInfer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "durable-profession",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\n",
+      "gateway.networking.istio.io \"seldon-gateway\" deleted\n",
+      "virtualservice.networking.istio.io \"iris-route\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/single-model | kubectl delete -f -"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vulnerable-enterprise",
+   "metadata": {},
+   "source": [
+    "### Traffic Split Two Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "practical-russia",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris1\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  storageUri: gs://seldon-models/mlserver/iris\r\n",
+      "---\r\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris2\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  storageUri: gs://seldon-models/mlserver/iris\r\n",
+      "---\r\n",
+      "apiVersion: networking.istio.io/v1beta1\r\n",
+      "kind: Gateway\r\n",
+      "metadata:\r\n",
+      "  name: seldon-gateway\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  selector:\r\n",
+      "    app: istio-ingressgateway\r\n",
+      "    istio: ingressgateway\r\n",
+      "  servers:\r\n",
+      "  - hosts:\r\n",
+      "    - '*'\r\n",
+      "    port:\r\n",
+      "      name: http\r\n",
+      "      number: 80\r\n",
+      "      protocol: HTTP\r\n",
+      "---\r\n",
+      "apiVersion: networking.istio.io/v1beta1\r\n",
+      "kind: VirtualService\r\n",
+      "metadata:\r\n",
+      "  name: iris-route\r\n",
+      "  namespace: seldon-mesh\r\n",
+      "spec:\r\n",
+      "  gateways:\r\n",
+      "  - seldon-gateway\r\n",
+      "  hosts:\r\n",
+      "  - '*'\r\n",
+      "  http:\r\n",
+      "  - match:\r\n",
+      "    - uri:\r\n",
+      "        prefix: /v2/models/iris\r\n",
+      "    name: iris-http\r\n",
+      "    route:\r\n",
+      "    - destination:\r\n",
+      "        host: seldon-mesh.seldon-mesh.svc.cluster.local\r\n",
+      "      headers:\r\n",
+      "        request:\r\n",
+      "          set:\r\n",
+      "            seldon-model: iris1\r\n",
+      "      weight: 50\r\n",
+      "    - destination:\r\n",
+      "        host: seldon-mesh.seldon-mesh.svc.cluster.local\r\n",
+      "      headers:\r\n",
+      "        request:\r\n",
+      "          set:\r\n",
+      "            seldon-model: iris2\r\n",
+      "      weight: 50\r\n",
+      "  - match:\r\n",
+      "    - uri:\r\n",
+      "        prefix: /inference.GRPCInferenceService\r\n",
+      "    name: iris-grpc\r\n",
+      "    route:\r\n",
+      "    - destination:\r\n",
+      "        host: seldon-mesh.seldon-mesh.svc.cluster.local\r\n",
+      "      headers:\r\n",
+      "        request:\r\n",
+      "          set:\r\n",
+      "            seldon-model: iris1\r\n",
+      "      weight: 50\r\n",
+      "    - destination:\r\n",
+      "        host: seldon-mesh.seldon-mesh.svc.cluster.local\r\n",
+      "      headers:\r\n",
+      "        request:\r\n",
+      "          set:\r\n",
+      "            seldon-model: iris2\r\n",
+      "      weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/traffic-split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "meaningful-toilet",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris1 created\n",
+      "model.mlops.seldon.io/iris2 created\n",
+      "gateway.networking.istio.io/seldon-gateway created\n",
+      "virtualservice.networking.istio.io/iris-route created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/traffic-split | kubectl apply -f -"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "sophisticated-junior",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris1 condition met\r\n",
+      "model.mlops.seldon.io/iris2 condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "worthy-donna",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*   Trying 172.21.255.1...\n",
+      "* Connected to 172.21.255.1 (172.21.255.1) port 80 (#0)\n",
+      "> POST /v2/models/iris/infer HTTP/1.1\n",
+      "> Host: 172.21.255.1\n",
+      "> User-Agent: curl/7.47.0\n",
+      "> Accept: */*\n",
+      "> Content-Type: application/json\n",
+      "> Content-Length: 94\n",
+      "> \n",
+      "* upload completely sent off: 94 out of 94 bytes\n",
+      "< HTTP/1.1 200 OK\n",
+      "< content-length: 197\n",
+      "< content-type: application/json\n",
+      "< date: Sat, 16 Apr 2022 15:35:01 GMT\n",
+      "< server: istio-envoy\n",
+      "< x-envoy-upstream-service-time: 801\n",
+      "< seldon-route: iris1_1\n",
+      "< \n",
+      "* Connection #0 to host 172.21.255.1 left intact\n",
+      "{\"model_name\":\"iris1_1\",\"model_version\":\"1\",\"id\":\"b54e6d8c-d253-4bb9-bb64-02c2ee49e89f\",\"parameters\":null,\"outputs\":[{\"name\":\"predict\",\"shape\":[1],\"datatype\":\"INT64\",\"parameters\":null,\"data\":[2]}]}"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -v http://${INGRESS_IP}/v2/models/iris/infer -H \"Content-Type: application/json\" \\\n",
+    "        -d '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "extensive-importance",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"iris1_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"int64Contents\": [\r\n",
+      "          \"2\"\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"model_name\":\"iris1\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../../apis \\\n",
+    "    -proto ../../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    ${INGRESS_IP}:80 inference.GRPCInferenceService/ModelInfer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "specific-american",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris1\" deleted\r\n",
+      "model.mlops.seldon.io \"iris2\" deleted\r\n",
+      "gateway.networking.istio.io \"seldon-gateway\" deleted\r\n",
+      "virtualservice.networking.istio.io \"iris-route\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kustomize build config/traffic-split | kubectl delete -f -"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de7c89f6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/k8s-batch-examples.ipynb b/docs/examples/notebooks/k8s-batch-examples.ipynb
new file mode 100644
index 0000000000..e53ae9f57d
--- /dev/null
+++ b/docs/examples/notebooks/k8s-batch-examples.ipynb
@@ -0,0 +1,990 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3b70cb38-a0a0-4f0a-9d77-eafa92301338",
+   "metadata": {},
+   "source": [
+    "### Seldon V2 Batch Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3d069ec2-50a0-4fd3-b65e-d178cb7b142d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"NAMESPACE\"] = \"seldon-mesh\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "93d79639-2651-4b83-bbcf-404e772f1f3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.18.255.2'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP'] = MESH_IP\n",
+    "MESH_IP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1098488-4929-4df4-b3f9-8e448e87bcec",
+   "metadata": {},
+   "source": [
+    "## Deploy Models and Pipelines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "77169ee9-896f-4ee3-b154-b79997d71925",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat models/sklearn-iris-gs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e5653b7f-b8f5-4f08-ac2c-a26775ddf027",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: iris-pipeline\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: iris\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - iris\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat pipelines/iris.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3c51fc8b-3164-4e1a-8616-452f62d61d22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple1\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\r\n",
+      "  requirements:\r\n",
+      "  - tensorflow\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat models/tfsimple1.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "992a0404-e1d6-4d55-a963-2cdebd42231c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple1\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b4f7affb-5418-4209-b665-4478482ea3dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\n",
+      "pipeline.mlops.seldon.io/iris-pipeline created\n",
+      "model.mlops.seldon.io/tfsimple1 created\n",
+      "pipeline.mlops.seldon.io/tfsimple created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl apply -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}\n",
+    "!kubectl apply -f pipelines/iris.yaml -n ${NAMESPACE}\n",
+    "\n",
+    "!kubectl apply -f models/tfsimple1.yaml -n ${NAMESPACE}\n",
+    "!kubectl apply -f pipelines/tfsimple.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "66b6b1f3-08bc-4212-be47-9a1df0b18348",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\n",
+      "model.mlops.seldon.io/tfsimple1 condition met\n",
+      "pipeline.mlops.seldon.io/iris-pipeline condition met\n",
+      "pipeline.mlops.seldon.io/tfsimple condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}\n",
+    "!kubectl wait --for condition=ready --timeout=300s pipelines --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c8e501b-fd01-47f6-a211-7de81942d754",
+   "metadata": {},
+   "source": [
+    "## Test Predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5b2678d3-a5cb-4b41-ad5b-3f29a7a1202d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"iris_1\",\r\n",
+      "  \"model_version\": \"1\",\r\n",
+      "  \"id\": \"25e1c1b9-a20f-456d-bdff-c75d5ba83b1f\",\r\n",
+      "  \"parameters\": {},\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        1\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"parameters\": {\r\n",
+      "        \"content_type\": \"np\"\r\n",
+      "      },\r\n",
+      "      \"data\": [\r\n",
+      "        2\r\n",
+      "      ]\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-host ${MESH_IP}:80 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "31701fb2-1185-415c-a0f8-b05ab89ae2d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        2\r\n",
+      "      ],\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        1\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"parameters\": {\r\n",
+      "        \"content_type\": \"np\"\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer iris-pipeline --inference-host ${MESH_IP}:80 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' |  jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e7fd9ec3-fd25-48ad-bf5d-90231805cd01",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"tfsimple1_1\",\r\n",
+      "  \"model_version\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"data\": [\r\n",
+      "        2,\r\n",
+      "        4,\r\n",
+      "        6,\r\n",
+      "        8,\r\n",
+      "        10,\r\n",
+      "        12,\r\n",
+      "        14,\r\n",
+      "        16,\r\n",
+      "        18,\r\n",
+      "        20,\r\n",
+      "        22,\r\n",
+      "        24,\r\n",
+      "        26,\r\n",
+      "        28,\r\n",
+      "        30,\r\n",
+      "        32\r\n",
+      "      ]\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer tfsimple1 --inference-host ${MESH_IP}:80 \\\n",
+    "  '{\"outputs\":[{\"name\":\"OUTPUT0\"}], \"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9473915b-0ec4-457c-b304-1ba50e9cd78d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        2,\r\n",
+      "        4,\r\n",
+      "        6,\r\n",
+      "        8,\r\n",
+      "        10,\r\n",
+      "        12,\r\n",
+      "        14,\r\n",
+      "        16,\r\n",
+      "        18,\r\n",
+      "        20,\r\n",
+      "        22,\r\n",
+      "        24,\r\n",
+      "        26,\r\n",
+      "        28,\r\n",
+      "        30,\r\n",
+      "        32\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple --inference-host ${MESH_IP}:80 \\\n",
+    "  '{\"outputs\":[{\"name\":\"OUTPUT0\"}], \"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aecd8d62-2802-4e1d-bd1e-78978f3d4972",
+   "metadata": {},
+   "source": [
+    "## MLServer Iris Batch Job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9e9bee26-3c1c-44b0-9889-587767cf933e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"inputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"data\": [\r\n",
+      "        0.38606369295833043,\r\n",
+      "        0.006894049558299753,\r\n",
+      "        0.6104082981607108,\r\n",
+      "        0.3958954239450676\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"FP64\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        4\r\n",
+      "      ]\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat batch-inputs/iris-input.txt | head -n 1 | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a3b60a8f-80b5-4803-82b9-9d68605480d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-06-30 11:05:32,389 [mlserver] INFO - server url: 172.18.255.2\n",
+      "2023-06-30 11:05:32,389 [mlserver] INFO - model name: iris\n",
+      "2023-06-30 11:05:32,389 [mlserver] INFO - request headers: {}\n",
+      "2023-06-30 11:05:32,389 [mlserver] INFO - input file path: batch-inputs/iris-input.txt\n",
+      "2023-06-30 11:05:32,389 [mlserver] INFO - output file path: /tmp/iris-output.txt\n",
+      "2023-06-30 11:05:32,389 [mlserver] INFO - workers: 5\n",
+      "2023-06-30 11:05:32,389 [mlserver] INFO - retries: 3\n",
+      "2023-06-30 11:05:32,389 [mlserver] INFO - batch interval: 0.0\n",
+      "2023-06-30 11:05:32,389 [mlserver] INFO - batch jitter: 0.0\n",
+      "2023-06-30 11:05:32,389 [mlserver] INFO - connection timeout: 60\n",
+      "2023-06-30 11:05:32,389 [mlserver] INFO - micro-batch size: 1\n",
+      "2023-06-30 11:05:32,503 [mlserver] INFO - Finalizer: processed instances: 100\n",
+      "2023-06-30 11:05:32,503 [mlserver] INFO - Total processed instances: 100\n",
+      "2023-06-30 11:05:32,503 [mlserver] INFO - Time taken: 0.11 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "mlserver infer -u ${MESH_IP} -m iris -i batch-inputs/iris-input.txt -o /tmp/iris-output.txt --workers 5 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d3b7ef85-6435-4041-9857-2f843091118a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-06-30 11:05:35,857 [mlserver] INFO - server url: 172.18.255.2\n",
+      "2023-06-30 11:05:35,858 [mlserver] INFO - model name: iris-pipeline.pipeline\n",
+      "2023-06-30 11:05:35,858 [mlserver] INFO - request headers: {}\n",
+      "2023-06-30 11:05:35,858 [mlserver] INFO - input file path: batch-inputs/iris-input.txt\n",
+      "2023-06-30 11:05:35,858 [mlserver] INFO - output file path: /tmp/iris-pipeline-output.txt\n",
+      "2023-06-30 11:05:35,858 [mlserver] INFO - workers: 5\n",
+      "2023-06-30 11:05:35,858 [mlserver] INFO - retries: 3\n",
+      "2023-06-30 11:05:35,858 [mlserver] INFO - batch interval: 0.0\n",
+      "2023-06-30 11:05:35,858 [mlserver] INFO - batch jitter: 0.0\n",
+      "2023-06-30 11:05:35,858 [mlserver] INFO - connection timeout: 60\n",
+      "2023-06-30 11:05:35,858 [mlserver] INFO - micro-batch size: 1\n",
+      "2023-06-30 11:05:36,145 [mlserver] INFO - Finalizer: processed instances: 100\n",
+      "2023-06-30 11:05:36,146 [mlserver] INFO - Total processed instances: 100\n",
+      "2023-06-30 11:05:36,146 [mlserver] INFO - Time taken: 0.29 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "mlserver infer -u ${MESH_IP} -m iris-pipeline.pipeline -i batch-inputs/iris-input.txt -o /tmp/iris-pipeline-output.txt --workers 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2fd223f2-9b91-47c2-99a6-e0f16879b44e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"iris_1\",\r\n",
+      "  \"model_version\": \"1\",\r\n",
+      "  \"id\": \"46bdfca2-8805-4a72-b1ce-95e4f38c1a19\",\r\n",
+      "  \"parameters\": {\r\n",
+      "    \"inference_id\": \"46bdfca2-8805-4a72-b1ce-95e4f38c1a19\",\r\n",
+      "    \"batch_index\": 0\r\n",
+      "  },\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        1\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"parameters\": {\r\n",
+      "        \"content_type\": \"np\"\r\n",
+      "      },\r\n",
+      "      \"data\": [\r\n",
+      "        1\r\n",
+      "      ]\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat /tmp/iris-output.txt | head -n 1 | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c14358ae-6206-4f67-84e6-2baa6a54c7db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;39m{\r\n",
+      "  \u001b[0m\u001b[34;1m\"model_name\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"\"\u001b[0m\u001b[1;39m,\r\n",
+      "  \u001b[0m\u001b[34;1m\"id\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"37e8c013-b348-41e8-89b9-fea86a4f9632\"\u001b[0m\u001b[1;39m,\r\n",
+      "  \u001b[0m\u001b[34;1m\"parameters\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\r\n",
+      "    \u001b[0m\u001b[34;1m\"batch_index\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39m1\u001b[0m\u001b[1;39m\r\n",
+      "  \u001b[1;39m}\u001b[0m\u001b[1;39m,\r\n",
+      "  \u001b[0m\u001b[34;1m\"outputs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "    \u001b[1;39m{\r\n",
+      "      \u001b[0m\u001b[34;1m\"name\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"predict\"\u001b[0m\u001b[1;39m,\r\n",
+      "      \u001b[0m\u001b[34;1m\"shape\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "        \u001b[0;39m1\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0;39m1\u001b[0m\u001b[1;39m\r\n",
+      "      \u001b[1;39m]\u001b[0m\u001b[1;39m,\r\n",
+      "      \u001b[0m\u001b[34;1m\"datatype\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"INT64\"\u001b[0m\u001b[1;39m,\r\n",
+      "      \u001b[0m\u001b[34;1m\"parameters\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\r\n",
+      "        \u001b[0m\u001b[34;1m\"content_type\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"np\"\u001b[0m\u001b[1;39m\r\n",
+      "      \u001b[1;39m}\u001b[0m\u001b[1;39m,\r\n",
+      "      \u001b[0m\u001b[34;1m\"data\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "        \u001b[0;39m1\u001b[0m\u001b[1;39m\r\n",
+      "      \u001b[1;39m]\u001b[0m\u001b[1;39m\r\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\r\n",
+      "  \u001b[1;39m]\u001b[0m\u001b[1;39m\r\n",
+      "\u001b[1;39m}\u001b[0m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat /tmp/iris-pipeline-output.txt | head -n 1 | jq ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b881fc4f-6e57-4164-9d8d-d174662a43ec",
+   "metadata": {},
+   "source": [
+    "## Triton TFSimple Batch Job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5ae725ef-bf07-42f7-8c57-d4e99ae99a87",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"inputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"INPUT0\",\r\n",
+      "      \"data\": [\r\n",
+      "        75,\r\n",
+      "        39,\r\n",
+      "        9,\r\n",
+      "        44,\r\n",
+      "        32,\r\n",
+      "        97,\r\n",
+      "        99,\r\n",
+      "        40,\r\n",
+      "        13,\r\n",
+      "        27,\r\n",
+      "        25,\r\n",
+      "        36,\r\n",
+      "        18,\r\n",
+      "        77,\r\n",
+      "        62,\r\n",
+      "        60\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ]\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"INPUT1\",\r\n",
+      "      \"data\": [\r\n",
+      "        39,\r\n",
+      "        7,\r\n",
+      "        14,\r\n",
+      "        58,\r\n",
+      "        13,\r\n",
+      "        88,\r\n",
+      "        98,\r\n",
+      "        66,\r\n",
+      "        97,\r\n",
+      "        57,\r\n",
+      "        49,\r\n",
+      "        3,\r\n",
+      "        49,\r\n",
+      "        63,\r\n",
+      "        37,\r\n",
+      "        12\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ]\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat batch-inputs/tfsimple-input.txt | head -n 1 | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5c5f360a-0c2c-4759-b30d-6e907aa5c75e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-06-30 11:22:52,662 [mlserver] INFO - server url: 172.18.255.2\n",
+      "2023-06-30 11:22:52,662 [mlserver] INFO - model name: tfsimple1\n",
+      "2023-06-30 11:22:52,662 [mlserver] INFO - request headers: {}\n",
+      "2023-06-30 11:22:52,662 [mlserver] INFO - input file path: batch-inputs/tfsimple-input.txt\n",
+      "2023-06-30 11:22:52,662 [mlserver] INFO - output file path: /tmp/tfsimple-output.txt\n",
+      "2023-06-30 11:22:52,662 [mlserver] INFO - workers: 5\n",
+      "2023-06-30 11:22:52,662 [mlserver] INFO - retries: 3\n",
+      "2023-06-30 11:22:52,662 [mlserver] INFO - batch interval: 0.0\n",
+      "2023-06-30 11:22:52,662 [mlserver] INFO - batch jitter: 0.0\n",
+      "2023-06-30 11:22:52,662 [mlserver] INFO - connection timeout: 60\n",
+      "2023-06-30 11:22:52,662 [mlserver] INFO - micro-batch size: 1\n",
+      "2023-06-30 11:22:52,755 [mlserver] INFO - Finalizer: processed instances: 100\n",
+      "2023-06-30 11:22:52,755 [mlserver] INFO - Total processed instances: 100\n",
+      "2023-06-30 11:22:52,756 [mlserver] INFO - Time taken: 0.09 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "mlserver infer -u ${MESH_IP} -m tfsimple1 -i batch-inputs/tfsimple-input.txt -o /tmp/tfsimple-output.txt --workers 5 -b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "753bc0ad-2fa0-47d8-a5cc-428cba326806",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2023-06-30 11:22:54,065 [mlserver] INFO - server url: 172.18.255.2\n",
+      "2023-06-30 11:22:54,065 [mlserver] INFO - model name: tfsimple.pipeline\n",
+      "2023-06-30 11:22:54,065 [mlserver] INFO - request headers: {}\n",
+      "2023-06-30 11:22:54,065 [mlserver] INFO - input file path: batch-inputs/tfsimple-input.txt\n",
+      "2023-06-30 11:22:54,065 [mlserver] INFO - output file path: /tmp/tfsimple-pipeline-output.txt\n",
+      "2023-06-30 11:22:54,065 [mlserver] INFO - workers: 5\n",
+      "2023-06-30 11:22:54,065 [mlserver] INFO - retries: 3\n",
+      "2023-06-30 11:22:54,065 [mlserver] INFO - batch interval: 0.0\n",
+      "2023-06-30 11:22:54,065 [mlserver] INFO - batch jitter: 0.0\n",
+      "2023-06-30 11:22:54,065 [mlserver] INFO - connection timeout: 60\n",
+      "2023-06-30 11:22:54,065 [mlserver] INFO - micro-batch size: 1\n",
+      "2023-06-30 11:22:54,302 [mlserver] INFO - Finalizer: processed instances: 100\n",
+      "2023-06-30 11:22:54,302 [mlserver] INFO - Total processed instances: 100\n",
+      "2023-06-30 11:22:54,303 [mlserver] INFO - Time taken: 0.24 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "mlserver infer -u ${MESH_IP} -m tfsimple.pipeline -i batch-inputs/tfsimple-input.txt -o /tmp/tfsimple-pipeline-output.txt --workers 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0f8a3641-c8a3-41c5-af4b-2647655fb10d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"tfsimple1_1\",\r\n",
+      "  \"model_version\": \"1\",\r\n",
+      "  \"id\": \"19952272-b023-4079-aa08-f1880ded05e5\",\r\n",
+      "  \"parameters\": {\r\n",
+      "    \"inference_id\": \"19952272-b023-4079-aa08-f1880ded05e5\",\r\n",
+      "    \"batch_index\": 1\r\n",
+      "  },\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"parameters\": {},\r\n",
+      "      \"data\": [\r\n",
+      "        115,\r\n",
+      "        69,\r\n",
+      "        97,\r\n",
+      "        112,\r\n",
+      "        73,\r\n",
+      "        106,\r\n",
+      "        58,\r\n",
+      "        182,\r\n",
+      "        114,\r\n",
+      "        66,\r\n",
+      "        64,\r\n",
+      "        110,\r\n",
+      "        100,\r\n",
+      "        24,\r\n",
+      "        22,\r\n",
+      "        77\r\n",
+      "      ]\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"parameters\": {},\r\n",
+      "      \"data\": [\r\n",
+      "        -77,\r\n",
+      "        33,\r\n",
+      "        25,\r\n",
+      "        -52,\r\n",
+      "        -49,\r\n",
+      "        -88,\r\n",
+      "        -48,\r\n",
+      "        0,\r\n",
+      "        -50,\r\n",
+      "        26,\r\n",
+      "        -44,\r\n",
+      "        46,\r\n",
+      "        -2,\r\n",
+      "        18,\r\n",
+      "        -6,\r\n",
+      "        -47\r\n",
+      "      ]\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat /tmp/tfsimple-output.txt | head -n 1 | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d17f03fa-08bc-42aa-95e8-8427e609e045",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"\",\r\n",
+      "  \"id\": \"46b05aab-07d9-414d-be96-c03d1863552a\",\r\n",
+      "  \"parameters\": {\r\n",
+      "    \"batch_index\": 3\r\n",
+      "  },\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"data\": [\r\n",
+      "        140,\r\n",
+      "        164,\r\n",
+      "        85,\r\n",
+      "        58,\r\n",
+      "        152,\r\n",
+      "        76,\r\n",
+      "        70,\r\n",
+      "        56,\r\n",
+      "        100,\r\n",
+      "        141,\r\n",
+      "        98,\r\n",
+      "        181,\r\n",
+      "        115,\r\n",
+      "        177,\r\n",
+      "        106,\r\n",
+      "        193\r\n",
+      "      ]\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"data\": [\r\n",
+      "        -10,\r\n",
+      "        0,\r\n",
+      "        -11,\r\n",
+      "        -38,\r\n",
+      "        2,\r\n",
+      "        -36,\r\n",
+      "        -52,\r\n",
+      "        -8,\r\n",
+      "        -18,\r\n",
+      "        57,\r\n",
+      "        94,\r\n",
+      "        -5,\r\n",
+      "        -27,\r\n",
+      "        17,\r\n",
+      "        58,\r\n",
+      "        -1\r\n",
+      "      ]\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat /tmp/tfsimple-pipeline-output.txt | head -n 1 | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfd8d138-4825-4a06-a395-bfe332dbfb8e",
+   "metadata": {},
+   "source": [
+    "## Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b8d20578-5e4a-445a-a947-f09c8327033c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\n",
+      "pipeline.mlops.seldon.io \"iris-pipeline\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f pipelines/iris.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5de7287b-577f-409f-af56-03aac9ec1f2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"tfsimple1\" deleted\n",
+      "pipeline.mlops.seldon.io \"tfsimple\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f models/tfsimple1.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f pipelines/tfsimple.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91bcec77",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/k8s-clusterwide-kafka-mtls-demo.ipynb b/docs/examples/notebooks/k8s-clusterwide-kafka-mtls-demo.ipynb
new file mode 100644
index 0000000000..4c3273d9af
--- /dev/null
+++ b/docs/examples/notebooks/k8s-clusterwide-kafka-mtls-demo.ipynb
@@ -0,0 +1,932 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6ebca60b",
+   "metadata": {},
+   "source": [
+    "## Kubernetes Clusterwide Kafka mTLS Demo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba016c14",
+   "metadata": {},
+   "source": [
+    "### Setup\n",
+    "\n",
+    "Create a Kind cluster by using an ansible playbook from the project `ansible` folder.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bc29bc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../ansible && ansible-playbook playbooks/kind-cluster.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b12607ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../ansible && ansible-playbook playbooks/setup-ecosystem.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "b4d93216",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-core-v2-crds\" has been upgraded. Happy Helming!\r\n",
+      "NAME: seldon-core-v2-crds\r\n",
+      "LAST DEPLOYED: Thu Jun 22 15:22:04 2023\r\n",
+      "NAMESPACE: seldon-mesh\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 5\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-core-v2-crds  ../k8s/helm-charts/seldon-core-v2-crds -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "bcf13333",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "namespace/ns1 created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create namespace ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "edba01cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "namespace/ns2 created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create namespace ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "6a9cdb85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---\r\n",
+      "kafka:\r\n",
+      "  bootstrap: seldon-kafka-bootstrap.seldon-mesh.svc.cluster.local:9093\r\n",
+      "\r\n",
+      "security:\r\n",
+      "  kafka:\r\n",
+      "    protocol: SSL\r\n",
+      "    ssl:\r\n",
+      "      client:\r\n",
+      "        secret: seldon\r\n",
+      "        brokerValidationSecret: seldon-cluster-ca-cert\r\n",
+      "        keyPath: /tmp/certs/kafka/client/user.key\r\n",
+      "        crtPath: /tmp/certs/kafka/client/user.crt\r\n",
+      "        caPath: /tmp/certs/kafka/client/ca.crt\r\n",
+      "        brokerCaPath: /tmp/certs/kafka/broker/ca.crt\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ../k8s/samples/values-strimzi-kafka-mtls.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "71bf922e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2\r\n",
+      "LAST DEPLOYED: Thu Jun 22 15:22:11 2023\r\n",
+      "NAMESPACE: seldon-mesh\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2 ../k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh --set controller.clusterwide=true --values ../k8s/samples/values-strimzi-kafka-mtls.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67ac3da4",
+   "metadata": {},
+   "source": [
+    "## Copy Strimzi secrets to namespaces\n",
+    "\n",
+    "Strimzi doesn't allow a single Kafka cluster to be used in TLS easily from multiple namespaces without copying the user Secrets created by the KafkaUser to those namespaces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "fd76efe2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "secret/seldon created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get secret seldon -n seldon-mesh -o json | jq 'del(.metadata.ownerReferences) | del(.metadata.namespace)' | kubectl create -f - -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "b3e2dbee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "secret/seldon-cluster-ca-cert created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get secret seldon-cluster-ca-cert -n seldon-mesh -o json | jq 'del(.metadata.ownerReferences) | del(.metadata.namespace)' | kubectl create -f - -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "96872dd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "secret/seldon created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get secret seldon -n seldon-mesh -o json | jq 'del(.metadata.ownerReferences) | del(.metadata.namespace)' | kubectl create -f - -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "24ce240c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "secret/seldon-cluster-ca-cert created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get secret seldon-cluster-ca-cert -n seldon-mesh -o json | jq 'del(.metadata.ownerReferences) | del(.metadata.namespace)' | kubectl create -f - -n ns2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93be7623",
+   "metadata": {},
+   "source": [
+    "## Create SeldonRuntimes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "e28147b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-runtime\r\n",
+      "LAST DEPLOYED: Thu Jun 22 15:22:19 2023\r\n",
+      "NAMESPACE: ns1\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-runtime ../k8s/helm-charts/seldon-core-v2-runtime  -n ns1 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "4423b5f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-runtime\r\n",
+      "LAST DEPLOYED: Thu Jun 22 15:22:20 2023\r\n",
+      "NAMESPACE: ns2\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-runtime ../k8s/helm-charts/seldon-core-v2-runtime  -n ns2 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "21e2dd25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-servers\r\n",
+      "LAST DEPLOYED: Thu Jun 22 15:22:23 2023\r\n",
+      "NAMESPACE: ns1\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n ns1 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "56a28fc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-servers\r\n",
+      "LAST DEPLOYED: Thu Jun 22 15:22:24 2023\r\n",
+      "NAMESPACE: ns2\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n ns2 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "1dd6a5dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver condition met\n",
+      "server.mlops.seldon.io/triton condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s server --all -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "e2464284",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver condition met\n",
+      "server.mlops.seldon.io/triton condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s server --all -n ns2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d05ee2b",
+   "metadata": {},
+   "source": [
+    "### Get Inference Endpoints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "3835b3dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.21.255.2'"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ns1 -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP_NS1=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP_NS1'] = MESH_IP_NS1\n",
+    "MESH_IP_NS1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "id": "33f7a263",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.21.255.4'"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ns2 -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP_NS2=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP_NS2'] = MESH_IP_NS2\n",
+    "MESH_IP_NS2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2243a193",
+   "metadata": {},
+   "source": [
+    "### Launch Pipeline in Namespace ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "fc5fdee5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 created\n",
+      "model.mlops.seldon.io/tfsimple2 created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/tfsimple1.yaml -n ns1\n",
+    "!kubectl create -f ./models/tfsimple2.yaml -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "id": "5c1328c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 condition met\n",
+      "model.mlops.seldon.io/tfsimple2 condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "d8d6a6dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/tfsimples created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./pipelines/tfsimples.yaml -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "f0861650",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/tfsimples condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s pipeline --all -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "id": "4e5f2e9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples --inference-mode grpc --inference-host ${MESH_IP_NS1}:80 \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53ea1e35",
+   "metadata": {},
+   "source": [
+    "### Launch Pipeline in Namespace ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "id": "88954ff6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 created\n",
+      "model.mlops.seldon.io/tfsimple2 created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/tfsimple1.yaml -n ns2\n",
+    "!kubectl create -f ./models/tfsimple2.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "id": "447ed372",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 condition met\n",
+      "model.mlops.seldon.io/tfsimple2 condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "id": "95d62d6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/tfsimples created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./pipelines/tfsimples.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "id": "8e8ebee6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/tfsimples condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s pipeline --all -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "1ce5d740",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples --inference-mode grpc --inference-host ${MESH_IP_NS2}:80 \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2177c110",
+   "metadata": {},
+   "source": [
+    "## TearDown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "id": "11d99aac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io \"tfsimples\" deleted\n",
+      "pipeline.mlops.seldon.io \"tfsimples\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple1\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple2\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple1\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple2\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./pipelines/tfsimples.yaml -n ns1\n",
+    "!kubectl delete -f ./pipelines/tfsimples.yaml -n ns2\n",
+    "!kubectl delete -f ./models/tfsimple1.yaml -n ns1\n",
+    "!kubectl delete -f ./models/tfsimple2.yaml -n ns1\n",
+    "!kubectl delete -f ./models/tfsimple1.yaml -n ns2\n",
+    "!kubectl delete -f ./models/tfsimple2.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "7ab5b2e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2-servers\" uninstalled\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2-servers -n ns1 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "id": "3a84da13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2-runtime\" uninstalled\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2-runtime -n ns1 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "d77a5ae9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2-servers\" uninstalled\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2-servers -n ns2 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "a5e6e07c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2-runtime\" uninstalled\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2-runtime -n ns2 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "4b13fb4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2\" uninstalled\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2  -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "id": "f06f0dac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "namespace \"ns1\" deleted\n",
+      "namespace \"ns2\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete namespace ns1\n",
+    "!kubectl delete namespace ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "555788e7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/k8s-clusterwide-ssl-demo.ipynb b/docs/examples/notebooks/k8s-clusterwide-ssl-demo.ipynb
new file mode 100644
index 0000000000..4bb7686bda
--- /dev/null
+++ b/docs/examples/notebooks/k8s-clusterwide-ssl-demo.ipynb
@@ -0,0 +1,762 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6ebca60b",
+   "metadata": {},
+   "source": [
+    "## Kubernetes Clusterwide SSL Demo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba016c14",
+   "metadata": {},
+   "source": [
+    "### Setup\n",
+    "\n",
+    "Create a Kind cluster by using an ansible playbook from the project `ansible` folder.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bc29bc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../ansible && ansible-playbook playbooks/kind-cluster.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b12607ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cd ../ansible && ansible-playbook playbooks/setup-ecosystem.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b4d93216",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-core-v2-crds\" has been upgraded. Happy Helming!\r\n",
+      "NAME: seldon-core-v2-crds\r\n",
+      "LAST DEPLOYED: Thu Jun 22 09:11:26 2023\r\n",
+      "NAMESPACE: seldon-mesh\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 2\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-core-v2-crds  ../k8s/helm-charts/seldon-core-v2-crds -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bcf13333",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "namespace/ns1 created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create namespace ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "edba01cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "namespace/ns2 created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create namespace ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "422fce1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-certs\r\n",
+      "LAST DEPLOYED: Thu Jun 22 10:00:32 2023\r\n",
+      "NAMESPACE: ns1\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-certs ../k8s/helm-charts/seldon-core-v2-certs/ -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df456290",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-certs\r\n",
+      "LAST DEPLOYED: Thu Jun 22 09:11:36 2023\r\n",
+      "NAMESPACE: ns2\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-certs ../k8s/helm-charts/seldon-core-v2-certs/ -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6a9cdb85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "security:\r\n",
+      "  controlplane:\r\n",
+      "    protocol: SSL\r\n",
+      "    ssl:\r\n",
+      "      server:\r\n",
+      "        secret: seldon-controlplane-server\r\n",
+      "        clientValidationSecret: seldon-controlplane-client\r\n",
+      "      client:\r\n",
+      "        secret: seldon-controlplane-client\r\n",
+      "        serverValidationSecret: seldon-controlplane-server\r\n",
+      "  envoy:\r\n",
+      "    protocol: SSL\r\n",
+      "    ssl:\r\n",
+      "      upstream:\r\n",
+      "        server:\r\n",
+      "          secret: seldon-upstream-server\r\n",
+      "          clientValidationSecret: seldon-upstream-client\r\n",
+      "        client:\r\n",
+      "          secret: seldon-upstream-client\r\n",
+      "          serverValidationSecret: seldon-upstream-server\r\n",
+      "      downstream:\r\n",
+      "        client:\r\n",
+      "          serverValidationSecret: seldon-downstream-server\r\n",
+      "        server: \r\n",
+      "          secret: seldon-downstream-server\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ../k8s/samples/values-tls-dataplane-controlplane-example.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "71bf922e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2\r\n",
+      "LAST DEPLOYED: Thu Jun 22 09:11:40 2023\r\n",
+      "NAMESPACE: seldon-mesh\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2 ../k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh --set controller.clusterwide=true --values ../k8s/samples/values-tls-dataplane-controlplane-example.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e28147b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-runtime\r\n",
+      "LAST DEPLOYED: Thu Jun 22 10:00:36 2023\r\n",
+      "NAMESPACE: ns1\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-runtime ../k8s/helm-charts/seldon-core-v2-runtime  -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "2864216b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-servers\r\n",
+      "LAST DEPLOYED: Thu Jun 22 10:00:36 2023\r\n",
+      "NAMESPACE: ns1\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4423b5f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-runtime\r\n",
+      "LAST DEPLOYED: Thu Jun 22 09:11:49 2023\r\n",
+      "NAMESPACE: ns2\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-runtime ../k8s/helm-charts/seldon-core-v2-runtime  -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "477c43a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-servers\r\n",
+      "LAST DEPLOYED: Thu Jun 22 09:12:16 2023\r\n",
+      "NAMESPACE: ns2\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5f781131",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver condition met\n",
+      "server.mlops.seldon.io/triton condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s server --all -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "af734add",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver condition met\n",
+      "server.mlops.seldon.io/triton condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s server --all -n ns2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d05ee2b",
+   "metadata": {},
+   "source": [
+    "### Setup TLS Config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3835b3dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.21.255.2'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ns1 -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP_NS1=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP_NS1'] = MESH_IP_NS1\n",
+    "MESH_IP_NS1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "33f7a263",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.21.255.4'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ns2 -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP_NS2=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP_NS2'] = MESH_IP_NS2\n",
+    "MESH_IP_NS2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "181faf47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon config add tls ${PWD}/config-dataplane-tls.json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5db513b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon config activate tls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2243a193",
+   "metadata": {},
+   "source": [
+    "### Launch model in namespace ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fc5fdee5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/sklearn-iris-gs.yaml -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5c1328c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "d048aa51",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"77f21360-b6cf-4c50-a000-1f219f5e9e29\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-host ${MESH_IP_NS1}:80 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53ea1e35",
+   "metadata": {},
+   "source": [
+    "### Launch model in namespace ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5e2e72aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/sklearn-iris-gs.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "6cb7f562",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "4f116773",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"902b8483-0ede-4136-97be-dbf266f05a0c\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-host ${MESH_IP_NS2}:80 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2177c110",
+   "metadata": {},
+   "source": [
+    "## TearDown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "11d99aac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/sklearn-iris-gs.yaml -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "d91cd84a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/sklearn-iris-gs.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "3a84da13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2-servers\" uninstalled\n",
+      "release \"seldon-v2-runtime\" uninstalled\n",
+      "release \"seldon-v2-certs\" uninstalled\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2-servers -n ns1\n",
+    "!helm delete seldon-v2-runtime -n ns1\n",
+    "!helm delete seldon-v2-certs -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "a5e6e07c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2-runtime\" uninstalled\n",
+      "Error: uninstall: Release not loaded: seldon-v2-runtime: release: not found\n",
+      "release \"seldon-v2-certs\" uninstalled\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2-runtime -n ns2\n",
+    "!helm delete seldon-v2-runtime -n ns2\n",
+    "!helm delete seldon-v2-certs -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "4b13fb4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2\" uninstalled\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2  -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "89b13117",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-core-v2-crds\" uninstalled\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-core-v2-crds -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "f06f0dac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "namespace \"ns1\" deleted\n",
+      "namespace \"ns2\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete namespace ns1\n",
+    "!kubectl delete namespace ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6529925f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/k8s-clusterwide.ipynb b/docs/examples/notebooks/k8s-clusterwide.ipynb
new file mode 100644
index 0000000000..7b78b46d79
--- /dev/null
+++ b/docs/examples/notebooks/k8s-clusterwide.ipynb
@@ -0,0 +1,1070 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "impressed-accessory",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Multi-Namespace Kubernetes Example\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "e09cb983",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-core-v2-crds\" does not exist. Installing it now.\n",
+      "NAME: seldon-core-v2-crds\n",
+      "LAST DEPLOYED: Tue Aug 15 11:01:03 2023\n",
+      "NAMESPACE: seldon-mesh\n",
+      "STATUS: deployed\n",
+      "REVISION: 1\n",
+      "TEST SUITE: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-core-v2-crds  ../k8s/helm-charts/seldon-core-v2-crds -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "448aed9c",
+   "metadata": {},
+   "source": [
+    " The below setup also illustrates using kafka specific prefixes for topics and consumerIds for isolation where the kafka cluster is shared with other applications and you want to enforce constraints. You would not strictly need this in this example as we install Kafka just for Seldon here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "a4debc76",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-v2\" does not exist. Installing it now.\n",
+      "NAME: seldon-v2\n",
+      "LAST DEPLOYED: Tue Aug 15 11:01:07 2023\n",
+      "NAMESPACE: seldon-mesh\n",
+      "STATUS: deployed\n",
+      "REVISION: 1\n",
+      "TEST SUITE: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-v2 ../k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh \\\n",
+    "    --set controller.clusterwide=true \\\n",
+    "    --set kafka.topicPrefix=myorg \\\n",
+    "    --set kafka.consumerGroupIdPrefix=myorg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "29353104",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "namespace/ns1 created\n",
+      "namespace/ns2 created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create namespace ns1\n",
+    "!kubectl create namespace ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "56cdf114",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-runtime\r\n",
+      "LAST DEPLOYED: Tue Aug 15 11:01:11 2023\r\n",
+      "NAMESPACE: ns1\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-runtime ../k8s/helm-charts/seldon-core-v2-runtime  -n ns1 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4b815db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-servers\r\n",
+      "LAST DEPLOYED: Tue Aug 15 10:47:31 2023\r\n",
+      "NAMESPACE: ns1\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n ns1 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3ce2d189",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-runtime\r\n",
+      "LAST DEPLOYED: Tue Aug 15 10:53:12 2023\r\n",
+      "NAMESPACE: ns2\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-runtime ../k8s/helm-charts/seldon-core-v2-runtime  -n ns2 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4a214689",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NAME: seldon-v2-servers\r\n",
+      "LAST DEPLOYED: Tue Aug 15 10:53:28 2023\r\n",
+      "NAMESPACE: ns2\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 1\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n ns2 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b194c88e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver condition met\n",
+      "server.mlops.seldon.io/triton condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s server --all -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "60016845",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver condition met\n",
+      "server.mlops.seldon.io/triton condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s server --all -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "sunrise-commercial",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.18.255.2'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ns1 -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP_NS1=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP_NS1'] = MESH_IP_NS1\n",
+    "MESH_IP_NS1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "012886cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.18.255.4'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ns2 -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP_NS2=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP_NS2'] = MESH_IP_NS2\n",
+    "MESH_IP_NS2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "speaking-bracelet",
+   "metadata": {},
+   "source": [
+    "### Run Models in Different Namespaces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "operating-console",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn-iris-gs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "exempt-bumper",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/sklearn-iris-gs.yaml -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "beneficial-logan",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ns1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "67900afd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"3ca1757c-df02-4e57-87c1-38311bcc5943\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-host ${MESH_IP_NS1}:80 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a1848c7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"iris_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"1\"\r\n",
+      "      ],\r\n",
+      "      \"parameters\": {\r\n",
+      "        \"content_type\": {\r\n",
+      "          \"stringParam\": \"np\"\r\n",
+      "        }\r\n",
+      "      },\r\n",
+      "      \"contents\": {\r\n",
+      "        \"int64Contents\": [\r\n",
+      "          \"2\"\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-mode grpc --inference-host ${MESH_IP_NS1}:80 \\\n",
+    "   '{\"model_name\":\"iris\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "0843b13e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/sklearn-iris-gs.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "99e270be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "ae262b3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"f706a23e-775f-4765-bd18-2e98d83bf7d5\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-host ${MESH_IP_NS2}:80 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "30b3342f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"iris_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"1\"\r\n",
+      "      ],\r\n",
+      "      \"parameters\": {\r\n",
+      "        \"content_type\": {\r\n",
+      "          \"stringParam\": \"np\"\r\n",
+      "        }\r\n",
+      "      },\r\n",
+      "      \"contents\": {\r\n",
+      "        \"int64Contents\": [\r\n",
+      "          \"2\"\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-mode grpc --inference-host ${MESH_IP_NS2}:80 \\\n",
+    "   '{\"model_name\":\"iris\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "precious-development",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\n",
+      "model.mlops.seldon.io \"iris\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/sklearn-iris-gs.yaml -n ns1\n",
+    "!kubectl delete -f ./models/sklearn-iris-gs.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b091f23",
+   "metadata": {},
+   "source": [
+    "## Pipelines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "21705cff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 created\n",
+      "model.mlops.seldon.io/tfsimple2 created\n",
+      "model.mlops.seldon.io/tfsimple1 created\n",
+      "model.mlops.seldon.io/tfsimple2 created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/tfsimple1.yaml -n ns1\n",
+    "!kubectl create -f ./models/tfsimple2.yaml -n ns1\n",
+    "!kubectl create -f ./models/tfsimple1.yaml -n ns2\n",
+    "!kubectl create -f ./models/tfsimple2.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "277f1ff7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 condition met\n",
+      "model.mlops.seldon.io/tfsimple2 condition met\n",
+      "model.mlops.seldon.io/tfsimple1 condition met\n",
+      "model.mlops.seldon.io/tfsimple2 condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ns1\n",
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "733b3f95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/tfsimples created\n",
+      "pipeline.mlops.seldon.io/tfsimples created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./pipelines/tfsimples.yaml -n ns1\n",
+    "!kubectl create -f ./pipelines/tfsimples.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "16dcb6e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/tfsimples condition met\n",
+      "pipeline.mlops.seldon.io/tfsimples condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s pipeline --all -n ns1\n",
+    "!kubectl wait --for condition=ready --timeout=300s pipeline --all -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "7b2f86fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples --inference-mode grpc --inference-host ${MESH_IP_NS1}:80 \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "9529a039",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples --inference-mode grpc --inference-host ${MESH_IP_NS2}:80 \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3fe4c34",
+   "metadata": {},
+   "source": [
+    "If you have installed Kafka via the ansible playbook setup-ecosystem then you can use the following command to see the consumer group ids which are reflecting the settings we created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "6a1eb5f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "myorg-ns2-seldon-pipelinegateway-dfd61b49-4bb9-4684-adce-0b7cc215d3af\r\n",
+      "myorg-ns2-seldon-modelgateway-17\r\n",
+      "myorg-ns1-seldon-pipelinegateway-d4fc83e6-29cb-442e-90cd-92a389961cfe\r\n",
+      "myorg-ns2-seldon-modelgateway-60\r\n",
+      "myorg-ns2-seldon-dataflow-73d465744b7b1b5be20e88d6245e50bd\r\n",
+      "myorg-ns1-seldon-modelgateway-60\r\n",
+      "myorg-ns1-seldon-modelgateway-17\r\n",
+      "myorg-ns1-seldon-dataflow-f563e04e093caa20c03e6eced084331b\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl exec seldon-kafka-0 -n seldon-mesh -- bin/kafka-consumer-groups.sh --list --bootstrap-server localhost:9092"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a360fcb",
+   "metadata": {},
+   "source": [
+    "We can similarly show the topics that have been created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "71285ba0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__consumer_offsets\r\n",
+      "myorg.ns1.errors.errors\r\n",
+      "myorg.ns1.model.iris.inputs\r\n",
+      "myorg.ns1.model.iris.outputs\r\n",
+      "myorg.ns1.model.tfsimple1.inputs\r\n",
+      "myorg.ns1.model.tfsimple1.outputs\r\n",
+      "myorg.ns1.model.tfsimple2.inputs\r\n",
+      "myorg.ns1.model.tfsimple2.outputs\r\n",
+      "myorg.ns1.pipeline.tfsimples.inputs\r\n",
+      "myorg.ns1.pipeline.tfsimples.outputs\r\n",
+      "myorg.ns2.errors.errors\r\n",
+      "myorg.ns2.model.iris.inputs\r\n",
+      "myorg.ns2.model.iris.outputs\r\n",
+      "myorg.ns2.model.tfsimple1.inputs\r\n",
+      "myorg.ns2.model.tfsimple1.outputs\r\n",
+      "myorg.ns2.model.tfsimple2.inputs\r\n",
+      "myorg.ns2.model.tfsimple2.outputs\r\n",
+      "myorg.ns2.pipeline.tfsimples.inputs\r\n",
+      "myorg.ns2.pipeline.tfsimples.outputs\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl exec seldon-kafka-0 -n seldon-mesh -- bin/kafka-topics.sh --bootstrap-server=localhost:9092 --list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "b0b4b82f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io \"tfsimples\" deleted\n",
+      "pipeline.mlops.seldon.io \"tfsimples\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./pipelines/tfsimples.yaml -n ns1\n",
+    "!kubectl delete -f ./pipelines/tfsimples.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "941ed2c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"tfsimple1\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple2\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple1\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple2\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/tfsimple1.yaml -n ns1\n",
+    "!kubectl delete -f ./models/tfsimple2.yaml -n ns1\n",
+    "!kubectl delete -f ./models/tfsimple1.yaml -n ns2\n",
+    "!kubectl delete -f ./models/tfsimple2.yaml -n ns2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "280c1dcd",
+   "metadata": {},
+   "source": [
+    "## TearDown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "e694edee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2-servers\" uninstalled\n",
+      "release \"seldon-v2-servers\" uninstalled\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2-servers -n ns1 --wait\n",
+    "!helm delete seldon-v2-servers -n ns2 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "8b00bd3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2-runtime\" uninstalled\n",
+      "release \"seldon-v2-runtime\" uninstalled\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2-runtime -n ns1 --wait\n",
+    "!helm delete seldon-v2-runtime -n ns2 --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "4c671429",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-v2\" uninstalled\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-v2 -n seldon-mesh --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "72b24840",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "release \"seldon-core-v2-crds\" uninstalled\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm delete seldon-core-v2-crds -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "e7773481",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "namespace \"ns1\" deleted\n",
+      "namespace \"ns2\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete namespace ns1\n",
+    "!kubectl delete namespace ns2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf5ce6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/k8s-custom-servers.ipynb b/docs/examples/notebooks/k8s-custom-servers.ipynb
new file mode 100644
index 0000000000..76f7b59e02
--- /dev/null
+++ b/docs/examples/notebooks/k8s-custom-servers.ipynb
@@ -0,0 +1,504 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "impressed-accessory",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Kubernetes Examples\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "91687b33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"NAMESPACE\"] = \"seldon-mesh\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "sunrise-commercial",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.18.255.2'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP'] = MESH_IP\n",
+    "MESH_IP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ae6fc5e",
+   "metadata": {},
+   "source": [
+    "## Custom Server with Capabilities\n",
+    "\n",
+    "The `capabilities` field replaces the capabilities from the ServerConfig."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "19cea33d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Server\r\n",
+      "metadata:\r\n",
+      "  name: mlserver-134\r\n",
+      "spec:\r\n",
+      "  serverConfig: mlserver\r\n",
+      "  capabilities:\r\n",
+      "  - mlserver-1.3.4\r\n",
+      "  podSpec:\r\n",
+      "    containers:\r\n",
+      "    - image: seldonio/mlserver:1.3.4\r\n",
+      "      name: mlserver\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./servers/custom-mlserver-capabilities.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "293cbbf2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver-134 created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./servers/custom-mlserver-capabilities.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "283c3cc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver condition met\n",
+      "server.mlops.seldon.io/mlserver-134 condition met\n",
+      "server.mlops.seldon.io/triton condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s server --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5f028d19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  requirements:\r\n",
+      "  - mlserver-1.3.4\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/iris-custom-requirements.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9c274ddf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/iris-custom-requirements.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b88e05d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "76ae2b50",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"057ae95c-e6bc-4f57-babf-0817ff171729\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-host ${MESH_IP}:80 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b9872125",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/iris-custom-server.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e07aa196",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io \"mlserver-134\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./servers/custom-mlserver.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31070ea2",
+   "metadata": {},
+   "source": [
+    "## Custom Server with Extra Capabilities\n",
+    "\n",
+    "The `extraCapabilities` field extends the existing list from the ServerConfig."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0a7d7dc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Server\r\n",
+      "metadata:\r\n",
+      "  name: mlserver-134\r\n",
+      "spec:\r\n",
+      "  serverConfig: mlserver\r\n",
+      "  extraCapabilities:\r\n",
+      "  - mlserver-1.3.4\r\n",
+      "  podSpec:\r\n",
+      "    containers:\r\n",
+      "    - image: seldonio/mlserver:1.3.4\r\n",
+      "      name: mlserver\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./servers/custom-mlserver.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8aa3ac8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver-134 created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./servers/custom-mlserver.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6c240ed2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver condition met\n",
+      "server.mlops.seldon.io/mlserver-134 condition met\n",
+      "server.mlops.seldon.io/triton condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s server --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a2fc09f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  server: mlserver-134\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/iris-custom-server.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d72cdf1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/iris-custom-server.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "226f5610",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e2da91a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"a3e17c6c-ee3f-4a51-b890-6fb16385a757\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-host ${MESH_IP}:80 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "0923dcf0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/iris-custom-server.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "db986727",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io \"mlserver-134\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./servers/custom-mlserver.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e694edee",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/k8s-examples.ipynb b/docs/examples/notebooks/k8s-examples.ipynb
new file mode 100644
index 0000000000..6abe2054eb
--- /dev/null
+++ b/docs/examples/notebooks/k8s-examples.ipynb
@@ -0,0 +1,1372 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "impressed-accessory",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Kubernetes Examples\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "91687b33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"NAMESPACE\"] = \"seldon-mesh\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "sunrise-commercial",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.18.255.2'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP'] = MESH_IP\n",
+    "MESH_IP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "speaking-bracelet",
+   "metadata": {},
+   "source": [
+    "### Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "operating-console",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn-iris-gs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "exempt-bumper",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/sklearn-iris-gs.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "beneficial-logan",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "prepared-duration",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-06-30T10:01:52Z\",\r\n",
+      "      \"message\": \"ModelAvailable\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-06-30T10:01:52Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "67900afd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"7fd401e1-3dce-46f5-9668-902aea652b89\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-host ${MESH_IP}:80 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a1848c7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"iris_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"1\"\r\n",
+      "      ],\r\n",
+      "      \"parameters\": {\r\n",
+      "        \"content_type\": {\r\n",
+      "          \"stringParam\": \"np\"\r\n",
+      "        }\r\n",
+      "      },\r\n",
+      "      \"contents\": {\r\n",
+      "        \"int64Contents\": [\r\n",
+      "          \"2\"\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-mode grpc --inference-host ${MESH_IP}:80 \\\n",
+    "   '{\"model_name\":\"iris\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "attended-vanilla",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-06-30T09:59:12Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-06-30T09:59:12Z\",\r\n",
+      "      \"reason\": \"StatefulSet replicas matches desired replicas\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"StatefulSetReady\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"loadedModels\": 1,\r\n",
+      "  \"replicas\": 1,\r\n",
+      "  \"selector\": \"seldon-server-name=mlserver\"\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get server mlserver -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "precious-development",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/sklearn-iris-gs.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lined-inspector",
+   "metadata": {},
+   "source": [
+    "### Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "daily-stretch",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn1.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "experienced-egypt",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris2\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "utility-sweden",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\n",
+      "model.mlops.seldon.io/iris2 created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/sklearn1.yaml -n ${NAMESPACE}\n",
+    "!kubectl create -f ./models/sklearn2.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "african-blair",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\n",
+      "model.mlops.seldon.io/iris2 condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "trained-steering",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: experiment-sample\r\n",
+      "spec:\r\n",
+      "  default: iris\r\n",
+      "  candidates:\r\n",
+      "  - name: iris\r\n",
+      "    weight: 50\r\n",
+      "  - name: iris2\r\n",
+      "    weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/ab-default-model.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "saving-links",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "experiment.mlops.seldon.io/experiment-sample created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./experiments/ab-default-model.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "finnish-rider",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "experiment.mlops.seldon.io/experiment-sample condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s experiment --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "effective-athens",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::29 :iris_1::21]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer --inference-host ${MESH_IP}:80 -i 50 iris \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "widespread-middle",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "experiment.mlops.seldon.io \"experiment-sample\" deleted\n",
+      "model.mlops.seldon.io \"iris\" deleted\n",
+      "model.mlops.seldon.io \"iris2\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./experiments/ab-default-model.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./models/sklearn1.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./models/sklearn2.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4712c36",
+   "metadata": {},
+   "source": [
+    "### Pipeline - model chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1b1f0a00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml \n",
+    "!cat ./models/tfsimple2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "776122d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 created\n",
+      "model.mlops.seldon.io/tfsimple2 created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/tfsimple1.yaml -n ${NAMESPACE}\n",
+    "!kubectl create -f ./models/tfsimple2.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2499e435",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 condition met\n",
+      "model.mlops.seldon.io/tfsimple2 condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "70c78cee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimples\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "    - name: tfsimple2\r\n",
+      "      inputs:\r\n",
+      "      - tfsimple1\r\n",
+      "      tensorMap:\r\n",
+      "        tfsimple1.outputs.OUTPUT0: INPUT0\r\n",
+      "        tfsimple1.outputs.OUTPUT1: INPUT1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple2\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimples.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "69033959",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/tfsimples created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./pipelines/tfsimples.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1d6f2f47",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/tfsimples condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s pipeline --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6fb5a35c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples --inference-mode grpc --inference-host ${MESH_IP}:80 \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "2487f322",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io \"tfsimples\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./pipelines/tfsimples.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "58f3b20c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"tfsimple1\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple2\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/tfsimple1.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./models/tfsimple2.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92b3ea6b",
+   "metadata": {},
+   "source": [
+    "### Pipeline - model join"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "f3c791fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple3\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml\n",
+    "!cat ./models/tfsimple2.yaml\n",
+    "!cat ./models/tfsimple3.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "779ebd6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 created\n",
+      "model.mlops.seldon.io/tfsimple2 created\n",
+      "model.mlops.seldon.io/tfsimple3 created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/tfsimple1.yaml -n ${NAMESPACE}\n",
+    "!kubectl create -f ./models/tfsimple2.yaml -n ${NAMESPACE}\n",
+    "!kubectl create -f ./models/tfsimple3.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "79a7088d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 condition met\n",
+      "model.mlops.seldon.io/tfsimple2 condition met\n",
+      "model.mlops.seldon.io/tfsimple3 condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c6a0e53e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: join\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "    - name: tfsimple2\r\n",
+      "    - name: tfsimple3      \r\n",
+      "      inputs:\r\n",
+      "      - tfsimple1.outputs.OUTPUT0\r\n",
+      "      - tfsimple2.outputs.OUTPUT1\r\n",
+      "      tensorMap:\r\n",
+      "        tfsimple1.outputs.OUTPUT0: INPUT0\r\n",
+      "        tfsimple2.outputs.OUTPUT1: INPUT1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple3\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimples-join.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "1ff21deb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/join created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./pipelines/tfsimples-join.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9984ebf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/join condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s pipeline --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "06c0f069",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer join --inference-mode grpc --inference-host ${MESH_IP}:80 \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "cd63243f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io \"join\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./pipelines/tfsimples-join.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a825bfe6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"tfsimple1\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple2\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple3\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/tfsimple1.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./models/tfsimple2.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./models/tfsimple3.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31f99eda",
+   "metadata": {},
+   "source": [
+    "## Explainer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "2c21f9c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: income\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/examples/mlserver_1.3.5/income/classifier\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/income.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "505d06ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/income.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "46f83c79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "00f5c247",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-06-30T10:02:53Z\",\r\n",
+      "      \"message\": \"ModelAvailable\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-06-30T10:02:53Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model income -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "02d28f57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"income_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"f52acfeb-0f22-429f-8c7a-785ef17cd470\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer income --inference-host ${MESH_IP}:80 \\\n",
+    "     '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 12], \"datatype\": \"FP32\", \"data\": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "52ec12eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: income-explainer\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/examples/mlserver_1.3.5/income/explainer\"\r\n",
+      "  explainer:\r\n",
+      "    type: anchor_tabular\r\n",
+      "    modelRef: income\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/income-explainer.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "2e028f78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income-explainer created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/income-explainer.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "8b8035b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income condition met\n",
+      "model.mlops.seldon.io/income-explainer condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "67489d73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-06-30T10:03:07Z\",\r\n",
+      "      \"message\": \"ModelAvailable\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-06-30T10:03:07Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model income-explainer -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "4f6cbcac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"income-explainer_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"3028a904-9bb3-42d7-bdb7-6e6993323ed7\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"explanation\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"BYTES\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"str\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t\"{\\\"meta\\\": {\\\"name\\\": \\\"AnchorTabular\\\", \\\"type\\\": [\\\"blackbox\\\"], \\\"explanations\\\": [\\\"local\\\"], \\\"params\\\": {\\\"seed\\\": 1, \\\"disc_perc\\\": [25, 50, 75], \\\"threshold\\\": 0.95, \\\"delta\\\": 0.1, \\\"tau\\\": 0.15, \\\"batch_size\\\": 100, \\\"coverage_samples\\\": 10000, \\\"beam_size\\\": 1, \\\"stop_on_first\\\": false, \\\"max_anchor_size\\\": null, \\\"min_samples_start\\\": 100, \\\"n_covered_ex\\\": 10, \\\"binary_cache_size\\\": 10000, \\\"cache_margin\\\": 1000, \\\"verbose\\\": false, \\\"verbose_every\\\": 1, \\\"kwargs\\\": {}}, \\\"version\\\": \\\"0.9.1\\\"}, \\\"data\\\": {\\\"anchor\\\": [\\\"Marital Status = Never-Married\\\", \\\"Relationship = Own-child\\\"], \\\"precision\\\": 0.9705882352941176, \\\"coverage\\\": 0.0699, \\\"raw\\\": {\\\"feature\\\": [3, 5], \\\"mean\\\": [0.8094218415417559, 0.9705882352941176], \\\"precision\\\": [0.8094218415417559, 0.9705882352941176], \\\"coverage\\\": [0.3036, 0.0699], \\\"examples\\\": [{\\\"covered_true\\\": [[23, 4, 1, 1, 5, 1, 4, 0, 0, 0, 40, 9], [44, 4, 1, 1, 8, 0, 4, 1, 0, 0, 40, 9], [60, 2, 5, 1, 5, 1, 4, 0, 0, 0, 25, 9], [52, 4, 1, 1, 2, 0, 4, 1, 0, 0, 50, 9], [66, 6, 1, 1, 8, 0, 4, 1, 0, 0, 8, 9], [52, 4, 1, 1, 8, 0, 4, 1, 0, 0, 40, 9], [27, 4, 1, 1, 1, 1, 4, 1, 0, 0, 35, 9], [48, 4, 1, 1, 6, 0, 4, 1, 0, 0, 45, 9], [45, 6, 1, 1, 5, 0, 4, 1, 0, 0, 40, 9], [40, 2, 1, 1, 5, 4, 4, 0, 0, 0, 45, 9]], \\\"covered_false\\\": [[42, 6, 5, 1, 6, 0, 4, 1, 99999, 0, 80, 9], [29, 4, 1, 1, 8, 1, 4, 1, 0, 0, 50, 9], [49, 4, 1, 1, 8, 0, 4, 1, 0, 0, 50, 9], [34, 4, 5, 1, 8, 0, 4, 1, 0, 0, 40, 9], [38, 2, 1, 1, 5, 5, 4, 0, 7688, 0, 40, 9], [45, 7, 5, 1, 5, 0, 4, 1, 0, 0, 45, 9], [43, 4, 2, 1, 5, 0, 4, 1, 99999, 0, 55, 9], [47, 4, 5, 1, 6, 1, 4, 1, 27828, 0, 60, 9], [42, 6, 1, 1, 2, 0, 4, 1, 15024, 0, 60, 9], [56, 4, 1, 1, 6, 0, 2, 1, 7688, 0, 45, 9]], \\\"uncovered_true\\\": [], \\\"uncovered_false\\\": []}, {\\\"covered_true\\\": [[23, 4, 1, 1, 4, 3, 4, 1, 0, 0, 40, 9], [50, 2, 5, 1, 8, 3, 2, 1, 0, 0, 45, 9], [24, 4, 1, 1, 7, 3, 4, 0, 0, 0, 40, 3], [62, 4, 5, 1, 5, 3, 4, 1, 0, 0, 40, 9], [22, 4, 1, 1, 5, 3, 4, 1, 0, 0, 40, 9], [44, 4, 1, 1, 1, 3, 4, 0, 0, 0, 40, 9], [46, 4, 1, 1, 4, 3, 4, 1, 0, 0, 40, 9], [44, 4, 1, 1, 2, 3, 4, 1, 0, 0, 40, 9], [25, 4, 5, 1, 5, 3, 4, 1, 0, 0, 35, 9], [32, 2, 5, 1, 5, 3, 4, 1, 0, 0, 50, 9]], \\\"covered_false\\\": [[57, 5, 5, 1, 6, 3, 4, 1, 99999, 0, 40, 9], [44, 4, 1, 1, 8, 3, 4, 1, 7688, 0, 60, 9], [43, 2, 5, 1, 4, 3, 2, 0, 8614, 0, 47, 9], [56, 5, 2, 1, 5, 3, 4, 1, 99999, 0, 70, 9]], \\\"uncovered_true\\\": [], \\\"uncovered_false\\\": []}], \\\"all_precision\\\": 0, \\\"num_preds\\\": 1000000, \\\"success\\\": true, \\\"names\\\": [\\\"Marital Status = Never-Married\\\", \\\"Relationship = Own-child\\\"], \\\"prediction\\\": [0], \\\"instance\\\": [47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0], \\\"instances\\\": [[47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0]]}}}\"\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer income-explainer --inference-host ${MESH_IP}:80 \\\n",
+    "     '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 12], \"datatype\": \"FP32\", \"data\": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "da7f239d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"income\" deleted\n",
+      "model.mlops.seldon.io \"income-explainer\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/income.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./models/income-explainer.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e694edee",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/k8s-explainers.ipynb b/docs/examples/notebooks/k8s-explainers.ipynb
new file mode 100644
index 0000000000..ab7ffe8877
--- /dev/null
+++ b/docs/examples/notebooks/k8s-explainers.ipynb
@@ -0,0 +1,763 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "impressed-accessory",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Kubernetes Examples\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "91687b33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"NAMESPACE\"] = \"seldon-mesh\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "sunrise-commercial",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.19.255.1'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP'] = MESH_IP\n",
+    "MESH_IP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31f99eda",
+   "metadata": {},
+   "source": [
+    "## Explain Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2c21f9c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: income\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/examples/mlserver_1.3.5/income/classifier\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/income.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "505d06ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/income.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "46f83c79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "00f5c247",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-03-10T10:54:55Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-03-10T10:54:55Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model income -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "02d28f57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"income_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"3fa3a933-37dd-4e42-b5fd-f5bbc56f8bf4\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer income --inference-host ${MESH_IP}:80 \\\n",
+    "     '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 12], \"datatype\": \"FP32\", \"data\": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "52ec12eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: income-explainer\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/examples/mlserver_1.3.5/income/explainer\"\r\n",
+      "  explainer:\r\n",
+      "    type: anchor_tabular\r\n",
+      "    modelRef: income\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/income-explainer.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2e028f78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income-explainer created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/income-explainer.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8b8035b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income condition met\n",
+      "model.mlops.seldon.io/income-explainer condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "67489d73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-03-10T10:55:07Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-03-10T10:55:07Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model income-explainer -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4f6cbcac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"income-explainer_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"55af3c9b-defd-4a52-8047-cc532461e8a4\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"explanation\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"BYTES\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"str\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t\"{\\\"meta\\\": {\\\"name\\\": \\\"AnchorTabular\\\", \\\"type\\\": [\\\"blackbox\\\"], \\\"explanations\\\": [\\\"local\\\"], \\\"params\\\": {\\\"seed\\\": 1, \\\"disc_perc\\\": [25, 50, 75], \\\"threshold\\\": 0.95, \\\"delta\\\": 0.1, \\\"tau\\\": 0.15, \\\"batch_size\\\": 100, \\\"coverage_samples\\\": 10000, \\\"beam_size\\\": 1, \\\"stop_on_first\\\": false, \\\"max_anchor_size\\\": null, \\\"min_samples_start\\\": 100, \\\"n_covered_ex\\\": 10, \\\"binary_cache_size\\\": 10000, \\\"cache_margin\\\": 1000, \\\"verbose\\\": false, \\\"verbose_every\\\": 1, \\\"kwargs\\\": {}}, \\\"version\\\": \\\"0.9.0\\\"}, \\\"data\\\": {\\\"anchor\\\": [\\\"Marital Status = Never-Married\\\", \\\"Relationship = Own-child\\\", \\\"Capital Gain <= 0.00\\\"], \\\"precision\\\": 0.9974489795918368, \\\"coverage\\\": 0.06853582554517133, \\\"raw\\\": {\\\"feature\\\": [3, 5, 8], \\\"mean\\\": [0.7767175572519084, 0.9107142857142857, 0.9974489795918368], \\\"precision\\\": [0.7767175572519084, 0.9107142857142857, 0.9974489795918368], \\\"coverage\\\": [0.3037383177570093, 0.07165109034267912, 0.06853582554517133], \\\"examples\\\": [{\\\"covered_true\\\": [[25, 2, 1, 1, 1, 0, 4, 1, 0, 0, 40, 9], [23, 4, 1, 1, 1, 3, 2, 1, 0, 0, 24, 9], [25, 4, 1, 1, 2, 0, 4, 1, 0, 0, 60, 9], [38, 4, 1, 1, 5, 0, 4, 1, 0, 0, 40, 9], [39, 4, 5, 1, 5, 5, 4, 0, 0, 1902, 18, 9], [24, 4, 5, 1, 5, 5, 4, 0, 0, 0, 35, 9], [23, 4, 5, 1, 5, 0, 1, 1, 0, 1887, 50, 1], [39, 5, 1, 1, 6, 1, 4, 1, 0, 0, 40, 9], [34, 4, 5, 1, 5, 0, 4, 1, 0, 0, 55, 9], [46, 5, 1, 1, 8, 1, 4, 0, 0, 0, 50, 9]], \\\"covered_false\\\": [[27, 4, 1, 1, 6, 1, 4, 1, 0, 0, 80, 9], [47, 4, 1, 1, 4, 0, 4, 1, 15024, 0, 40, 9], [38, 4, 5, 1, 8, 0, 4, 1, 99999, 0, 70, 9], [43, 4, 5, 1, 8, 0, 4, 1, 0, 0, 50, 9], [47, 4, 1, 1, 8, 0, 4, 1, 3103, 0, 60, 9], [46, 4, 1, 1, 8, 0, 4, 1, 15024, 0, 45, 9], [36, 4, 5, 1, 8, 0, 4, 1, 15024, 0, 50, 9], [45, 4, 5, 1, 8, 0, 4, 1, 0, 0, 50, 9], [37, 4, 5, 1, 8, 0, 4, 1, 0, 0, 50, 9], [43, 1, 1, 1, 8, 1, 4, 1, 0, 0, 40, 9]], \\\"uncovered_true\\\": [], \\\"uncovered_false\\\": []}, {\\\"covered_true\\\": [[36, 4, 1, 1, 7, 3, 4, 0, 0, 0, 40, 9], [45, 2, 5, 1, 5, 3, 4, 0, 0, 0, 45, 9], [34, 4, 1, 1, 5, 3, 4, 1, 0, 0, 40, 9], [42, 2, 5, 1, 5, 3, 4, 0, 0, 0, 45, 9], [44, 4, 1, 1, 4, 3, 4, 1, 5178, 0, 40, 9], [35, 4, 5, 1, 1, 3, 4, 1, 0, 0, 40, 9], [25, 0, 5, 1, 0, 3, 4, 0, 0, 0, 35, 1], [27, 4, 1, 1, 8, 3, 4, 0, 0, 0, 40, 9], [26, 2, 5, 1, 5, 3, 4, 1, 0, 0, 50, 9], [36, 4, 1, 1, 6, 3, 4, 1, 0, 0, 40, 9]], \\\"covered_false\\\": [[25, 4, 1, 1, 2, 3, 4, 1, 27828, 0, 50, 9], [39, 4, 1, 1, 8, 3, 4, 1, 15024, 0, 50, 9], [44, 4, 1, 1, 5, 3, 4, 1, 15024, 0, 50, 3], [36, 7, 1, 1, 4, 3, 4, 1, 7298, 0, 55, 9], [38, 6, 1, 1, 6, 3, 4, 1, 7298, 0, 50, 9], [50, 4, 5, 1, 5, 3, 4, 1, 15024, 0, 45, 9], [39, 4, 1, 1, 6, 3, 4, 1, 15024, 0, 50, 9]], \\\"uncovered_true\\\": [], \\\"uncovered_false\\\": []}, {\\\"covered_true\\\": [[28, 4, 5, 1, 6, 3, 4, 0, 0, 0, 80, 9], [29, 2, 1, 1, 5, 3, 4, 1, 0, 0, 60, 9], [57, 2, 1, 1, 8, 3, 4, 1, 0, 0, 40, 9], [41, 4, 1, 1, 2, 3, 2, 1, 0, 0, 40, 0], [32, 4, 1, 1, 8, 3, 4, 0, 0, 0, 40, 9], [40, 4, 1, 1, 2, 3, 3, 1, 0, 0, 45, 0], [34, 4, 1, 1, 4, 3, 2, 1, 0, 0, 30, 9], [50, 0, 5, 1, 0, 3, 4, 1, 0, 0, 40, 9], [53, 5, 5, 1, 8, 3, 4, 1, 0, 0, 40, 9], [23, 4, 1, 1, 8, 3, 4, 0, 0, 0, 15, 9]], \\\"covered_false\\\": [[23, 6, 1, 1, 1, 3, 4, 1, 0, 2231, 40, 9]], \\\"uncovered_true\\\": [], \\\"uncovered_false\\\": []}], \\\"all_precision\\\": 0, \\\"num_preds\\\": 1000000, \\\"success\\\": true, \\\"names\\\": [\\\"Marital Status = Never-Married\\\", \\\"Relationship = Own-child\\\", \\\"Capital Gain <= 0.00\\\"], \\\"prediction\\\": [0], \\\"instance\\\": [47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0], \\\"instances\\\": [[47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0]]}}}\"\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer income-explainer --inference-host ${MESH_IP}:80 \\\n",
+    "     '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 12], \"datatype\": \"FP32\", \"data\": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "da7f239d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"income\" deleted\n",
+      "model.mlops.seldon.io \"income-explainer\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/income.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./models/income-explainer.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54149676",
+   "metadata": {},
+   "source": [
+    "## Explain Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1cb27568",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: income\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/examples/mlserver_1.3.5/income/classifier\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/income.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "59031973",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/income.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "83248c35",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "582560e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-03-10T10:55:15Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-03-10T10:55:15Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model income -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "488bc1d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"income_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"a7d69896-1396-4c15-92bb-bd586cb3572a\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer income --inference-host ${MESH_IP}:80 \\\n",
+    "     '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 12], \"datatype\": \"FP32\", \"data\": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "43313bfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: income-prod\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: income\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - income\r\n",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/income-v1.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "74927027",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/income-prod created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./pipelines/income-v1.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6c510892",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/income-prod condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s pipeline --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e44b3d2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"\",\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\"\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer income-prod --inference-host ${MESH_IP}:80 \\\n",
+    "     '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 12], \"datatype\": \"FP32\", \"data\": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "faa2c4be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: income-explainer\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/examples/mlserver_1.3.5/income/explainer\"\r\n",
+      "  explainer:\r\n",
+      "    type: anchor_tabular\r\n",
+      "    pipelineRef: income-prod\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/income-explainer-pipeline.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "26c6b223",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income-explainer created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/income-explainer-pipeline.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d66f6fd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/income condition met\n",
+      "model.mlops.seldon.io/income-explainer condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "5eac34e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-03-10T10:55:34Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-03-10T10:55:34Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model income-explainer -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "28c1610f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"income-explainer_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"aff2498f-c3be-4056-b718-eaa2f714d75b\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"explanation\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"BYTES\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"str\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t\"{\\\"meta\\\": {\\\"name\\\": \\\"AnchorTabular\\\", \\\"type\\\": [\\\"blackbox\\\"], \\\"explanations\\\": [\\\"local\\\"], \\\"params\\\": {\\\"seed\\\": 1, \\\"disc_perc\\\": [25, 50, 75], \\\"threshold\\\": 0.95, \\\"delta\\\": 0.1, \\\"tau\\\": 0.15, \\\"batch_size\\\": 100, \\\"coverage_samples\\\": 10000, \\\"beam_size\\\": 1, \\\"stop_on_first\\\": false, \\\"max_anchor_size\\\": null, \\\"min_samples_start\\\": 100, \\\"n_covered_ex\\\": 10, \\\"binary_cache_size\\\": 10000, \\\"cache_margin\\\": 1000, \\\"verbose\\\": false, \\\"verbose_every\\\": 1, \\\"kwargs\\\": {}}, \\\"version\\\": \\\"0.9.0\\\"}, \\\"data\\\": {\\\"anchor\\\": [\\\"Marital Status = Never-Married\\\", \\\"Relationship = Own-child\\\", \\\"Capital Gain <= 0.00\\\"], \\\"precision\\\": 0.9937629937629938, \\\"coverage\\\": 0.06853582554517133, \\\"raw\\\": {\\\"feature\\\": [3, 5, 8], \\\"mean\\\": [0.7935606060606061, 0.9120879120879121, 0.9937629937629938], \\\"precision\\\": [0.7935606060606061, 0.9120879120879121, 0.9937629937629938], \\\"coverage\\\": [0.3037383177570093, 0.07165109034267912, 0.06853582554517133], \\\"examples\\\": [{\\\"covered_true\\\": [[27, 4, 1, 1, 1, 1, 4, 0, 0, 0, 65, 9], [35, 4, 1, 1, 6, 0, 4, 1, 0, 0, 60, 9], [40, 4, 1, 1, 2, 0, 4, 1, 0, 0, 40, 9], [63, 0, 2, 1, 0, 0, 4, 1, 0, 0, 12, 9], [40, 0, 1, 1, 0, 1, 4, 0, 0, 0, 50, 9], [24, 4, 1, 1, 2, 3, 2, 0, 0, 0, 40, 9], [36, 4, 1, 1, 5, 4, 4, 0, 0, 0, 44, 9], [24, 4, 1, 1, 6, 3, 2, 1, 0, 0, 40, 9], [48, 4, 1, 1, 7, 4, 1, 0, 0, 0, 40, 7], [39, 4, 1, 1, 2, 0, 1, 1, 0, 0, 40, 7]], \\\"covered_false\\\": [[32, 4, 5, 1, 8, 0, 2, 1, 0, 1977, 40, 9], [36, 4, 1, 1, 8, 1, 4, 1, 8614, 0, 40, 9], [47, 2, 5, 1, 8, 0, 2, 1, 0, 1977, 50, 9], [48, 4, 1, 1, 8, 0, 4, 1, 0, 0, 45, 9], [49, 4, 5, 1, 5, 1, 4, 1, 0, 0, 40, 9], [48, 7, 2, 1, 5, 0, 4, 1, 0, 0, 65, 9], [38, 4, 5, 1, 5, 0, 4, 1, 99999, 0, 65, 9], [49, 2, 5, 1, 8, 0, 2, 1, 0, 0, 47, 9], [62, 5, 5, 1, 6, 0, 4, 1, 0, 0, 45, 9], [46, 7, 2, 1, 5, 0, 4, 1, 7688, 0, 45, 9]], \\\"uncovered_true\\\": [], \\\"uncovered_false\\\": []}, {\\\"covered_true\\\": [[43, 4, 1, 1, 8, 3, 4, 1, 0, 0, 40, 9], [22, 4, 1, 1, 1, 3, 4, 0, 0, 0, 40, 9], [60, 2, 5, 1, 8, 3, 4, 1, 0, 0, 40, 9], [57, 4, 5, 1, 5, 3, 4, 1, 0, 1977, 45, 9], [65, 4, 5, 1, 6, 3, 4, 1, 0, 0, 28, 9], [50, 2, 1, 1, 4, 3, 4, 1, 0, 0, 40, 9], [48, 4, 1, 1, 8, 3, 1, 1, 0, 0, 40, 1], [46, 7, 1, 1, 8, 3, 4, 1, 0, 0, 45, 9], [48, 2, 5, 1, 5, 3, 4, 0, 0, 0, 80, 9], [22, 4, 1, 1, 6, 3, 4, 1, 0, 0, 40, 9]], \\\"covered_false\\\": [[30, 4, 1, 1, 5, 3, 4, 1, 99999, 0, 35, 9], [52, 4, 5, 1, 5, 3, 4, 1, 15020, 0, 50, 9]], \\\"uncovered_true\\\": [], \\\"uncovered_false\\\": []}, {\\\"covered_true\\\": [[38, 4, 1, 1, 2, 3, 2, 1, 0, 0, 40, 5], [35, 4, 5, 1, 5, 3, 4, 1, 0, 1887, 40, 9], [45, 4, 2, 1, 5, 3, 2, 0, 0, 3004, 35, 9], [78, 5, 1, 1, 8, 3, 4, 1, 0, 2392, 40, 9], [33, 4, 1, 1, 8, 3, 4, 1, 0, 0, 55, 9], [29, 4, 1, 1, 5, 3, 4, 1, 0, 0, 40, 9], [51, 2, 1, 1, 5, 3, 4, 1, 0, 0, 26, 9], [48, 4, 5, 1, 8, 3, 4, 1, 0, 0, 40, 9], [45, 2, 5, 1, 5, 3, 4, 0, 0, 0, 30, 9], [29, 4, 5, 1, 8, 3, 1, 1, 0, 0, 40, 6]], \\\"covered_false\\\": [], \\\"uncovered_true\\\": [], \\\"uncovered_false\\\": []}], \\\"all_precision\\\": 0, \\\"num_preds\\\": 1000000, \\\"success\\\": true, \\\"names\\\": [\\\"Marital Status = Never-Married\\\", \\\"Relationship = Own-child\\\", \\\"Capital Gain <= 0.00\\\"], \\\"prediction\\\": [0], \\\"instance\\\": [47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0], \\\"instances\\\": [[47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0]]}}}\"\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer income-explainer --inference-host ${MESH_IP}:80 \\\n",
+    "     '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 12], \"datatype\": \"FP32\", \"data\": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "fbd5d037",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io \"income-prod\" deleted\n",
+      "model.mlops.seldon.io \"income-explainer\" deleted\n",
+      "model.mlops.seldon.io \"income\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./pipelines/income-v1.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./models/income-explainer-pipeline.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./models/income.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e694edee",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/k8s-multi-version.ipynb b/docs/examples/notebooks/k8s-multi-version.ipynb
new file mode 100644
index 0000000000..145dc71239
--- /dev/null
+++ b/docs/examples/notebooks/k8s-multi-version.ipynb
@@ -0,0 +1,319 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "impressed-accessory",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Kubernetes Multi Version Artifact Examples\n",
+    "\n",
+    "We have a Triton model that has two version folders\n",
+    "\n",
+    "Model 1 adds 10 to input, Model 2 multiples by 10 the input. The structure of the artifact repo is shown below:\n",
+    "\n",
+    "```\n",
+    "config.pbtxt\n",
+    "1/model.py <add 10>\n",
+    "2/model.py <mul 10>\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "91687b33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"NAMESPACE\"] = \"seldon-mesh\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "sunrise-commercial",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.19.255.1'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP'] = MESH_IP\n",
+    "MESH_IP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "speaking-bracelet",
+   "metadata": {},
+   "source": [
+    "### Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "operating-console",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: math\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/multi-version\"\r\n",
+      "  artifactVersion: 1\r\n",
+      "  requirements:\r\n",
+      "  - triton\r\n",
+      "  - python\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/multi-version-1.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "exempt-bumper",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/math created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl apply -f ./models/multi-version-1.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "beneficial-logan",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/math condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "67900afd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"math_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer math --inference-mode grpc --inference-host ${MESH_IP}:80 \\\n",
+    "  '{\"model_name\":\"math\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2c75517b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: math\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/multi-version\"\r\n",
+      "  artifactVersion: 2\r\n",
+      "  requirements:\r\n",
+      "  - triton\r\n",
+      "  - python\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/multi-version-2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "923fa31f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/math configured\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl apply -f ./models/multi-version-2.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a3b12f5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/math condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "45726caa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"math_2\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          10,\r\n",
+      "          20,\r\n",
+      "          30,\r\n",
+      "          40\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer math --inference-mode grpc --inference-host ${MESH_IP}:80 \\\n",
+    "  '{\"model_name\":\"math\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "precious-development",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"math\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/multi-version-1.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e694edee",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/k8s-resource-deletion.ipynb b/docs/examples/notebooks/k8s-resource-deletion.ipynb
new file mode 100644
index 0000000000..016af054c6
--- /dev/null
+++ b/docs/examples/notebooks/k8s-resource-deletion.ipynb
@@ -0,0 +1,922 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "impressed-accessory",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Kubernetes Model and Server Deletion Tests\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "210dc092",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-core-v2-crds\" has been upgraded. Happy Helming!\r\n",
+      "NAME: seldon-core-v2-crds\r\n",
+      "LAST DEPLOYED: Thu Jul 27 10:23:00 2023\r\n",
+      "NAMESPACE: seldon-mesh\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 2\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-core-v2-crds  ../k8s/helm-charts/seldon-core-v2-crds -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "90cf75bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-core-v2\" does not exist. Installing it now.\n",
+      "NAME: seldon-core-v2\n",
+      "LAST DEPLOYED: Thu Jul 27 10:23:03 2023\n",
+      "NAMESPACE: seldon-mesh\n",
+      "STATUS: deployed\n",
+      "REVISION: 1\n",
+      "TEST SUITE: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-core-v2 ../k8s/helm-charts/seldon-core-v2-setup/ -n seldon-mesh --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ef3069ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-v2-runtime\" does not exist. Installing it now.\n",
+      "NAME: seldon-v2-runtime\n",
+      "LAST DEPLOYED: Thu Jul 27 10:23:23 2023\n",
+      "NAMESPACE: seldon-mesh\n",
+      "STATUS: deployed\n",
+      "REVISION: 1\n",
+      "TEST SUITE: None\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-v2-runtime ../k8s/helm-charts/seldon-core-v2-runtime   --namespace seldon-mesh --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9e481bf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-v2-servers\" has been upgraded. Happy Helming!\r\n",
+      "NAME: seldon-v2-servers\r\n",
+      "LAST DEPLOYED: Thu Jul 27 13:50:38 2023\r\n",
+      "NAMESPACE: seldon-mesh\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 2\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n seldon-mesh --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "91687b33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"NAMESPACE\"] = \"seldon-mesh\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "sunrise-commercial",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.18.255.2'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP'] = MESH_IP\n",
+    "MESH_IP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3468a659",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldonruntime.mlops.seldon.io/seldon condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s seldonruntime --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8674f2e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver condition met\n",
+      "server.mlops.seldon.io/triton condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s server --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "speaking-bracelet",
+   "metadata": {},
+   "source": [
+    "### Check Model can be deleted after Server delete on which it is running"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "exempt-bumper",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/sklearn-iris-gs.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "beneficial-logan",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "prepared-duration",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T09:54:40Z\",\r\n",
+      "      \"message\": \"ModelAvailable\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T09:54:40Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "attended-vanilla",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io \"mlserver\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete server mlserver -n ${NAMESPACE} "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "91e18da0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T09:54:44Z\",\r\n",
+      "      \"message\": \"ScheduleFailed\",\r\n",
+      "      \"reason\": \"failed to schedule model iris. [failed replica filter RequirementsReplicaFilter for server replica triton:0 : model requirements [sklearn] replica capabilities [triton dali fil onnx openvino python pytorch tensorflow tensorrt]]\",\r\n",
+      "      \"status\": \"False\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T09:54:44Z\",\r\n",
+      "      \"message\": \"ScheduleFailed\",\r\n",
+      "      \"reason\": \"failed to schedule model iris. [failed replica filter RequirementsReplicaFilter for server replica triton:0 : model requirements [sklearn] replica capabilities [triton dali fil onnx openvino python pytorch tensorflow tensorrt]]\",\r\n",
+      "      \"status\": \"False\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "d7dd152c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-v2-servers\" has been upgraded. Happy Helming!\r\n",
+      "NAME: seldon-v2-servers\r\n",
+      "LAST DEPLOYED: Mon Jul 31 10:54:56 2023\r\n",
+      "NAMESPACE: seldon-mesh\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 6\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n seldon-mesh --wait"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "89721868",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "552ded3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T09:55:01Z\",\r\n",
+      "      \"message\": \"ModelAvailable\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T09:55:01Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "4796427d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io \"mlserver\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete server mlserver -n ${NAMESPACE} "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "6ed9a5b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T09:55:24Z\",\r\n",
+      "      \"message\": \"ScheduleFailed\",\r\n",
+      "      \"reason\": \"failed to schedule model iris. [failed replica filter RequirementsReplicaFilter for server replica triton:0 : model requirements [sklearn] replica capabilities [triton dali fil onnx openvino python pytorch tensorflow tensorrt]]\",\r\n",
+      "      \"status\": \"False\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T09:55:24Z\",\r\n",
+      "      \"message\": \"ScheduleFailed\",\r\n",
+      "      \"reason\": \"failed to schedule model iris. [failed replica filter RequirementsReplicaFilter for server replica triton:0 : model requirements [sklearn] replica capabilities [triton dali fil onnx openvino python pytorch tensorflow tensorrt]]\",\r\n",
+      "      \"status\": \"False\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "396e67f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/sklearn-iris-gs.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "b84424d0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-v2-servers\" has been upgraded. Happy Helming!\r\n",
+      "NAME: seldon-v2-servers\r\n",
+      "LAST DEPLOYED: Mon Jul 31 10:55:35 2023\r\n",
+      "NAMESPACE: seldon-mesh\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 7\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n seldon-mesh --wait"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4186bd10",
+   "metadata": {},
+   "source": [
+    "### Check Model can be deleted after failed scheduling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1419b5db",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/sklearn-iris-gs-baduri.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "df3ec0c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T09:38:59Z\",\r\n",
+      "      \"message\": \"ModelFailed\",\r\n",
+      "      \"reason\": \"Failed to download from gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-notexistent-uri any files\",\r\n",
+      "      \"status\": \"False\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T09:38:59Z\",\r\n",
+      "      \"message\": \"ModelFailed\",\r\n",
+      "      \"reason\": \"Failed to download from gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-notexistent-uri any files\",\r\n",
+      "      \"status\": \"False\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "df03e639",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/sklearn-iris-gs-baduri.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "497d97d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No resources found in seldon-mesh namespace.\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f941bcc1",
+   "metadata": {},
+   "source": [
+    "## Check Models rescheduled when Server deleted\n",
+    "\n",
+    " * Add models to initial MLServer\n",
+    " * Create second MLServer \n",
+    " * Delete first MLServer\n",
+    " * Model rescheduled\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "0a85a02b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/sklearn-iris-gs.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "d6c9d0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "8109cc71",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T13:45:56Z\",\r\n",
+      "      \"message\": \"ModelAvailable\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T13:45:56Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "b4cd54e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Server\r\n",
+      "metadata:\r\n",
+      "  name: mlserver2\r\n",
+      "spec:\r\n",
+      "  serverConfig: mlserver\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./servers/mlserver2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "561b8492",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver2 created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./servers/mlserver2.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "db1369ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io/mlserver condition met\n",
+      "server.mlops.seldon.io/mlserver2 condition met\n",
+      "server.mlops.seldon.io/triton condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s server --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "4b635449",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io \"mlserver\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete server mlserver -n ${NAMESPACE} "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "de4faaa9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"conditions\": [\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T13:46:15Z\",\r\n",
+      "      \"message\": \"ModelAvailable\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"ModelReady\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"lastTransitionTime\": \"2023-07-31T13:46:15Z\",\r\n",
+      "      \"status\": \"True\",\r\n",
+      "      \"type\": \"Ready\"\r\n",
+      "    }\r\n",
+      "  ],\r\n",
+      "  \"replicas\": 1\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get model iris -n ${NAMESPACE} -o jsonpath='{.status}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "ddb7c76a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "f60051a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/sklearn-iris-gs.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "bad76bf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "server.mlops.seldon.io \"mlserver2\" deleted\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./servers/mlserver2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "a6a000e0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Release \"seldon-v2-servers\" has been upgraded. Happy Helming!\r\n",
+      "NAME: seldon-v2-servers\r\n",
+      "LAST DEPLOYED: Mon Jul 31 14:46:28 2023\r\n",
+      "NAMESPACE: seldon-mesh\r\n",
+      "STATUS: deployed\r\n",
+      "REVISION: 9\r\n",
+      "TEST SUITE: None\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!helm upgrade --install seldon-v2-servers ../k8s/helm-charts/seldon-core-v2-servers  -n seldon-mesh --wait"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74a049ab",
+   "metadata": {},
+   "source": [
+    "## Cause OOM on Server and validate Models can be resheduled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7deabc28",
+   "metadata": {},
+   "source": [
+    "## Model code changes\n",
+    "  * remove finalizer but ensure model delete message gets through and Model is marked for deletion.\n",
+    "  * Check cases to ensure failures to Delete allow new model to be created with same name.\n",
+    "    * Maybe need unique hash so agent can remove Model if new load comes in for smae name with new Hash"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e0a8067",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/k8s-tritonclient-aio.ipynb b/docs/examples/notebooks/k8s-tritonclient-aio.ipynb
new file mode 100644
index 0000000000..804224625c
--- /dev/null
+++ b/docs/examples/notebooks/k8s-tritonclient-aio.ipynb
@@ -0,0 +1,924 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tritonclient Examples with Seldon Core V2 (Asyncio)\n",
+    "\n",
+    "- Note: for compatibility of Tritonclient check this issue https://github.com/SeldonIO/seldon-core-v2/issues/471\n",
+    "\n",
+    "Requirements:\n",
+    "   * `pip install tritonclient`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"NAMESPACE\"] = \"seldon-mesh\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.19.255.1'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP'] = MESH_IP\n",
+    "MESH_IP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## With MLServer\n",
+    "\n",
+    "- Note: binary data support in HTTP is blocked by https://github.com/SeldonIO/MLServer/issues/324"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy Model and Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat models/sklearn-iris-gs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: iris-pipeline\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: iris\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - iris\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat pipelines/iris.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\n",
+      "pipeline.mlops.seldon.io/iris-pipeline created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl apply -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}\n",
+    "!kubectl apply -f pipelines/iris.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\n",
+      "pipeline.mlops.seldon.io/iris-pipeline condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model iris -n ${NAMESPACE}\n",
+    "!kubectl wait --for condition=ready --timeout=300s pipelines iris-pipeline -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### HTTP Transport Protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model ready: True\n",
+      "model metadata: {'name': 'iris_1', 'versions': [], 'platform': '', 'inputs': [], 'outputs': [], 'parameters': {}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tritonclient.http.aio as httpclient\n",
+    "import numpy as np\n",
+    "\n",
+    "http_triton_client = httpclient.InferenceServerClient(\n",
+    "    url=f\"{MESH_IP}:80\",\n",
+    "    verbose=False,\n",
+    ")\n",
+    "\n",
+    "print(\"model ready:\", await http_triton_client.is_model_ready(\"iris\"))\n",
+    "print(\"model metadata:\", await http_triton_client.get_model_metadata(\"iris\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Against Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "headers = {\"content-type\": \"application/json\"}\n",
+    "\n",
+    "binary_data = False\n",
+    "\n",
+    "inputs = [httpclient.InferInput(\"predict\", (1, 4), \"FP64\")]\n",
+    "inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype(\"float64\"), binary_data=binary_data)\n",
+    "\n",
+    "outputs = [httpclient.InferRequestedOutput(\"predict\", binary_data=binary_data)]\n",
+    "\n",
+    "result = await http_triton_client.infer(\"iris\", inputs, outputs=outputs, headers=headers)\n",
+    "result.as_numpy(\"predict\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Against Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "headers = {\"content-type\": \"application/json\"}\n",
+    "\n",
+    "binary_data = False\n",
+    "\n",
+    "inputs = [httpclient.InferInput(\"predict\", (1, 4), \"FP64\")]\n",
+    "inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype(\"float64\"), binary_data=binary_data)\n",
+    "\n",
+    "outputs = [httpclient.InferRequestedOutput(\"predict\", binary_data=binary_data)]\n",
+    "\n",
+    "result = await http_triton_client.infer(\"iris-pipeline.pipeline\", inputs, outputs=outputs, headers=headers)\n",
+    "result.as_numpy(\"predict\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GRPC Transport Protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tritonclient.grpc.aio as grpcclient\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "grpc_triton_client = grpcclient.InferenceServerClient(\n",
+    "    url=f\"{MESH_IP}:80\",\n",
+    "    verbose=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model ready: True\n",
+      "name: \"iris_1\"\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_name = \"iris\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "print(\"model ready:\", await grpc_triton_client.is_model_ready(model_name, headers=headers))\n",
+    "print(await grpc_triton_client.get_model_metadata(model_name, headers=headers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Against Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_name = \"iris\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "inputs = [\n",
+    "    grpcclient.InferInput(\"predict\", (1, 4), \"FP64\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype(\"float64\"))\n",
+    "\n",
+    "outputs = [grpcclient.InferRequestedOutput(\"predict\")]\n",
+    "\n",
+    "\n",
+    "result = await grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)\n",
+    "result.as_numpy(\"predict\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Against Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_name = \"iris-pipeline.pipeline\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "inputs = [\n",
+    "    grpcclient.InferInput(\"predict\", (1, 4), \"FP64\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype(\"float64\"))\n",
+    "\n",
+    "outputs = [grpcclient.InferRequestedOutput(\"predict\")]\n",
+    "\n",
+    "\n",
+    "result = await grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)\n",
+    "result.as_numpy(\"predict\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "await http_triton_client.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\n",
+      "pipeline.mlops.seldon.io \"iris-pipeline\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f models/sklearn-iris-gs.yaml -n seldon-mesh\n",
+    "!kubectl delete -f pipelines/iris.yaml -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## With Tritonserver"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy Model and Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple1\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\r\n",
+      "  requirements:\r\n",
+      "  - tensorflow\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat models/tfsimple1.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple1\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 created\n",
+      "pipeline.mlops.seldon.io/tfsimple created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl apply -f models/tfsimple1.yaml -n seldon-mesh\n",
+    "!kubectl apply -f pipelines/tfsimple.yaml -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 condition met\n",
+      "pipeline.mlops.seldon.io/tfsimple condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model tfsimple1 -n seldon-mesh\n",
+    "!kubectl wait --for condition=ready --timeout=300s pipelines tfsimple -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### HTTP Transport Protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model ready: True\n",
+      "model metadata: {'name': 'tfsimple1_1', 'versions': ['1'], 'platform': 'tensorflow_graphdef', 'inputs': [{'name': 'INPUT0', 'datatype': 'INT32', 'shape': [-1, 16]}, {'name': 'INPUT1', 'datatype': 'INT32', 'shape': [-1, 16]}], 'outputs': [{'name': 'OUTPUT0', 'datatype': 'INT32', 'shape': [-1, 16]}, {'name': 'OUTPUT1', 'datatype': 'INT32', 'shape': [-1, 16]}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tritonclient.http.aio as httpclient\n",
+    "import numpy as np\n",
+    "\n",
+    "http_triton_client = httpclient.InferenceServerClient(\n",
+    "    url=f\"{MESH_IP}:80\",\n",
+    "    verbose=False,\n",
+    ")\n",
+    "\n",
+    "print(\"model ready:\", await http_triton_client.is_model_ready(\"tfsimple1\"))\n",
+    "print(\"model metadata:\", await http_triton_client.get_model_metadata(\"tfsimple1\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Against Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "binary_data = False\n",
+    "\n",
+    "inputs = [\n",
+    "    httpclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "    httpclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "\n",
+    "outputs = [httpclient.InferRequestedOutput(\"OUTPUT0\", binary_data=binary_data)]\n",
+    "\n",
+    "\n",
+    "result = await http_triton_client.infer(\"tfsimple1\", inputs, outputs=outputs)\n",
+    "result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "binary_data = True\n",
+    "\n",
+    "inputs = [\n",
+    "    httpclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "    httpclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "\n",
+    "outputs = [httpclient.InferRequestedOutput(\"OUTPUT0\", binary_data=binary_data)]\n",
+    "\n",
+    "\n",
+    "result = await http_triton_client.infer(\"tfsimple1\", inputs, outputs=outputs)\n",
+    "result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Against Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "binary_data = False\n",
+    "\n",
+    "inputs = [\n",
+    "    httpclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "    httpclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "\n",
+    "outputs = [httpclient.InferRequestedOutput(\"OUTPUT0\", binary_data=binary_data)]\n",
+    "\n",
+    "\n",
+    "result = await http_triton_client.infer(\"tfsimple.pipeline\", inputs, outputs=outputs)\n",
+    "result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# binary data does not work with http behind pipeline: no opened issue yet\n",
+    "\n",
+    "# binary_data = True\n",
+    "\n",
+    "# inputs = [\n",
+    "#     httpclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "#     httpclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "# ]\n",
+    "# inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "# inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "\n",
+    "# outputs = [httpclient.InferRequestedOutput(\"OUTPUT0\", binary_data=binary_data)]\n",
+    "\n",
+    "\n",
+    "# result = await http_triton_client.infer(\"tfsimple.pipeline\", inputs, outputs=outputs)\n",
+    "# result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GRPC Transport Protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tritonclient.grpc.aio as grpcclient\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "grpc_triton_client = grpcclient.InferenceServerClient(\n",
+    "    url=f\"{MESH_IP}:80\",\n",
+    "    verbose=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model ready: True\n",
+      "name: \"tfsimple1_1\"\n",
+      "versions: \"1\"\n",
+      "platform: \"tensorflow_graphdef\"\n",
+      "inputs {\n",
+      "  name: \"INPUT0\"\n",
+      "  datatype: \"INT32\"\n",
+      "  shape: -1\n",
+      "  shape: 16\n",
+      "}\n",
+      "inputs {\n",
+      "  name: \"INPUT1\"\n",
+      "  datatype: \"INT32\"\n",
+      "  shape: -1\n",
+      "  shape: 16\n",
+      "}\n",
+      "outputs {\n",
+      "  name: \"OUTPUT0\"\n",
+      "  datatype: \"INT32\"\n",
+      "  shape: -1\n",
+      "  shape: 16\n",
+      "}\n",
+      "outputs {\n",
+      "  name: \"OUTPUT1\"\n",
+      "  datatype: \"INT32\"\n",
+      "  shape: -1\n",
+      "  shape: 16\n",
+      "}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_name = \"tfsimple1\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "print(\"model ready:\", await grpc_triton_client.is_model_ready(model_name, headers=headers))\n",
+    "print(await grpc_triton_client.get_model_metadata(model_name, headers=headers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Against Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_name = \"tfsimple1\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "inputs = [\n",
+    "    grpcclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "    grpcclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"))\n",
+    "inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"))\n",
+    "\n",
+    "outputs = [grpcclient.InferRequestedOutput(\"OUTPUT0\")]\n",
+    "\n",
+    "\n",
+    "result = await grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)\n",
+    "result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Against Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_name = \"tfsimple.pipeline\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "inputs = [\n",
+    "    grpcclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "    grpcclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"))\n",
+    "inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"))\n",
+    "\n",
+    "outputs = [grpcclient.InferRequestedOutput(\"OUTPUT0\")]\n",
+    "\n",
+    "\n",
+    "result = await grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)\n",
+    "result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "await http_triton_client.close()\n",
+    "await grpc_triton_client.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"tfsimple1\" deleted\n",
+      "pipeline.mlops.seldon.io \"tfsimple\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f models/tfsimple1.yaml -n seldon-mesh\n",
+    "!kubectl delete -f pipelines/tfsimple.yaml -n seldon-mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f90d0a8b54f81c3642b2ee3336e1de55986a3a982587eefc0621499dbdd4a3fd"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/examples/notebooks/k8s-tritonclient.ipynb b/docs/examples/notebooks/k8s-tritonclient.ipynb
new file mode 100644
index 0000000000..354ff2518b
--- /dev/null
+++ b/docs/examples/notebooks/k8s-tritonclient.ipynb
@@ -0,0 +1,867 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tritonclient Examples with Seldon Core V2\n",
+    "\n",
+    "- Note: for compatibility of Tritonclient check this issue https://github.com/SeldonIO/seldon-core-v2/issues/471"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"NAMESPACE\"] = \"seldon-mesh\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.19.255.1'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP'] = MESH_IP\n",
+    "MESH_IP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## With MLServer\n",
+    "\n",
+    "- Note: binary data support in HTTP is blocked by https://github.com/SeldonIO/MLServer/issues/324"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy Model and Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat models/sklearn-iris-gs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: iris-pipeline\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: iris\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - iris\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat pipelines/iris.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris created\n",
+      "pipeline.mlops.seldon.io/iris-pipeline created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl apply -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}\n",
+    "!kubectl apply -f pipelines/iris.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/iris condition met\n",
+      "pipeline.mlops.seldon.io/iris-pipeline condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model iris -n ${NAMESPACE}\n",
+    "!kubectl wait --for condition=ready --timeout=300s pipelines iris-pipeline -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### HTTP Transport Protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model ready: True\n",
+      "model metadata: {'name': 'iris_1', 'versions': [], 'platform': '', 'inputs': [], 'outputs': [], 'parameters': {}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tritonclient.http as httpclient\n",
+    "import numpy as np\n",
+    "\n",
+    "http_triton_client = httpclient.InferenceServerClient(\n",
+    "    url=f\"{MESH_IP}:80\",\n",
+    "    verbose=False,\n",
+    ")\n",
+    "\n",
+    "print(\"model ready:\", http_triton_client.is_model_ready(\"iris\"))\n",
+    "print(\"model metadata:\", http_triton_client.get_model_metadata(\"iris\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Against model\n",
+    "\n",
+    "binary_data = False\n",
+    "\n",
+    "inputs = [httpclient.InferInput(\"predict\", (1, 4), \"FP64\")]\n",
+    "inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype(\"float64\"), binary_data=binary_data)\n",
+    "\n",
+    "outputs = [httpclient.InferRequestedOutput(\"predict\", binary_data=binary_data)]\n",
+    "\n",
+    "result = http_triton_client.infer(\"iris\", inputs, outputs=outputs)\n",
+    "result.as_numpy(\"predict\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Against pipeline\n",
+    "\n",
+    "binary_data = False\n",
+    "\n",
+    "inputs = [httpclient.InferInput(\"predict\", (1, 4), \"FP64\")]\n",
+    "inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype(\"float64\"), binary_data=binary_data)\n",
+    "\n",
+    "outputs = [httpclient.InferRequestedOutput(\"predict\", binary_data=binary_data)]\n",
+    "\n",
+    "result = http_triton_client.infer(\"iris-pipeline.pipeline\", inputs, outputs=outputs)\n",
+    "result.as_numpy(\"predict\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GRPC Transport Protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tritonclient.grpc as grpcclient\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "grpc_triton_client = grpcclient.InferenceServerClient(\n",
+    "    url=f\"{MESH_IP}:80\",\n",
+    "    verbose=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model ready: True\n",
+      "name: \"iris_1\"\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_name = \"iris\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "print(\"model ready:\", grpc_triton_client.is_model_ready(model_name, headers=headers))\n",
+    "print(grpc_triton_client.get_model_metadata(model_name, headers=headers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Against Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_name = \"iris\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "inputs = [\n",
+    "    grpcclient.InferInput(\"predict\", (1, 4), \"FP64\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype(\"float64\"))\n",
+    "\n",
+    "outputs = [grpcclient.InferRequestedOutput(\"predict\")]\n",
+    "\n",
+    "\n",
+    "result = grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)\n",
+    "result.as_numpy(\"predict\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Against Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_name = \"iris-pipeline.pipeline\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "inputs = [\n",
+    "    grpcclient.InferInput(\"predict\", (1, 4), \"FP64\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.array([[1, 2, 3, 4]]).astype(\"float64\"))\n",
+    "\n",
+    "outputs = [grpcclient.InferRequestedOutput(\"predict\")]\n",
+    "\n",
+    "\n",
+    "result = grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)\n",
+    "result.as_numpy(\"predict\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## With Tritonserver\n",
+    "\n",
+    "- Note: binary data support in HTTP is blocked by https://github.com/SeldonIO/seldon-core-v2/issues/475"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy Model and Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple1\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\r\n",
+      "  requirements:\r\n",
+      "  - tensorflow\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat models/tfsimple1.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple1\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 created\n",
+      "pipeline.mlops.seldon.io/tfsimple created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl apply -f models/tfsimple1.yaml -n ${NAMESPACE}\n",
+    "!kubectl apply -f pipelines/tfsimple.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 condition met\n",
+      "pipeline.mlops.seldon.io/tfsimple condition met\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s model tfsimple1 -n ${NAMESPACE}\n",
+    "!kubectl wait --for condition=ready --timeout=300s pipelines tfsimple -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### HTTP Transport Protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model ready: True\n",
+      "model metadata: {'name': 'iris_1', 'versions': [], 'platform': '', 'inputs': [], 'outputs': [], 'parameters': {}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tritonclient.http as httpclient\n",
+    "import numpy as np\n",
+    "\n",
+    "http_triton_client = httpclient.InferenceServerClient(\n",
+    "    url=f\"{MESH_IP}:80\",\n",
+    "    verbose=False,\n",
+    ")\n",
+    "\n",
+    "print(\"model ready:\", http_triton_client.is_model_ready(\"iris\"))\n",
+    "print(\"model metadata:\", http_triton_client.get_model_metadata(\"iris\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Against model (no binary data)\n",
+    "\n",
+    "binary_data = False\n",
+    "\n",
+    "inputs = [\n",
+    "    httpclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "    httpclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "\n",
+    "outputs = [httpclient.InferRequestedOutput(\"OUTPUT0\", binary_data=binary_data)]\n",
+    "\n",
+    "\n",
+    "result = http_triton_client.infer(\"tfsimple1\", inputs, outputs=outputs)\n",
+    "result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Against model (with binary data)\n",
+    "\n",
+    "binary_data = True\n",
+    "\n",
+    "inputs = [\n",
+    "    httpclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "    httpclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "\n",
+    "outputs = [httpclient.InferRequestedOutput(\"OUTPUT0\", binary_data=binary_data)]\n",
+    "\n",
+    "\n",
+    "result = http_triton_client.infer(\"tfsimple1\", inputs, outputs=outputs)\n",
+    "result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Against Pipeline (no binary data)\n",
+    "\n",
+    "binary_data = False\n",
+    "\n",
+    "inputs = [\n",
+    "    httpclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "    httpclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "\n",
+    "outputs = [httpclient.InferRequestedOutput(\"OUTPUT0\", binary_data=binary_data)]\n",
+    "\n",
+    "\n",
+    "result = http_triton_client.infer(\"tfsimple.pipeline\", inputs, outputs=outputs)\n",
+    "result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## binary data does not work with http behind pipeline\n",
+    "\n",
+    "# import numpy as np\n",
+    "\n",
+    "# binary_data = True\n",
+    "\n",
+    "# inputs = [\n",
+    "#     httpclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "#     httpclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "# ]\n",
+    "# inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "# inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"), binary_data=binary_data)\n",
+    "\n",
+    "# outputs = [httpclient.InferRequestedOutput(\"OUTPUT0\", binary_data=binary_data)]\n",
+    "\n",
+    "\n",
+    "# result = http_triton_client.infer(\"tfsimple.pipeline\", inputs, outputs=outputs)\n",
+    "# result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GRPC Transport Protocol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tritonclient.grpc as grpcclient\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "grpc_triton_client = grpcclient.InferenceServerClient(\n",
+    "    url=f\"{MESH_IP}:80\",\n",
+    "    verbose=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model ready: True\n",
+      "name: \"tfsimple1_1\"\n",
+      "versions: \"1\"\n",
+      "platform: \"tensorflow_graphdef\"\n",
+      "inputs {\n",
+      "  name: \"INPUT0\"\n",
+      "  datatype: \"INT32\"\n",
+      "  shape: -1\n",
+      "  shape: 16\n",
+      "}\n",
+      "inputs {\n",
+      "  name: \"INPUT1\"\n",
+      "  datatype: \"INT32\"\n",
+      "  shape: -1\n",
+      "  shape: 16\n",
+      "}\n",
+      "outputs {\n",
+      "  name: \"OUTPUT0\"\n",
+      "  datatype: \"INT32\"\n",
+      "  shape: -1\n",
+      "  shape: 16\n",
+      "}\n",
+      "outputs {\n",
+      "  name: \"OUTPUT1\"\n",
+      "  datatype: \"INT32\"\n",
+      "  shape: -1\n",
+      "  shape: 16\n",
+      "}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_name = \"tfsimple1\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "print(\"model ready:\", grpc_triton_client.is_model_ready(model_name, headers=headers))\n",
+    "print(grpc_triton_client.get_model_metadata(model_name, headers=headers))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Against Model\n",
+    "\n",
+    "model_name = \"tfsimple1\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "inputs = [\n",
+    "    grpcclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "    grpcclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"))\n",
+    "inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"))\n",
+    "\n",
+    "outputs = [grpcclient.InferRequestedOutput(\"OUTPUT0\")]\n",
+    "\n",
+    "\n",
+    "result = grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)\n",
+    "result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Against Pipeline\n",
+    "\n",
+    "model_name = \"tfsimple.pipeline\"\n",
+    "headers = {\"seldon-model\": model_name}\n",
+    "\n",
+    "inputs = [\n",
+    "    grpcclient.InferInput(\"INPUT0\", (1, 16), \"INT32\"),\n",
+    "    grpcclient.InferInput(\"INPUT1\", (1, 16), \"INT32\"),\n",
+    "]\n",
+    "inputs[0].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"))\n",
+    "inputs[1].set_data_from_numpy(np.arange(1, 17).reshape(-1, 16).astype(\"int32\"))\n",
+    "\n",
+    "outputs = [grpcclient.InferRequestedOutput(\"OUTPUT0\")]\n",
+    "\n",
+    "\n",
+    "result = grpc_triton_client.infer(model_name, inputs, outputs=outputs, headers=headers)\n",
+    "result.as_numpy(\"OUTPUT0\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"iris\" deleted\n",
+      "pipeline.mlops.seldon.io \"iris-pipeline\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f models/sklearn-iris-gs.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f pipelines/iris.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"tfsimple1\" deleted\n",
+      "pipeline.mlops.seldon.io \"tfsimple\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f models/tfsimple1.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f pipelines/tfsimple.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f90d0a8b54f81c3642b2ee3336e1de55986a3a982587eefc0621499dbdd4a3fd"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/examples/notebooks/local-examples.ipynb b/docs/examples/notebooks/local-examples.ipynb
new file mode 100644
index 0000000000..e920dd816d
--- /dev/null
+++ b/docs/examples/notebooks/local-examples.ipynb
@@ -0,0 +1,1052 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Non Kubernetes Local Examples\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b1925df",
+   "metadata": {},
+   "source": [
+    "### SKLearn Model\n",
+    "\n",
+    "We use a simple sklearn iris classification model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8b4be911",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn-iris-gs.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26c11833",
+   "metadata": {},
+   "source": [
+    "Load the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "checked-cream",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/sklearn-iris-gs.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccd73e1c",
+   "metadata": {},
+   "source": [
+    "Wait for the model to be ready"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "interracial-secret",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status iris -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83a3583c",
+   "metadata": {},
+   "source": [
+    "Do a REST inference call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "upper-cholesterol",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"983bd95f-4b4d-4ff1-95b2-df9d6d089164\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58057385",
+   "metadata": {},
+   "source": [
+    "Do a gRPC inference call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "right-talent",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"iris_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"predict\",\r\n",
+      "      \"datatype\": \"INT64\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"1\"\r\n",
+      "      ],\r\n",
+      "      \"parameters\": {\r\n",
+      "        \"content_type\": {\r\n",
+      "          \"stringParam\": \"np\"\r\n",
+      "        }\r\n",
+      "      },\r\n",
+      "      \"contents\": {\r\n",
+      "        \"int64Contents\": [\r\n",
+      "          \"2\"\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-mode grpc \\\n",
+    "   '{\"model_name\":\"iris\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d95a5f03",
+   "metadata": {},
+   "source": [
+    "Unload the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ceramic-illness",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload iris"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30431134",
+   "metadata": {},
+   "source": [
+    "### Tensorflow Model\n",
+    "\n",
+    "We run a simple tensorflow model. Note the requirements section specifying `tensorflow`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "34165288",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple1\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\r\n",
+      "  requirements:\r\n",
+      "  - tensorflow\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "275a8445",
+   "metadata": {},
+   "source": [
+    "Load the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "726f2dd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b141e73",
+   "metadata": {},
+   "source": [
+    "Wait for the model to be ready."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2d9b631f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88301952",
+   "metadata": {},
+   "source": [
+    "Get model metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f812f531",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"name\": \"tfsimple1_1\",\r\n",
+      "\t\"versions\": [\r\n",
+      "\t\t\"1\"\r\n",
+      "\t],\r\n",
+      "\t\"platform\": \"tensorflow_graphdef\",\r\n",
+      "\t\"inputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"INPUT0\",\r\n",
+      "\t\t\t\"datatype\": \"INT32\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t-1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t]\r\n",
+      "\t\t},\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"INPUT1\",\r\n",
+      "\t\t\t\"datatype\": \"INT32\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t-1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t],\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"OUTPUT0\",\r\n",
+      "\t\t\t\"datatype\": \"INT32\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t-1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t]\r\n",
+      "\t\t},\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"OUTPUT1\",\r\n",
+      "\t\t\t\"datatype\": \"INT32\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t-1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model metadata tfsimple1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77f85f7d",
+   "metadata": {},
+   "source": [
+    "Do a REST inference call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7abd240a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"tfsimple1_1\",\r\n",
+      "  \"model_version\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"data\": [\r\n",
+      "        2,\r\n",
+      "        4,\r\n",
+      "        6,\r\n",
+      "        8,\r\n",
+      "        10,\r\n",
+      "        12,\r\n",
+      "        14,\r\n",
+      "        16,\r\n",
+      "        18,\r\n",
+      "        20,\r\n",
+      "        22,\r\n",
+      "        24,\r\n",
+      "        26,\r\n",
+      "        28,\r\n",
+      "        30,\r\n",
+      "        32\r\n",
+      "      ]\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"data\": [\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0\r\n",
+      "      ]\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer tfsimple1 \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34a1f004",
+   "metadata": {},
+   "source": [
+    "Do a gRPC inference call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f86c494a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"modelName\": \"tfsimple1_1\",\r\n",
+      "  \"modelVersion\": \"1\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0,\r\n",
+      "          0\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer tfsimple1 --inference-mode grpc \\\n",
+    "    '{\"model_name\":\"tfsimple1\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d32d3c0",
+   "metadata": {},
+   "source": [
+    "Unload the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f7405940",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload tfsimple1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amber-inclusion",
+   "metadata": {},
+   "source": [
+    "### Experiment\n",
+    "\n",
+    "We will use two SKlearn Iris classification models to illustrate an experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "dfc5a4fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn1.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8837f315",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris2\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn2.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "359b456e",
+   "metadata": {},
+   "source": [
+    "Load both models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "elect-samba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/sklearn1.yaml\n",
+    "!seldon model load -f ./models/sklearn2.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1aaa2b8f",
+   "metadata": {},
+   "source": [
+    "Wait for both models to be ready."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "floral-moisture",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"modelName\": \"iris\",\n",
+      "  \"versions\": [\n",
+      "    {\n",
+      "      \"version\": 1,\n",
+      "      \"serverName\": \"mlserver\",\n",
+      "      \"kubernetesMeta\": {},\n",
+      "      \"modelReplicaState\": {\n",
+      "        \"0\": {\n",
+      "          \"state\": \"Available\",\n",
+      "          \"lastChangeTimestamp\": \"2023-06-29T14:01:41.362720538Z\"\n",
+      "        }\n",
+      "      },\n",
+      "      \"state\": {\n",
+      "        \"state\": \"ModelAvailable\",\n",
+      "        \"availableReplicas\": 1,\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T14:01:41.362720538Z\"\n",
+      "      },\n",
+      "      \"modelDefn\": {\n",
+      "        \"meta\": {\n",
+      "          \"name\": \"iris\",\n",
+      "          \"kubernetesMeta\": {}\n",
+      "        },\n",
+      "        \"modelSpec\": {\n",
+      "          \"uri\": \"gs://seldon-models/mlserver/iris\",\n",
+      "          \"requirements\": [\n",
+      "            \"sklearn\"\n",
+      "          ]\n",
+      "        },\n",
+      "        \"deploymentSpec\": {\n",
+      "          \"replicas\": 1\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  ]\n",
+      "}\n",
+      "{\n",
+      "  \"modelName\": \"iris2\",\n",
+      "  \"versions\": [\n",
+      "    {\n",
+      "      \"version\": 1,\n",
+      "      \"serverName\": \"mlserver\",\n",
+      "      \"kubernetesMeta\": {},\n",
+      "      \"modelReplicaState\": {\n",
+      "        \"0\": {\n",
+      "          \"state\": \"Available\",\n",
+      "          \"lastChangeTimestamp\": \"2023-06-29T14:01:41.362845079Z\"\n",
+      "        }\n",
+      "      },\n",
+      "      \"state\": {\n",
+      "        \"state\": \"ModelAvailable\",\n",
+      "        \"availableReplicas\": 1,\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T14:01:41.362845079Z\"\n",
+      "      },\n",
+      "      \"modelDefn\": {\n",
+      "        \"meta\": {\n",
+      "          \"name\": \"iris2\",\n",
+      "          \"kubernetesMeta\": {}\n",
+      "        },\n",
+      "        \"modelSpec\": {\n",
+      "          \"uri\": \"gs://seldon-models/mlserver/iris\",\n",
+      "          \"requirements\": [\n",
+      "            \"sklearn\"\n",
+      "          ]\n",
+      "        },\n",
+      "        \"deploymentSpec\": {\n",
+      "          \"replicas\": 1\n",
+      "        }\n",
+      "      }\n",
+      "    }\n",
+      "  ]\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status iris | jq -M .\n",
+    "!seldon model status iris2 | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf1ac158",
+   "metadata": {},
+   "source": [
+    "Create an experiment that modifies the iris model to add a second model splitting traffic 50/50 between the two."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "10fe4447",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: experiment-sample\r\n",
+      "spec:\r\n",
+      "  default: iris\r\n",
+      "  candidates:\r\n",
+      "  - name: iris\r\n",
+      "    weight: 50\r\n",
+      "  - name: iris2\r\n",
+      "    weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/ab-default-model.yaml "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c9b215b",
+   "metadata": {},
+   "source": [
+    "Start the experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "differential-array",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment start -f ./experiments/ab-default-model.yaml "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "727e4fd4",
+   "metadata": {},
+   "source": [
+    "Wait for the experiment to be ready."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "overhead-banks",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"experimentName\": \"experiment-sample\",\r\n",
+      "  \"active\": true,\r\n",
+      "  \"candidatesReady\": true,\r\n",
+      "  \"mirrorReady\": true,\r\n",
+      "  \"statusDescription\": \"experiment active\",\r\n",
+      "  \"kubernetesMeta\": {}\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment status experiment-sample -w | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efe293b0",
+   "metadata": {},
+   "source": [
+    "Run a set of calls and record which route the traffic took. There should be roughly a 50/50 split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "finite-significance",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::57 :iris_1::43]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 100 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30fe1b9f",
+   "metadata": {},
+   "source": [
+    "Run one more request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "85357805",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"fa425bdf-737c-41fe-894d-58868f70fe5d\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7ca1665",
+   "metadata": {},
+   "source": [
+    "Use sticky session key passed by last infer request to ensure same route is taken each time. \n",
+    "We will test REST and gRPC."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1ffb00b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -s -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "81a10627",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-mode grpc -s -i 50\\\n",
+    "   '{\"model_name\":\"iris\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12ad0016",
+   "metadata": {},
+   "source": [
+    "Stop the experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "downtown-construction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment stop experiment-sample"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5264ed4",
+   "metadata": {},
+   "source": [
+    "Show the requests all go to original model now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "b085568e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::100]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 100 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9da648e8",
+   "metadata": {},
+   "source": [
+    "Unload both models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "077bbce8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload iris\n",
+    "!seldon model unload iris2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3720658f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/local-experiments.ipynb b/docs/examples/notebooks/local-experiments.ipynb
new file mode 100644
index 0000000000..c669ad53ea
--- /dev/null
+++ b/docs/examples/notebooks/local-experiments.ipynb
@@ -0,0 +1,1910 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Non Kubernetes Local Experiment Examples\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "amber-inclusion",
+   "metadata": {},
+   "source": [
+    "### Model Experiment\n",
+    "\n",
+    "We will use two SKlearn Iris classification models to illustrate experiments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dfc5a4fd",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn1.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8837f315",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris2\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn2.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "359b456e",
+   "metadata": {},
+   "source": [
+    "Load both models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "elect-samba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/sklearn1.yaml\n",
+    "!seldon model load -f ./models/sklearn2.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1aaa2b8f",
+   "metadata": {},
+   "source": [
+    "Wait for both models to be ready."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "floral-moisture",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status iris -w ModelAvailable\n",
+    "!seldon model status iris2 -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0a7cfc57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "570c4769",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris2 -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf1ac158",
+   "metadata": {},
+   "source": [
+    "Create an experiment that modifies the iris model to add a second model splitting traffic 50/50 between the two."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "10fe4447",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: experiment-sample\r\n",
+      "spec:\r\n",
+      "  default: iris\r\n",
+      "  candidates:\r\n",
+      "  - name: iris\r\n",
+      "    weight: 50\r\n",
+      "  - name: iris2\r\n",
+      "    weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/ab-default-model.yaml "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c9b215b",
+   "metadata": {},
+   "source": [
+    "Start the experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "differential-array",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment start -f ./experiments/ab-default-model.yaml "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "727e4fd4",
+   "metadata": {},
+   "source": [
+    "Wait for the experiment to be ready."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "overhead-banks",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"experimentName\": \"experiment-sample\",\r\n",
+      "  \"active\": true,\r\n",
+      "  \"candidatesReady\": true,\r\n",
+      "  \"mirrorReady\": true,\r\n",
+      "  \"statusDescription\": \"experiment active\",\r\n",
+      "  \"kubernetesMeta\": {}\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment status experiment-sample -w | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efe293b0",
+   "metadata": {},
+   "source": [
+    "Run a set of calls and record which route the traffic took. There should be roughly a 50/50 split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "finite-significance",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris2_1::27 :iris_1::23]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01ac708c",
+   "metadata": {},
+   "source": [
+    "Show sticky session header `x-seldon-route` that is returned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "85357805",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> POST /v2/models/iris/infer HTTP/1.1\r\n",
+      "> Host: localhost:9000\r\n",
+      "> Content-Type:[application/json]\r\n",
+      "> Seldon-Model:[iris]\r\n",
+      "\r\n",
+      "< X-Seldon-Route:[:iris_1:]\r\n",
+      "< Ce-Id:[463e96ad-645f-4442-8890-4c340b58820b]\r\n",
+      "< Traceparent:[00-fe9e87fcbe4be98ed82fb76166e15ceb-d35e7ac96bd8b718-01]\r\n",
+      "< X-Envoy-Upstream-Service-Time:[3]\r\n",
+      "< Ce-Specversion:[0.3]\r\n",
+      "< Date:[Thu, 29 Jun 2023 14:03:03 GMT]\r\n",
+      "< Ce-Source:[io.seldon.serving.deployment.mlserver]\r\n",
+      "< Content-Type:[application/json]\r\n",
+      "< Server:[envoy]\r\n",
+      "< X-Request-Id:[cieou5ofh5ss73fbjdu0]\r\n",
+      "< Ce-Endpoint:[iris_1]\r\n",
+      "< Ce-Modelid:[iris_1]\r\n",
+      "< Ce-Type:[io.seldon.serving.inference.response]\r\n",
+      "< Content-Length:[213]\r\n",
+      "< Ce-Inferenceservicename:[mlserver]\r\n",
+      "< Ce-Requestid:[463e96ad-645f-4442-8890-4c340b58820b]\r\n",
+      "\r\n",
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"463e96ad-645f-4442-8890-4c340b58820b\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"parameters\": {\r\n",
+      "\t\t\t\t\"content_type\": \"np\"\r\n",
+      "\t\t\t},\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --show-headers \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7ca1665",
+   "metadata": {},
+   "source": [
+    "Use sticky session key passed by last infer request to ensure same route is taken each time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1ffb00b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -s -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "81a10627",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-mode grpc -s -i 50\\\n",
+    "   '{\"model_name\":\"iris\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12ad0016",
+   "metadata": {},
+   "source": [
+    "Stop the experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "downtown-construction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment stop experiment-sample"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9da648e8",
+   "metadata": {},
+   "source": [
+    "Unload both models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "077bbce8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload iris\n",
+    "!seldon model unload iris2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "819d5b28",
+   "metadata": {},
+   "source": [
+    "### Pipeline Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "aee426cd",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: add10\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/add10\"\r\n",
+      "  requirements:\r\n",
+      "  - triton\r\n",
+      "  - python\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/add10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3720658f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: mul10\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/mul10\"\r\n",
+      "  requirements:\r\n",
+      "  - triton\r\n",
+      "  - python\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e1a8c098",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/add10.yaml\n",
+    "!seldon model load -f ./models/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "46ac6ee1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status add10 -w ModelAvailable\n",
+    "!seldon model status mul10 -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "44cae93b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: pipeline-mul10\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: mul10\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - mul10\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "64780610",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: pipeline-add10\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: add10\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - add10\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/add10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "18bbd7e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/add10.yaml\n",
+    "!seldon pipeline load -f ./pipelines/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "2e82b63b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"pipelineName\":\"pipeline-add10\", \"versions\":[{\"pipeline\":{\"name\":\"pipeline-add10\", \"uid\":\"cieov47l80lc739juklg\", \"version\":1, \"steps\":[{\"name\":\"add10\"}], \"output\":{\"steps\":[\"add10.outputs\"]}, \"kubernetesMeta\":{}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T14:05:04.460868091Z\", \"modelsReady\":true}}]}\n",
+      "{\"pipelineName\":\"pipeline-mul10\", \"versions\":[{\"pipeline\":{\"name\":\"pipeline-mul10\", \"uid\":\"cieov47l80lc739jukm0\", \"version\":1, \"steps\":[{\"name\":\"mul10\"}], \"output\":{\"steps\":[\"mul10.outputs\"]}, \"kubernetesMeta\":{}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T14:05:04.631980330Z\", \"modelsReady\":true}}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status pipeline-add10 -w PipelineReady \n",
+    "!seldon pipeline status pipeline-mul10 -w PipelineReady "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "67c8cedd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M . "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a549a962",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          10,\r\n",
+      "          20,\r\n",
+      "          30,\r\n",
+      "          40\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-mul10 --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1153547d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: addmul10\r\n",
+      "spec:\r\n",
+      "  default: pipeline-add10\r\n",
+      "  resourceType: pipeline\r\n",
+      "  candidates:\r\n",
+      "  - name: pipeline-add10\r\n",
+      "    weight: 50\r\n",
+      "  - name: pipeline-mul10\r\n",
+      "    weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/addmul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e3229c09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment start -f ./experiments/addmul10.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "1c4dbd59",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"experimentName\": \"addmul10\",\r\n",
+      "  \"active\": true,\r\n",
+      "  \"candidatesReady\": true,\r\n",
+      "  \"mirrorReady\": true,\r\n",
+      "  \"statusDescription\": \"experiment active\",\r\n",
+      "  \"kubernetesMeta\": {}\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment status addmul10 -w | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "20ba8a38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:add10_1::28 :mul10_1::22 :pipeline-add10.pipeline::28 :pipeline-mul10.pipeline::22]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 -i 50 --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b928d9b7",
+   "metadata": {},
+   "source": [
+    "Use sticky session key passed by last infer request to ensure same route is taken each time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2f2a3a27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> /inference.GRPCInferenceService/ModelInfer HTTP/2\r\n",
+      "> Host: localhost:9000\r\n",
+      "> seldon-model:[pipeline-add10.pipeline]\r\n",
+      "\r\n",
+      "< x-envoy-expected-rq-timeout-ms:[60000]\r\n",
+      "< x-request-id:[cieov8ofh5ss739277i0]\r\n",
+      "< date:[Thu, 29 Jun 2023 14:05:23 GMT]\r\n",
+      "< server:[envoy]\r\n",
+      "< content-type:[application/grpc]\r\n",
+      "< x-envoy-upstream-service-time:[6]\r\n",
+      "< x-seldon-route:[:add10_1: :pipeline-add10.pipeline:]\r\n",
+      "< x-forwarded-proto:[http]\r\n",
+      "\r\n",
+      "{\"outputs\":[{\"name\":\"OUTPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[11, 12, 13, 14]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 --show-headers --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "082c6bb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> /inference.GRPCInferenceService/ModelInfer HTTP/2\r\n",
+      "> Host: localhost:9000\r\n",
+      "> x-seldon-route:[:add10_1: :pipeline-add10.pipeline:]\r\n",
+      "> seldon-model:[pipeline-add10.pipeline]\r\n",
+      "\r\n",
+      "< content-type:[application/grpc]\r\n",
+      "< x-forwarded-proto:[http]\r\n",
+      "< x-envoy-expected-rq-timeout-ms:[60000]\r\n",
+      "< x-seldon-route:[:add10_1: :pipeline-add10.pipeline: :pipeline-add10.pipeline:]\r\n",
+      "< x-request-id:[cieov90fh5ss739277ig]\r\n",
+      "< x-envoy-upstream-service-time:[7]\r\n",
+      "< date:[Thu, 29 Jun 2023 14:05:24 GMT]\r\n",
+      "< server:[envoy]\r\n",
+      "\r\n",
+      "{\"outputs\":[{\"name\":\"OUTPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[11, 12, 13, 14]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 -s --show-headers --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "d70f0acf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:add10_1::50 :pipeline-add10.pipeline::150]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 -s -i 50 --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "dedfbed5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: add20\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/triton/add20\"\r\n",
+      "  requirements:\r\n",
+      "  - triton\r\n",
+      "  - python\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/add20.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "1436e721",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/add20.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "9d010fd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status add20 -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "09094919",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: add1020\r\n",
+      "spec:\r\n",
+      "  default: add10\r\n",
+      "  candidates:\r\n",
+      "  - name: add10\r\n",
+      "    weight: 50\r\n",
+      "  - name: add20\r\n",
+      "    weight: 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/add1020.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "4547d936",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment start -f ./experiments/add1020.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "fba5dce6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"experimentName\": \"add1020\",\r\n",
+      "  \"active\": true,\r\n",
+      "  \"candidatesReady\": true,\r\n",
+      "  \"mirrorReady\": true,\r\n",
+      "  \"statusDescription\": \"experiment active\",\r\n",
+      "  \"kubernetesMeta\": {}\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment status add1020 -w | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "8f089362",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:add10_1::22 :add20_1::28]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer add10 -i 50  --inference-mode grpc \\\n",
+    "  '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "66d7fa6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:add10_1::24 :add20_1::32 :mul10_1::44 :pipeline-add10.pipeline::56 :pipeline-mul10.pipeline::44]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 -i 100 --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "c0f22776",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> /inference.GRPCInferenceService/ModelInfer HTTP/2\r\n",
+      "> Host: localhost:9000\r\n",
+      "> seldon-model:[pipeline-add10.pipeline]\r\n",
+      "\r\n",
+      "< x-request-id:[cieovf0fh5ss739279u0]\r\n",
+      "< x-envoy-upstream-service-time:[5]\r\n",
+      "< x-seldon-route:[:add10_1: :pipeline-add10.pipeline:]\r\n",
+      "< date:[Thu, 29 Jun 2023 14:05:48 GMT]\r\n",
+      "< server:[envoy]\r\n",
+      "< content-type:[application/grpc]\r\n",
+      "< x-forwarded-proto:[http]\r\n",
+      "< x-envoy-expected-rq-timeout-ms:[60000]\r\n",
+      "\r\n",
+      "{\"outputs\":[{\"name\":\"OUTPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[11, 12, 13, 14]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 --show-headers --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "774e4373",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> /inference.GRPCInferenceService/ModelInfer HTTP/2\r\n",
+      "> Host: localhost:9000\r\n",
+      "> x-seldon-route:[:add10_1: :pipeline-add10.pipeline:]\r\n",
+      "> seldon-model:[pipeline-add10.pipeline]\r\n",
+      "\r\n",
+      "< x-forwarded-proto:[http]\r\n",
+      "< x-envoy-expected-rq-timeout-ms:[60000]\r\n",
+      "< x-request-id:[cieovf8fh5ss739279ug]\r\n",
+      "< x-envoy-upstream-service-time:[6]\r\n",
+      "< date:[Thu, 29 Jun 2023 14:05:49 GMT]\r\n",
+      "< server:[envoy]\r\n",
+      "< content-type:[application/grpc]\r\n",
+      "< x-seldon-route:[:add10_1: :pipeline-add10.pipeline: :add20_1: :pipeline-add10.pipeline:]\r\n",
+      "\r\n",
+      "{\"outputs\":[{\"name\":\"OUTPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[21, 22, 23, 24]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 -s --show-headers --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "9f3f766e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment stop addmul10\n",
+    "!seldon experiment stop add1020\n",
+    "!seldon pipeline unload pipeline-add10\n",
+    "!seldon pipeline unload pipeline-mul10\n",
+    "!seldon model unload add10\n",
+    "!seldon model unload add20\n",
+    "!seldon model unload mul10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72b16bbc",
+   "metadata": {},
+   "source": [
+    "### Model Mirror Experiment\n",
+    "\n",
+    "We will use two SKlearn Iris classification models to illustrate a model with a mirror."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "20f19234",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn1.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "40ba5a74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris2\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/mlserver/iris\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn2.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "328fdaf6",
+   "metadata": {},
+   "source": [
+    "Load both models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "b4ec8d19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/sklearn1.yaml\n",
+    "!seldon model load -f ./models/sklearn2.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed848e0b",
+   "metadata": {},
+   "source": [
+    "Wait for both models to be ready."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "115699a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status iris -w ModelAvailable\n",
+    "!seldon model status iris2 -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c58bb91",
+   "metadata": {},
+   "source": [
+    "Create an experiment that modifies in which we mirror traffic to iris also to iris2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "632b4a58",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: sklearn-mirror\r\n",
+      "spec:\r\n",
+      "  default: iris\r\n",
+      "  candidates:\r\n",
+      "  - name: iris\r\n",
+      "    weight: 100\r\n",
+      "  mirror:\r\n",
+      "    name: iris2\r\n",
+      "    percent: 100\r\n",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/sklearn-mirror.yaml "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f83c88c",
+   "metadata": {},
+   "source": [
+    "Start the experiment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "2466a9b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment start -f ./experiments/sklearn-mirror.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e8e4e63",
+   "metadata": {},
+   "source": [
+    "Wait for the experiment to be ready."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "0ac0054e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"experimentName\": \"sklearn-mirror\",\r\n",
+      "  \"active\": true,\r\n",
+      "  \"candidatesReady\": true,\r\n",
+      "  \"mirrorReady\": true,\r\n",
+      "  \"statusDescription\": \"experiment active\",\r\n",
+      "  \"kubernetesMeta\": {}\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment status sklearn-mirror -w | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df95e80d",
+   "metadata": {},
+   "source": [
+    "We get responses from iris but all requests would also have been mirrored to iris2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "81c6023d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e445bb7",
+   "metadata": {},
+   "source": [
+    "We can check the local prometheus port from the agent to validate requests went to iris2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "35a0f1f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon_model_infer_total{code=\"200\",method_type=\"rest\",model=\"iris\",model_internal=\"iris2_1\",server=\"mlserver\",server_replica=\"0\"} 50\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -s 0.0.0:9006/metrics | grep seldon_model_infer_total | grep iris2_1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3dee8fd1",
+   "metadata": {},
+   "source": [
+    "Stop the experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "e777b0c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment stop sklearn-mirror"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0af10e93",
+   "metadata": {},
+   "source": [
+    "Unload both models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "44085e38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload iris\n",
+    "!seldon model unload iris2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "006bed5c",
+   "metadata": {},
+   "source": [
+    "## Pipeline Mirror Experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "01bf3aa8",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: add10\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/add10\"\r\n",
+      "  requirements:\r\n",
+      "  - triton\r\n",
+      "  - python\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/add10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "be5c5426",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: mul10\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/mul10\"\r\n",
+      "  requirements:\r\n",
+      "  - triton\r\n",
+      "  - python\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "54ac44c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/add10.yaml\n",
+    "!seldon model load -f ./models/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "78debf8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status add10 -w ModelAvailable\n",
+    "!seldon model status mul10 -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "c04346fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: pipeline-mul10\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: mul10\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - mul10\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "0aa43131",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: pipeline-add10\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: add10\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - add10\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/add10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "8bb044d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/add10.yaml\n",
+    "!seldon pipeline load -f ./pipelines/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "cd5c869f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"pipelineName\":\"pipeline-add10\", \"versions\":[{\"pipeline\":{\"name\":\"pipeline-add10\", \"uid\":\"ciep072i8ufs73flaipg\", \"version\":1, \"steps\":[{\"name\":\"add10\"}], \"output\":{\"steps\":[\"add10.outputs\"]}, \"kubernetesMeta\":{}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T14:07:24.903503109Z\", \"modelsReady\":true}}]}\n",
+      "{\"pipelineName\":\"pipeline-mul10\", \"versions\":[{\"pipeline\":{\"name\":\"pipeline-mul10\", \"uid\":\"ciep072i8ufs73flaiq0\", \"version\":1, \"steps\":[{\"name\":\"mul10\"}], \"output\":{\"steps\":[\"mul10.outputs\"]}, \"kubernetesMeta\":{}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T14:07:25.082642153Z\", \"modelsReady\":true}}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status pipeline-add10 -w PipelineReady \n",
+    "!seldon pipeline status pipeline-mul10 -w PipelineReady "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "9c2b666e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"outputs\":[{\"name\":\"OUTPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[11, 12, 13, 14]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "ef844ae7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"outputs\":[{\"name\":\"OUTPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[10, 20, 30, 40]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-mul10 --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "3a9f756f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Experiment\r\n",
+      "metadata:\r\n",
+      "  name: addmul10-mirror\r\n",
+      "spec:\r\n",
+      "  default: pipeline-add10\r\n",
+      "  resourceType: pipeline\r\n",
+      "  candidates:\r\n",
+      "  - name: pipeline-add10\r\n",
+      "    weight: 100\r\n",
+      "  mirror:\r\n",
+      "    name: pipeline-mul10\r\n",
+      "    percent: 100\r\n",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./experiments/addmul10-mirror.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "3777e310",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment start -f ./experiments/addmul10-mirror.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "63d087f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"experimentName\": \"addmul10-mirror\",\r\n",
+      "  \"active\": true,\r\n",
+      "  \"candidatesReady\": true,\r\n",
+      "  \"mirrorReady\": true,\r\n",
+      "  \"statusDescription\": \"experiment active\",\r\n",
+      "  \"kubernetesMeta\": {}\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon experiment status addmul10-mirror -w | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "319ef08f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"outputs\":[{\"name\":\"OUTPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[11, 12, 13, 14]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 -i 1 --inference-mode grpc \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23eb87d0",
+   "metadata": {},
+   "source": [
+    "Let's check that the mul10 model was called."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "086359f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon_model_infer_total{code=\"OK\",method_type=\"grpc\",model=\"mul10\",model_internal=\"mul10_1\",server=\"triton\",server_replica=\"0\"} 2\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -s 0.0.0:9007/metrics | grep seldon_model_infer_total | grep mul10_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "25d03bc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon_model_infer_total{code=\"OK\",method_type=\"grpc\",model=\"add10\",model_internal=\"add10_1\",server=\"triton\",server_replica=\"0\"} 2\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -s 0.0.0:9007/metrics | grep seldon_model_infer_total | grep add10_1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bacaccb",
+   "metadata": {},
+   "source": [
+    "Let's do an http call and check agaib the two models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "94d784bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"\",\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t11,\r\n",
+      "\t\t\t\t12,\r\n",
+      "\t\t\t\t13,\r\n",
+      "\t\t\t\t14\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t4\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"FP32\"\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-add10 -i 1 \\\n",
+    " '{\"model_name\":\"add10\",\"inputs\":[{\"name\":\"INPUT\",\"data\":[1,2,3,4],\"datatype\":\"FP32\",\"shape\":[4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "509efbcb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon_model_infer_total{code=\"OK\",method_type=\"grpc\",model=\"mul10\",model_internal=\"mul10_1\",server=\"triton\",server_replica=\"0\"} 3\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -s 0.0.0:9007/metrics | grep seldon_model_infer_total | grep mul10_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "d4d86a74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon_model_infer_total{code=\"OK\",method_type=\"grpc\",model=\"add10\",model_internal=\"add10_1\",server=\"triton\",server_replica=\"0\"} 3\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -s 0.0.0:9007/metrics | grep seldon_model_infer_total | grep add10_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "eb8be18f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.mul10.inputs\tciep0bofh5ss73dpdiq0\t{\"inputs\":[{\"name\":\"INPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[1, 2, 3, 4]}}]}\r\n",
+      "seldon.default.model.mul10.outputs\tciep0bofh5ss73dpdiq0\t{\"modelName\":\"mul10_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[10, 20, 30, 40]}}]}\r\n",
+      "seldon.default.pipeline.pipeline-mul10.inputs\tciep0bofh5ss73dpdiq0\t{\"inputs\":[{\"name\":\"INPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[1, 2, 3, 4]}}]}\r\n",
+      "seldon.default.pipeline.pipeline-mul10.outputs\tciep0bofh5ss73dpdiq0\t{\"outputs\":[{\"name\":\"OUTPUT\", \"datatype\":\"FP32\", \"shape\":[\"4\"], \"contents\":{\"fp32Contents\":[10, 20, 30, 40]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect pipeline-mul10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "c84bad04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon experiment stop addmul10-mirror\n",
+    "!seldon pipeline unload pipeline-add10\n",
+    "!seldon pipeline unload pipeline-mul10\n",
+    "!seldon model unload add10\n",
+    "!seldon model unload mul10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc4a9b2e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/local-over-commit-test.ipynb b/docs/examples/notebooks/local-over-commit-test.ipynb
new file mode 100644
index 0000000000..1d551b475d
--- /dev/null
+++ b/docs/examples/notebooks/local-over-commit-test.ipynb
@@ -0,0 +1,1849 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "forced-trading",
+   "metadata": {},
+   "source": [
+    "# Multimodel serving with over-commit example\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5745880c",
+   "metadata": {},
+   "source": [
+    "Note: this notebook requires access to internal services, so either `make start-all-host` (under `scheduler` sub directory) or expose the relevant ports."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5ebabe7",
+   "metadata": {},
+   "source": [
+    "## `iris` model on `MLServer`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e52febfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: INFER_ENDPOINT=0.0.0.0:9000\n",
+      "env: SCHEDULER_ENDPOINT=0.0.0.0:9004\n",
+      "env: MLSERVER_DEBUG=0.0.0.0:7777\n",
+      "env: TRITON_DEBUG=0.0.0.0:7778\n"
+     ]
+    }
+   ],
+   "source": [
+    "%env INFER_ENDPOINT=0.0.0.0:9000\n",
+    "%env SCHEDULER_ENDPOINT=0.0.0.0:9004\n",
+    "%env MLSERVER_DEBUG=0.0.0.0:7777\n",
+    "%env TRITON_DEBUG=0.0.0.0:7778"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebf5eb28",
+   "metadata": {},
+   "source": [
+    "By default if running locally there is 1 replica of `mlserver` with memory slots up to 10MB and 20% overcommit budget. We will load 11 `iris` models each requiring 1MB worth of memory slots as an example. These numbers allow for 10 models to be active at the same time and 1 model to be evicted to disk.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "unusual-mills",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading model iris1\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model iris2\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model iris3\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model iris4\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model iris5\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model iris6\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model iris7\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model iris8\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model iris9\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model iris10\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model iris11\n",
+      "{\n",
+      "  \n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "for i in {1..11}; \n",
+    "do\n",
+    "\n",
+    "echo \"loading model iris$i\"\n",
+    "\n",
+    "data='{ \n",
+    "        \"model\":{ \n",
+    "            \"meta\": {\"name\":\"iris'\"$i\"'\"}, \n",
+    "            \"modelSpec\" : { \n",
+    "                \"uri\":\"gs://seldon-models/mlserver/iris\", \n",
+    "                \"requirements\":[\"sklearn\"],  \n",
+    "                \"memoryBytes\":1000000}, \n",
+    "            \"deploymentSpec\": {\"replicas\":1}\n",
+    "            }\n",
+    "      }'\n",
+    "\n",
+    "grpcurl -d \"$data\" \\\n",
+    "-plaintext \\\n",
+    "-import-path ../apis \\\n",
+    "-proto ../apis/mlops/scheduler/scheduler.proto \"$SCHEDULER_ENDPOINT\" seldon.mlops.scheduler.Scheduler/LoadModel\n",
+    "\n",
+    "sleep 0.05\n",
+    "done"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9faa0e33",
+   "metadata": {},
+   "source": [
+    "Get the list of models on this mlserver replica and whether they are loaded in main memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "sunset-theta",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"models\": [\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:45:40Z\",\n",
+      "      \"name\": \"iris10_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:44:48Z\",\n",
+      "      \"name\": \"iris3_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:44:48Z\",\n",
+      "      \"name\": \"iris6_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:44:48Z\",\n",
+      "      \"name\": \"iris1_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"iris11_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:44:48Z\",\n",
+      "      \"name\": \"iris9_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:44:48Z\",\n",
+      "      \"name\": \"iris7_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:44:48Z\",\n",
+      "      \"name\": \"iris8_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:44:48Z\",\n",
+      "      \"name\": \"iris5_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:44:48Z\",\n",
+      "      \"name\": \"iris4_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:44:48Z\",\n",
+      "      \"name\": \"iris2_1\"\n",
+      "    }\n",
+      "  ]\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "grpcurl -d '{}' \\\n",
+    "         -plaintext \\\n",
+    "         -import-path ../apis/ \\\n",
+    "         -proto ../apis/mlops/agent_debug/agent_debug.proto  ${MLSERVER_DEBUG} seldon.mlops.agent_debug.AgentDebugService/ReplicaStatus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "naval-geography",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All succeeded\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "for i in {1..11}; \n",
+    "do\n",
+    "\n",
+    "url=http://${INFER_ENDPOINT}/v2/models/iris${i}/infer \n",
+    "ret=`curl -s -o /dev/null -w \"%{http_code}\" \"${url}\" -H \"Content-Type: application/json\" \\\n",
+    "        -d '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}'`\n",
+    "if [ $ret -ne 200 ]; then\n",
+    "    echo \"Failed with code ${ret}\"\n",
+    "    exit\n",
+    "fi\n",
+    "\n",
+    "done\n",
+    "echo \"All succeeded\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "552e62d9",
+   "metadata": {},
+   "source": [
+    "Doing inference for all models succeeds as swapping in and out models is handled automatically"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4b6359c6",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "for i in {1..10}; \n",
+    "do \n",
+    "\n",
+    "for j in {1..11};\n",
+    "do\n",
+    "curl -s -o /dev/null http://\"$INFER_ENDPOINT\"/v2/models/iris$j/infer -H \"Content-Type: application/json\"  \\\n",
+    "        -d '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' &\n",
+    "done\n",
+    "\n",
+    "done\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10b53afb",
+   "metadata": {},
+   "source": [
+    "Unload models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "entire-black",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "for i in {1..11};\n",
+    "do\n",
+    "\n",
+    "grpcurl -d '{\"model\": {\"name\" : \"iris'\"$i\"'\"}}' \\\n",
+    "         -plaintext \\\n",
+    "         -import-path ../apis/ \\\n",
+    "         -proto ../apis/mlops/scheduler/scheduler.proto \"$SCHEDULER_ENDPOINT\" seldon.mlops.scheduler.Scheduler/UnloadModel\n",
+    "\n",
+    "\n",
+    "done"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "417559e0",
+   "metadata": {},
+   "source": [
+    "## `tfsimple` model on `triton`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1302a2c5",
+   "metadata": {},
+   "source": [
+    "With `tfsimple` on `triton` we will reduce the memory slot required to 100KB, which will allow us to load at least 100 models on the server in memory. The remaining models (10) will have to be evicted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "suburban-victim",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading model tfsimple1\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple2\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple3\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple4\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple5\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple6\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple7\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple8\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple9\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple10\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple11\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple12\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple13\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple14\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple15\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple16\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple17\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple18\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple19\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple20\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple21\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple22\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple23\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple24\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple25\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple26\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple27\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple28\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple29\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple30\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple31\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple32\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple33\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple34\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple35\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple36\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple37\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple38\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple39\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple40\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple41\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple42\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple43\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple44\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple45\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple46\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple47\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple48\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple49\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple50\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple51\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple52\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple53\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple54\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple55\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple56\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple57\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple58\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple59\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple60\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple61\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple62\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple63\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple64\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple65\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple66\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple67\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple68\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple69\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple70\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple71\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple72\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple73\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple74\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple75\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple76\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple77\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple78\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple79\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple80\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple81\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple82\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple83\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple84\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple85\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple86\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple87\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple88\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple89\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple90\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple91\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple92\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple93\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple94\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple95\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple96\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple97\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple98\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple99\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple100\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple101\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple102\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple103\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple104\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple105\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple106\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple107\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple108\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple109\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "loading model tfsimple110\n",
+      "{\n",
+      "  \n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "for i in {1..110}; \n",
+    "do\n",
+    "\n",
+    "echo \"loading model tfsimple$i\"\n",
+    "\n",
+    "data='{ \n",
+    "        \"model\":{ \n",
+    "            \"meta\": {\"name\":\"tfsimple'\"$i\"'\"}, \n",
+    "            \"modelSpec\" : { \n",
+    "                \"uri\":\"gs://seldon-models/triton/simple\", \n",
+    "                \"requirements\":[\"tensorflow\"],  \n",
+    "                \"memoryBytes\":100000}, \n",
+    "            \"deploymentSpec\": {\"replicas\":1}\n",
+    "            }\n",
+    "      }'\n",
+    "\n",
+    "grpcurl -d \"$data\" \\\n",
+    "-plaintext \\\n",
+    "-import-path ../apis \\\n",
+    "-proto ../apis/mlops/scheduler/scheduler.proto \"$SCHEDULER_ENDPOINT\" seldon.mlops.scheduler.Scheduler/LoadModel\n",
+    "\n",
+    "sleep 0.05\n",
+    "done"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "3ec2e8ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All succeeded\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "for i in {1..110}; \n",
+    "do\n",
+    "\n",
+    "url=http://${INFER_ENDPOINT}/v2/models/tfsimple${i}/infer \n",
+    "ret=`curl -s -o /dev/null -w \"%{http_code}\" curl -s -o /dev/null -w \"%{http_code}\" \"${url}\" -H \"Content-Type: application/json\" \\\n",
+    "        -d '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}'`\n",
+    "if [ $ret -ne 200 ]; then\n",
+    "    echo \"Failed with code ${ret}\"\n",
+    "    exit\n",
+    "fi\n",
+    "done\n",
+    "echo \"All succeeded\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3682fea5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"models\": [\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple89_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple34_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple59_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple76_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple45_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple60_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"tfsimple8_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple19_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple31_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple85_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple107_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple27_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple51_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple91_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"tfsimple2_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple24_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple53_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple98_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple17_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple35_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple38_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple12_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple83_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple99_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple20_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple58_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple101_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple37_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple63_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple87_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple40_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple72_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple94_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"tfsimple4_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple80_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple95_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple26_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple48_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple55_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple75_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple100_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple41_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple43_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple56_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple73_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple74_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple21_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple42_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple82_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple86_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple97_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple106_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple108_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"tfsimple10_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple15_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple67_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple105_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"tfsimple7_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple22_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple61_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple25_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple54_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple64_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"tfsimple6_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple90_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple96_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple16_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple62_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple71_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple28_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple103_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"tfsimple5_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple93_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple14_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple69_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple84_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple36_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple104_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple70_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple110_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"tfsimple9_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple57_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple65_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple109_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple30_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple66_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple68_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple39_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple44_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple88_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple13_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple23_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple29_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple50_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple79_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"tfsimple3_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple18_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:29Z\",\n",
+      "      \"name\": \"tfsimple102_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple92_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple49_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple77_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple78_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"lastAccessed\": \"0001-01-01T00:00:00Z\",\n",
+      "      \"name\": \"tfsimple1_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple47_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:28Z\",\n",
+      "      \"name\": \"tfsimple81_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple11_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple33_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple46_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:27Z\",\n",
+      "      \"name\": \"tfsimple52_1\"\n",
+      "    },\n",
+      "    {\n",
+      "      \"state\": \"InMemory\",\n",
+      "      \"lastAccessed\": \"2023-01-20T18:48:26Z\",\n",
+      "      \"name\": \"tfsimple32_1\"\n",
+      "    }\n",
+      "  ]\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "grpcurl -d '{}' \\\n",
+    "         -plaintext \\\n",
+    "         -import-path ../apis/ \\\n",
+    "         -proto ../apis/mlops/agent_debug/agent_debug.proto  ${TRITON_DEBUG} seldon.mlops.agent_debug.AgentDebugService/ReplicaStatus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b73a8f61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n",
+      "{\n",
+      "  \n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "for i in {1..110};\n",
+    "do\n",
+    "\n",
+    "grpcurl -d '{\"model\": {\"name\" : \"tfsimple'\"$i\"'\"}}' \\\n",
+    "         -plaintext \\\n",
+    "         -import-path ../apis/ \\\n",
+    "         -proto ../apis/mlops/scheduler/scheduler.proto \"$SCHEDULER_ENDPOINT\" seldon.mlops.scheduler.Scheduler/UnloadModel\n",
+    "\n",
+    "\n",
+    "done"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a35cb6c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.12 ('seldon-core-v2-python-3.8')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "dc8b7b3d3b143757bd161269cf4ea949ead8c2ebc12155bae9e3cf57923f4a90"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/metrics.ipynb b/docs/examples/notebooks/metrics.ipynb
new file mode 100644
index 0000000000..4885632f8a
--- /dev/null
+++ b/docs/examples/notebooks/metrics.ipynb
@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Metric Examples\n",
+    "\n",
+    "This notebook tests the exposed Prometheus metrics of model and pipeline servers.\n",
+    "\n",
+    "Requires: `prometheus_client` and `requests` libraries.\n",
+    "See docs for full set of metrics available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2abedfc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mlserver_metrics_host=\"0.0.0.0:9006\"\n",
+    "triton_metrics_host=\"0.0.0.0:9007\"\n",
+    "pipeline_metrics_host=\"0.0.0.0:9009\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f93e80df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from prometheus_client.parser import text_string_to_metric_families\n",
+    "import requests\n",
+    "\n",
+    "def scrape_metrics(host):\n",
+    "    data = requests.get(f\"http://{host}/metrics\").text\n",
+    "    return {\n",
+    "        family.name: family for family in text_string_to_metric_families(data)\n",
+    "    }\n",
+    "\n",
+    "def print_sample(family, label, value):\n",
+    "    for sample in family.samples:\n",
+    "        if sample.labels[label] == value:\n",
+    "            print(sample)\n",
+    "\n",
+    "def get_model_infer_count(host, model_name):\n",
+    "    metrics = scrape_metrics(host)\n",
+    "    family = metrics[\"seldon_model_infer\"]\n",
+    "    print_sample(family, \"model\", model_name)\n",
+    "    \n",
+    "def get_pipeline_infer_count(host, pipeline_name):\n",
+    "    metrics = scrape_metrics(host)\n",
+    "    family = metrics[\"seldon_pipeline_infer\"]\n",
+    "    print_sample(family, \"pipeline\", pipeline_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2bad42e",
+   "metadata": {},
+   "source": [
+    "### MLServer Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "checked-cream",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/sklearn-iris-gs.yaml\n",
+    "!seldon model status iris -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "upper-cholesterol",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris -i 50 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "right-talent",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:iris_1::100]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris --inference-mode grpc -i 100 \\\n",
+    "   '{\"model_name\":\"iris\",\"inputs\":[{\"name\":\"input\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[1,4]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "77205b71",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample(name='seldon_model_infer_total', labels={'code': '200', 'method_type': 'rest', 'model': 'iris', 'model_internal': 'iris_1', 'server': 'mlserver', 'server_replica': '0'}, value=50.0, timestamp=None, exemplar=None)\n",
+      "Sample(name='seldon_model_infer_total', labels={'code': 'OK', 'method_type': 'grpc', 'model': 'iris', 'model_internal': 'iris_1', 'server': 'mlserver', 'server_replica': '0'}, value=100.0, timestamp=None, exemplar=None)\n"
+     ]
+    }
+   ],
+   "source": [
+    "get_model_infer_count(mlserver_metrics_host,\"iris\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ceramic-illness",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload iris"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30431134",
+   "metadata": {},
+   "source": [
+    "### Triton Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "275a8445",
+   "metadata": {},
+   "source": [
+    "Load the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "726f2dd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml\n",
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7abd240a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:tfsimple1_1::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer tfsimple1 -i 50\\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f86c494a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:tfsimple1_1::100]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer tfsimple1 --inference-mode grpc -i 100 \\\n",
+    "    '{\"model_name\":\"tfsimple1\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2f0f940f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample(name='seldon_model_infer_total', labels={'code': '200', 'method_type': 'rest', 'model': 'tfsimple1', 'model_internal': 'tfsimple1_1', 'server': 'triton', 'server_replica': '0'}, value=50.0, timestamp=None, exemplar=None)\n",
+      "Sample(name='seldon_model_infer_total', labels={'code': 'OK', 'method_type': 'grpc', 'model': 'tfsimple1', 'model_internal': 'tfsimple1_1', 'server': 'triton', 'server_replica': '0'}, value=100.0, timestamp=None, exemplar=None)\n"
+     ]
+    }
+   ],
+   "source": [
+    "get_model_infer_count(triton_metrics_host,\"tfsimple1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f7405940",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload tfsimple1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d07a12e",
+   "metadata": {},
+   "source": [
+    "### Pipeline\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d540b7f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{\"pipelineName\":\"tfsimples\", \"versions\":[{\"pipeline\":{\"name\":\"tfsimples\", \"uid\":\"cdqji39qa12c739ab3o0\", \"version\":2, \"steps\":[{\"name\":\"tfsimple1\"}, {\"name\":\"tfsimple2\", \"inputs\":[\"tfsimple1.outputs\"], \"tensorMap\":{\"tfsimple1.outputs.OUTPUT0\":\"INPUT0\", \"tfsimple1.outputs.OUTPUT1\":\"INPUT1\"}}], \"output\":{\"steps\":[\"tfsimple2.outputs\"]}, \"kubernetesMeta\":{}}, \"state\":{\"pipelineVersion\":2, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2022-11-16T19:25:01.255955114Z\"}}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model load -f ./models/tfsimple2.yaml\n",
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M .\n",
+    "!seldon pipeline load -f ./pipelines/tfsimples.yaml\n",
+    "!seldon pipeline status tfsimples -w PipelineReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1c78ca44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Success: map[:tfsimple1_1::50 :tfsimple2_1::50 :tfsimples.pipeline::50]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples -i 50 \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7f48b7c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sample(name='seldon_pipeline_infer_total', labels={'code': '200', 'method_type': 'rest', 'pipeline': 'tfsimples', 'server': 'pipeline-gateway'}, value=50.0, timestamp=None, exemplar=None)\n"
+     ]
+    }
+   ],
+   "source": [
+    "get_pipeline_infer_count(pipeline_metrics_host,\"tfsimples\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2a35f0ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2\n",
+    "!seldon pipeline unload tfsimples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "305f4ec9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/model-zoo.ipynb b/docs/examples/notebooks/model-zoo.ipynb
new file mode 100644
index 0000000000..7ba09899b4
--- /dev/null
+++ b/docs/examples/notebooks/model-zoo.ipynb
@@ -0,0 +1,1167 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Seldon Model Zoo\n",
+    "\n",
+    "Examples of various model artefact types from various frameworks running under Seldon Core V2.\n",
+    "\n",
+    " * SKlearn\n",
+    " * Tensorflow\n",
+    " * XGBoost\n",
+    " * ONNX\n",
+    " * Lightgbm\n",
+    " * MLFlow\n",
+    " * PyTorch\n",
+    "\n",
+    "Python requirements in `model-zoo-requirements.txt`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b1925df",
+   "metadata": {},
+   "source": [
+    "### SKLearn Iris Classification Model\n",
+    "\n",
+    "The training code for this model can be found at `./scripts/models/iris`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8b4be911",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: iris\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/iris-sklearn\"\r\n",
+      "  requirements:\r\n",
+      "  - sklearn\r\n",
+      "  memory: 100Ki\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/sklearn-iris-gs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "checked-cream",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/sklearn-iris-gs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "interracial-secret",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status iris -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "upper-cholesterol",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"iris_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"09263298-ca66-49c5-acb9-0ca75b06f825\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT64\",\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer iris \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ceramic-illness",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload iris"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30431134",
+   "metadata": {},
+   "source": [
+    "### Tensorflow CIFAR10 Image Classification Model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f0e7043f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-03-09 19:43:43.637892: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2023-03-09 19:43:43.637906: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "from typing import Dict, List\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import tensorflow as tf\n",
+    "from alibi_detect.utils.perturbation import apply_mask\n",
+    "from alibi_detect.datasets import fetch_cifar10c\n",
+    "import matplotlib.pyplot as plt\n",
+    "tf.keras.backend.clear_session()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "727e6a62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train, test = tf.keras.datasets.cifar10.load_data()\n",
+    "X_train, y_train = train\n",
+    "X_test, y_test = test\n",
+    "\n",
+    "X_train = X_train.astype('float32') / 255\n",
+    "X_test = X_test.astype('float32') / 255\n",
+    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n",
+    "classes = (\n",
+    "    \"plane\",\n",
+    "    \"car\",\n",
+    "    \"bird\",\n",
+    "    \"cat\",\n",
+    "    \"deer\",\n",
+    "    \"dog\",\n",
+    "    \"frog\",\n",
+    "    \"horse\",\n",
+    "    \"ship\",\n",
+    "    \"truck\",\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e2dc7fe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reqJson = json.loads('{\"inputs\":[{\"name\":\"input_1\",\"data\":[],\"datatype\":\"FP32\",\"shape\":[]}]}')\n",
+    "url = \"http://0.0.0.0:9000/v2/models/model/infer\"\n",
+    "\n",
+    "def infer(resourceName: str, idx: int):\n",
+    "    rows = X_train[idx:idx+1]\n",
+    "    show(rows[0])\n",
+    "    reqJson[\"inputs\"][0][\"data\"] = rows.flatten().tolist()\n",
+    "    reqJson[\"inputs\"][0][\"shape\"] = [1, 32, 32, 3]\n",
+    "    headers = {\"Content-Type\": \"application/json\", \"seldon-model\":resourceName}\n",
+    "    response_raw = requests.post(url, json=reqJson, headers=headers)\n",
+    "    probs = np.array(response_raw.json()[\"outputs\"][0][\"data\"])\n",
+    "    print(classes[probs.argmax(axis=0)])\n",
+    "    \n",
+    "\n",
+    "def show(X):\n",
+    "    plt.imshow(X.reshape(32, 32, 3))\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "34165288",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: cifar10\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/tensorflow/cifar10\"\r\n",
+      "  requirements:\r\n",
+      "  - tensorflow\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/cifar10-no-config.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "726f2dd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/cifar10-no-config.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2d9b631f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status cifar10 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "16ac398b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "car\n"
+     ]
+    }
+   ],
+   "source": [
+    "infer(\"cifar10\",4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f7405940",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload cifar10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04a726c0",
+   "metadata": {},
+   "source": [
+    "### XGBoost Model\n",
+    "\n",
+    "The training code for this model can be found at `./scripts/models/income-xgb`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c57d72d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: income-xgb\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/income-xgb\"\r\n",
+      "  requirements:\r\n",
+      "  - xgboost\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/income-xgb.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "28e5f8d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/income-xgb.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8e20ab04",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status income-xgb -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a772f985",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"income-xgb_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"e30c3b44-fa14-4e5f-88f5-d6f4d287da20\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"FP32\",\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t-1.8380107879638672\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer income-xgb \\\n",
+    "  '{ \"parameters\": {\"content_type\": \"pd\"}, \"inputs\": [{\"name\": \"Age\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [47]},{\"name\": \"Workclass\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [4]},{\"name\": \"Education\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [1]},{\"name\": \"Marital Status\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [1]},{\"name\": \"Occupation\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [1]},{\"name\": \"Relationship\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [3]},{\"name\": \"Race\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [4]},{\"name\": \"Sex\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [1]},{\"name\": \"Capital Gain\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [0]},{\"name\": \"Capital Loss\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [0]},{\"name\": \"Hours per week\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [40]},{\"name\": \"Country\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [9]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "01f1d42b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload income-xgb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca3f0a3c",
+   "metadata": {},
+   "source": [
+    "## ONNX MNIST Model\n",
+    "\n",
+    "This model is a pretrained model as defined in `./scripts/models/Makefile` target `mnist-onnx`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "322251ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import json\n",
+    "import requests\n",
+    "from torchvision.datasets import MNIST\n",
+    "from torchvision.transforms import ToTensor\n",
+    "from torchvision import transforms\n",
+    "from torch.utils.data import DataLoader\n",
+    "import numpy as np\n",
+    "training_data = MNIST(\n",
+    "    root=\".\",\n",
+    "    download=True,\n",
+    "    train=False,\n",
+    "    transform = transforms.Compose([\n",
+    "              transforms.ToTensor()\n",
+    "          ])\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "256db298",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reqJson = json.loads('{\"inputs\":[{\"name\":\"Input3\",\"data\":[],\"datatype\":\"FP32\",\"shape\":[]}]}')\n",
+    "url = \"http://0.0.0.0:9000/v2/models/model/infer\"\n",
+    "dl = DataLoader(training_data, batch_size=1, shuffle=False)\n",
+    "dlIter = iter(dl)\n",
+    "\n",
+    "def infer_mnist():\n",
+    "    x, y = next(dlIter)\n",
+    "    data = x.cpu().numpy()\n",
+    "    reqJson[\"inputs\"][0][\"data\"] = data.flatten().tolist()\n",
+    "    reqJson[\"inputs\"][0][\"shape\"] = [1, 1, 28, 28]\n",
+    "    headers = {\"Content-Type\": \"application/json\", \"seldon-model\":\"mnist-onnx\"}\n",
+    "    response_raw = requests.post(url, json=reqJson, headers=headers)\n",
+    "    show_mnist(x)\n",
+    "    probs = np.array(response_raw.json()[\"outputs\"][0][\"data\"])\n",
+    "    print(probs.argmax(axis=0))\n",
+    "    \n",
+    "\n",
+    "def show_mnist(X):\n",
+    "    plt.imshow(X.reshape(28, 28))\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9c5cd5af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: mnist-onnx\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/mnist-onnx\"\r\n",
+      "  requirements:\r\n",
+      "  - onnx\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/mnist-onnx.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8c698c9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/mnist-onnx.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "91e05090",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status mnist-onnx -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bec480da",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "infer_mnist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6a0cd431",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload mnist-onnx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8325a49e",
+   "metadata": {},
+   "source": [
+    "### LightGBM Model\n",
+    "\n",
+    "The training code for this model can be found at `./scripts/models/income-lgb`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "47896ade",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: income-lgb\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/income-lgb\"\r\n",
+      "  requirements:\r\n",
+      "  - lightgbm\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/income-lgb.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "8ac81161",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/income-lgb.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "d65a7f91",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status income-lgb -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "aa6403a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"income-lgb_1\",\r\n",
+      "\t\"model_version\": \"1\",\r\n",
+      "\t\"id\": \"4437a71e-9af1-4e3b-aa4b-cb95d2cd86b9\",\r\n",
+      "\t\"parameters\": {},\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"name\": \"predict\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t1\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"FP64\",\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0.06279460120044741\r\n",
+      "\t\t\t]\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model infer income-lgb \\\n",
+    "  '{ \"parameters\": {\"content_type\": \"pd\"}, \"inputs\": [{\"name\": \"Age\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [47]},{\"name\": \"Workclass\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [4]},{\"name\": \"Education\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [1]},{\"name\": \"Marital Status\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [1]},{\"name\": \"Occupation\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [1]},{\"name\": \"Relationship\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [3]},{\"name\": \"Race\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [4]},{\"name\": \"Sex\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [1]},{\"name\": \"Capital Gain\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [0]},{\"name\": \"Capital Loss\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [0]},{\"name\": \"Hours per week\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [40]},{\"name\": \"Country\", \"shape\": [1, 1], \"datatype\": \"INT64\", \"data\": [9]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "f1912a83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload income-lgb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1baa5a6c",
+   "metadata": {},
+   "source": [
+    "### MLFlow Wine Model\n",
+    "\n",
+    "The training code for this model can be found at `./scripts/models/wine-mlflow`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "5ffd0f32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: wine\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/mlserver_1.3.5/wine-mlflow\"\r\n",
+      "  requirements:\r\n",
+      "  - mlflow\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/wine-mlflow.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "8a5d38b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/wine-mlflow.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "2e749f0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status wine -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "c71e83de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'model_name': 'wine_1', 'model_version': '1', 'id': '0d7e44f8-b46c-4438-b8af-a749e6aa6039', 'parameters': {}, 'outputs': [{'name': 'output-1', 'shape': [1, 1], 'datatype': 'FP64', 'data': [5.576883936610762]}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "url = \"http://0.0.0.0:9000/v2/models/model/infer\"\n",
+    "inference_request = {\n",
+    "    \"inputs\": [\n",
+    "        {\n",
+    "          \"name\": \"fixed acidity\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [7.4],\n",
+    "        },\n",
+    "        {\n",
+    "          \"name\": \"volatile acidity\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [0.7000],\n",
+    "        },\n",
+    "        {\n",
+    "          \"name\": \"citric acid\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [0],\n",
+    "        },\n",
+    "        {\n",
+    "          \"name\": \"residual sugar\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [1.9],\n",
+    "        },\n",
+    "        {\n",
+    "          \"name\": \"chlorides\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [0.076],\n",
+    "        },\n",
+    "        {\n",
+    "          \"name\": \"free sulfur dioxide\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [11],\n",
+    "        },\n",
+    "        {\n",
+    "          \"name\": \"total sulfur dioxide\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [34],\n",
+    "        },\n",
+    "        {\n",
+    "          \"name\": \"density\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [0.9978],\n",
+    "        },\n",
+    "        {\n",
+    "          \"name\": \"pH\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [3.51],\n",
+    "        },\n",
+    "        {\n",
+    "          \"name\": \"sulphates\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [0.56],\n",
+    "        },\n",
+    "        {\n",
+    "          \"name\": \"alcohol\",\n",
+    "          \"shape\": [1],\n",
+    "          \"datatype\": \"FP32\",\n",
+    "          \"data\": [9.4],\n",
+    "        },\n",
+    "    ]\n",
+    "}\n",
+    "headers = {\"Content-Type\": \"application/json\", \"seldon-model\":\"wine\"}\n",
+    "response_raw = requests.post(url, json=inference_request, headers=headers)\n",
+    "print(response_raw.json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "a2ba83ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload wine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d850d9dd",
+   "metadata": {},
+   "source": [
+    "## Pytorch MNIST Model\n",
+    "\n",
+    "This example model is downloaded and trained in `./scripts/models/Makefile` target `mnist-pytorch`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "57d19f06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import json\n",
+    "import requests\n",
+    "from torchvision.datasets import MNIST\n",
+    "from torchvision.transforms import ToTensor\n",
+    "from torchvision import transforms\n",
+    "from torch.utils.data import DataLoader\n",
+    "training_data = MNIST(\n",
+    "    root=\".\",\n",
+    "    download=True,\n",
+    "    train=False,\n",
+    "    transform = transforms.Compose([\n",
+    "              transforms.ToTensor()\n",
+    "          ])\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5b27e8b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reqJson = json.loads('{\"inputs\":[{\"name\":\"x__0\",\"data\":[],\"datatype\":\"FP32\",\"shape\":[]}]}')\n",
+    "url = \"http://0.0.0.0:9000/v2/models/model/infer\"\n",
+    "dl = DataLoader(training_data, batch_size=1, shuffle=False)\n",
+    "dlIter = iter(dl)\n",
+    "\n",
+    "def infer_mnist():\n",
+    "    x, y = next(dlIter)\n",
+    "    data = x.cpu().numpy()\n",
+    "    reqJson[\"inputs\"][0][\"data\"] = data.flatten().tolist()\n",
+    "    reqJson[\"inputs\"][0][\"shape\"] = [1, 1, 28, 28]\n",
+    "    headers = {\"Content-Type\": \"application/json\", \"seldon-model\":\"mnist-pytorch\"}\n",
+    "    response_raw = requests.post(url, json=reqJson, headers=headers)\n",
+    "    show_mnist(x)\n",
+    "    probs = np.array(response_raw.json()[\"outputs\"][0][\"data\"])\n",
+    "    print(probs.argmax(axis=0))\n",
+    "    \n",
+    "\n",
+    "def show_mnist(X):\n",
+    "    plt.imshow(X.reshape(28, 28))\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a5085df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: mnist-pytorch\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/mnist-pytorch\"\r\n",
+      "  requirements:\r\n",
+      "  - pytorch\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/mnist-pytorch.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ada997dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/mnist-pytorch.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "85e443ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status mnist-pytorch -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a8f1ba5c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGFCAYAAAASI+9IAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/av/WaAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAIX0lEQVR4nO3cX6jfZQHH8e+ZZy43tczSSNNRoqSlV1YiwYJGhEgXOirIuzKqC+uiLgIhKIwkCcu6rAuxP0yDQCsQoSIylSKNTNBKQlqulqXm3Fzn1028ITI8z9eds+P2el3/Pnwf2PS95+ZZWiwWiwkApmnadKQPAMDGIQoARBQAiCgAEFEAIKIAQEQBgIgCAFle7Q93btq1lucAYI3dubL7BX/jpgBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgBZPtIHOBbs+9Alw5uzrnpk1rce2nv68Obggc3DmzO+Nb7Z+tjTw5tpmqaVXz04aweMc1MAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQDildR18KlPfnN4c8W2J+Z97A3zZsN2jE8ePfTMrE/d+Jd3zNqxfu7de/bwZtsNL5/1reW7fjFrx+q4KQAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgCwtFovFan64c9OutT7LUeufV751ePPXC+f1+pTfruqP87888cal4c3xF/59eHP9m747vJmmadp5wv7hzR3PnDi8uWzr08Ob9bR/cXB4c8+BbcObHS97bngzxzl3fHjW7tyr7zvMJzl23Lmy+wV/46YAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQCyfKQPcCzYdus9MzZrcJD/4+R1+s5XXrNj1u5zl24f3pz840eGN9fvOGd4s56W968Mb7Y9sGd4c+pPbhvevPn4zcObrY+Ob1h7bgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACAexGPdHPrz47N2224b3/1rzndu3TdjtbE9/sFLhjcXHD/+v4Uv/u284c32b/x+eDNN03Ro1orVclMAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQDilVR4iVg++3XDm5s+fdPwZvPSccOb3Te+c3hz6p67hzesPTcFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQD+LBS8RDnzhjeHPxlqXhzW8O7h/evPLBZ4Y3bExuCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIB7Eg3V24LKLZ+1+eeWXZqy2DC8+cs01w5sTfnbv8IaNyU0BgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgDEg3iwzv747nn/Fjtxafxxu/f/YefwZusP7x/eLIYXbFRuCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIB7Egxdh00knDW+uevtPZ33ryZVnhzd7r3v98GbLgfuGNxw93BQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYB4JRVehIc/c8Hw5vZXfW3Wt97z8BXDmy3f9+IpY9wUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBAPIgH//GPD7xtePPAe788vPndoeeGN9M0TU9/4czhzZZpz6xvcexyUwAgogBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCAPEgHkel5TNeO7z5+LXfGd5sWRr/T+h99181vJmmaXr1D+6btYMRbgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACAexGPDW1oe/2t60e2PDW92nbhveHPLU6cNb06/dt6/xVZmrWCMmwIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgH8dj4LjpvePLZ025eg4P8r69et2t484r7716Dk8Dh4aYAQEQBgIgCABEFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgDEK6msm+POP3fW7upvf+8wn+T5nf/1jw1vtt/88zU4CRw5bgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACAexGPdPPTRU2btLt/65GE+yfM780cHx0eLxeE/CBxBbgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAEQUAIgoABBRACAexGOWZy9/y/DmrstvmPm1rTN3wCg3BQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAIgoARBQAiCgAEA/iMcufLj1ueHPW8vo9bHfLU6cNbzY/eXB4sxhewMbmpgBARAGAiAIAEQUAIgoARBQAiCgAEFEAIKIAQEQBgIgCABEFACIKAMQrqWx4n993/vDm7ndtH94s9vx6eANHGzcFACIKAEQUAIgoABBRACCiAEBEAYCIAgARBQAiCgBEFACIKACQpcVisVjND3du2rXWZwFgDd25svsFf+OmAEBEAYCIAgARBQAiCgBEFACIKAAQUQAgogBARAGAiAIAEQUAsuoH8QA4+rkpABBRACCiAEBEAYCIAgARBQAiCgBEFACIKACQfwOvoLnvrbsiYAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "infer_mnist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b972b1ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload mnist-pytorch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94cd2aba",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/pipeline-errors.ipynb b/docs/examples/notebooks/pipeline-errors.ipynb
new file mode 100644
index 0000000000..957102f251
--- /dev/null
+++ b/docs/examples/notebooks/pipeline-errors.ipynb
@@ -0,0 +1,438 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Pipeline Errors\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0f2ab2a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml\n",
+    "!cat ./models/tfsimple2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f9e073d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model load -f ./models/tfsimple2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "997b4028",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3d017ede",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimples\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "    - name: tfsimple2\r\n",
+      "      inputs:\r\n",
+      "      - tfsimple1\r\n",
+      "      tensorMap:\r\n",
+      "        tfsimple1.outputs.OUTPUT0: INPUT0\r\n",
+      "        tfsimple1.outputs.OUTPUT1: INPUT1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple2\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimples.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "following-winning",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimples.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "artistic-kentucky",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimples\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimples\",\r\n",
+      "        \"uid\": \"cg5fn9s6dpcs73c4qheg\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple1\"\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple2\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"tfsimple1.outputs\"\r\n",
+      "            ],\r\n",
+      "            \"tensorMap\": {\r\n",
+      "              \"tfsimple1.outputs.OUTPUT0\": \"INPUT0\",\r\n",
+      "              \"tfsimple1.outputs.OUTPUT1\": \"INPUT1\"\r\n",
+      "            }\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple2.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-03-10T09:43:03.270822655Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimples -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "87f10a5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"\",\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2,\r\n",
+      "\t\t\t\t4,\r\n",
+      "\t\t\t\t6,\r\n",
+      "\t\t\t\t8,\r\n",
+      "\t\t\t\t10,\r\n",
+      "\t\t\t\t12,\r\n",
+      "\t\t\t\t14,\r\n",
+      "\t\t\t\t16,\r\n",
+      "\t\t\t\t18,\r\n",
+      "\t\t\t\t20,\r\n",
+      "\t\t\t\t22,\r\n",
+      "\t\t\t\t24,\r\n",
+      "\t\t\t\t26,\r\n",
+      "\t\t\t\t28,\r\n",
+      "\t\t\t\t30,\r\n",
+      "\t\t\t\t32\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT0\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t},\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2,\r\n",
+      "\t\t\t\t4,\r\n",
+      "\t\t\t\t6,\r\n",
+      "\t\t\t\t8,\r\n",
+      "\t\t\t\t10,\r\n",
+      "\t\t\t\t12,\r\n",
+      "\t\t\t\t14,\r\n",
+      "\t\t\t\t16,\r\n",
+      "\t\t\t\t18,\r\n",
+      "\t\t\t\t20,\r\n",
+      "\t\t\t\t22,\r\n",
+      "\t\t\t\t24,\r\n",
+      "\t\t\t\t26,\r\n",
+      "\t\t\t\t28,\r\n",
+      "\t\t\t\t30,\r\n",
+      "\t\t\t\t32\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT1\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e27d4f8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: V2 server error: 400 tfsimple1 : rpc error: code = InvalidArgument desc = [request id: <id_unknown>] unexpected shape for input 'INPUT0' for model 'tfsimple1_1'. Expected [-1,16], got [1,20]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,20]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5c33133e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples --inference-mode grpc \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ea07d3f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: rpc error: code = Unknown desc = tfsimple1 : rpc error: code = InvalidArgument desc = [request id: <id_unknown>] unexpected shape for input 'INPUT0' for model 'tfsimple1_1'. Expected [-1,16], got [1,20]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples --inference-mode grpc \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,20]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e017099a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline unload tfsimples\n",
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db3dc8f4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/pipeline-examples.ipynb b/docs/examples/notebooks/pipeline-examples.ipynb
new file mode 100644
index 0000000000..47d770ebe1
--- /dev/null
+++ b/docs/examples/notebooks/pipeline-examples.ipynb
@@ -0,0 +1,2150 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Pipeline Examples\n",
+    "\n",
+    "This notebook illustrates a series of Pipelines showing of different ways of combining flows of data and conditional logic. We assume you have Seldon Core V2 running locally.\n",
+    "\n",
+    "### Models Used\n",
+    "\n",
+    " * `gs://seldon-models/triton/simple` an example Triton tensorflow model that takes 2 inputs INPUT0 and INPUT1 and adds them to produce OUTPUT0 and also subtracts INPUT1 from INPUT0 to produce OUTPUT1. See [here](https://github.com/triton-inference-server/server/tree/main/docs/examples/model_repository/simple) for the original source code and license.\n",
+    " * Other models can be found at https://github.com/SeldonIO/triton-python-examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "running-antarctica",
+   "metadata": {},
+   "source": [
+    "### Model Chaining\n",
+    "\n",
+    "Chain the output of one model into the next. Also shows chaning the tensor names via `tensorMap` to conform to the expected input tensor names of the second model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0f2ab2a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml\n",
+    "!cat ./models/tfsimple2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f9e073d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model load -f ./models/tfsimple2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "997b4028",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beb58fda",
+   "metadata": {},
+   "source": [
+    "The pipeline below chains the output of `tfsimple1` into `tfsimple2`. As these models have compatible shape and data type this can be done. However, the output tensor names from `tfsimple1` need to be renamed to match the input tensor names for `tfsimple2`. We do this with the `tensorMap` feature.\n",
+    "\n",
+    "The output of the Pipeline is the output from `tfsimple2`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3d017ede",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimples\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "    - name: tfsimple2\r\n",
+      "      inputs:\r\n",
+      "      - tfsimple1\r\n",
+      "      tensorMap:\r\n",
+      "        tfsimple1.outputs.OUTPUT0: INPUT0\r\n",
+      "        tfsimple1.outputs.OUTPUT1: INPUT1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple2\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimples.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "following-winning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimples.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "artistic-kentucky",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimples\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimples\",\r\n",
+      "        \"uid\": \"ciep26qi8ufs73flaiqg\",\r\n",
+      "        \"version\": 2,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple1\"\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple2\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"tfsimple1.outputs\"\r\n",
+      "            ],\r\n",
+      "            \"tensorMap\": {\r\n",
+      "              \"tfsimple1.outputs.OUTPUT0\": \"INPUT0\",\r\n",
+      "              \"tfsimple1.outputs.OUTPUT1\": \"INPUT1\"\r\n",
+      "            }\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple2.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 2,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T14:11:40.101677847Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimples -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "87f10a5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        2,\r\n",
+      "        4,\r\n",
+      "        6,\r\n",
+      "        8,\r\n",
+      "        10,\r\n",
+      "        12,\r\n",
+      "        14,\r\n",
+      "        16,\r\n",
+      "        18,\r\n",
+      "        20,\r\n",
+      "        22,\r\n",
+      "        24,\r\n",
+      "        26,\r\n",
+      "        28,\r\n",
+      "        30,\r\n",
+      "        32\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        2,\r\n",
+      "        4,\r\n",
+      "        6,\r\n",
+      "        8,\r\n",
+      "        10,\r\n",
+      "        12,\r\n",
+      "        14,\r\n",
+      "        16,\r\n",
+      "        18,\r\n",
+      "        20,\r\n",
+      "        22,\r\n",
+      "        24,\r\n",
+      "        26,\r\n",
+      "        28,\r\n",
+      "        30,\r\n",
+      "        32\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5c33133e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples --inference-mode grpc \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9878e97",
+   "metadata": {},
+   "source": [
+    "We use the Seldon CLI `pipeline inspect` feature to look at the data for all steps of the pipeline for the last data item passed through the pipeline (the default). This can be useful for debugging."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "126144a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple1.inputs\tciep298fh5ss73dpdir0\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}\r\n",
+      "seldon.default.model.tfsimple1.outputs\tciep298fh5ss73dpdir0\t{\"modelName\":\"tfsimple1_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n",
+      "seldon.default.model.tfsimple2.inputs\tciep298fh5ss73dpdir0\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.model.tfsimple2.outputs\tciep298fh5ss73dpdir0\t{\"modelName\":\"tfsimple2_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n",
+      "seldon.default.pipeline.tfsimples.inputs\tciep298fh5ss73dpdir0\t{\"modelName\":\"tfsimples\", \"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}\r\n",
+      "seldon.default.pipeline.tfsimples.outputs\tciep298fh5ss73dpdir0\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf445e9b",
+   "metadata": {},
+   "source": [
+    "Next, we look get the output as json and use the `jq` tool to get just one value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "10b5bca0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"inputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"INPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          1,\r\n",
+      "          2,\r\n",
+      "          3,\r\n",
+      "          4,\r\n",
+      "          5,\r\n",
+      "          6,\r\n",
+      "          7,\r\n",
+      "          8,\r\n",
+      "          9,\r\n",
+      "          10,\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14,\r\n",
+      "          15,\r\n",
+      "          16\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"INPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          1,\r\n",
+      "          2,\r\n",
+      "          3,\r\n",
+      "          4,\r\n",
+      "          5,\r\n",
+      "          6,\r\n",
+      "          7,\r\n",
+      "          8,\r\n",
+      "          9,\r\n",
+      "          10,\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14,\r\n",
+      "          15,\r\n",
+      "          16\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimples --format json | jq -M .topics[0].msgs[0].value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dimensional-hours",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload tfsimples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "outside-inspiration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c6a8e27",
+   "metadata": {},
+   "source": [
+    "### Model Chaining from inputs\n",
+    "\n",
+    "Chain the output of one model into the next. Shows using the input and outputs and combining."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7cbf98a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml\n",
+    "!cat ./models/tfsimple2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f0674da8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model load -f ./models/tfsimple2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "53ab8c9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "aa3c0927",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimples-input\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "    - name: tfsimple2\r\n",
+      "      inputs:\r\n",
+      "      - tfsimple1.inputs.INPUT0\r\n",
+      "      - tfsimple1.outputs.OUTPUT1\r\n",
+      "      tensorMap:\r\n",
+      "        tfsimple1.outputs.OUTPUT1: INPUT1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple2\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimples-input.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5c0aa3a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimples-input.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0fc44852",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimples-input\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimples-input\",\r\n",
+      "        \"uid\": \"ciep2fii8ufs73flair0\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple1\"\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple2\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"tfsimple1.inputs.INPUT0\",\r\n",
+      "              \"tfsimple1.outputs.OUTPUT1\"\r\n",
+      "            ],\r\n",
+      "            \"tensorMap\": {\r\n",
+      "              \"tfsimple1.outputs.OUTPUT1\": \"INPUT1\"\r\n",
+      "            }\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple2.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T14:12:14.711416101Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimples-input -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "904cf65a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        1,\r\n",
+      "        2,\r\n",
+      "        3,\r\n",
+      "        4,\r\n",
+      "        5,\r\n",
+      "        6,\r\n",
+      "        7,\r\n",
+      "        8,\r\n",
+      "        9,\r\n",
+      "        10,\r\n",
+      "        11,\r\n",
+      "        12,\r\n",
+      "        13,\r\n",
+      "        14,\r\n",
+      "        15,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        1,\r\n",
+      "        2,\r\n",
+      "        3,\r\n",
+      "        4,\r\n",
+      "        5,\r\n",
+      "        6,\r\n",
+      "        7,\r\n",
+      "        8,\r\n",
+      "        9,\r\n",
+      "        10,\r\n",
+      "        11,\r\n",
+      "        12,\r\n",
+      "        13,\r\n",
+      "        14,\r\n",
+      "        15,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples-input \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "58a2f2ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          1,\r\n",
+      "          2,\r\n",
+      "          3,\r\n",
+      "          4,\r\n",
+      "          5,\r\n",
+      "          6,\r\n",
+      "          7,\r\n",
+      "          8,\r\n",
+      "          9,\r\n",
+      "          10,\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14,\r\n",
+      "          15,\r\n",
+      "          16\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          1,\r\n",
+      "          2,\r\n",
+      "          3,\r\n",
+      "          4,\r\n",
+      "          5,\r\n",
+      "          6,\r\n",
+      "          7,\r\n",
+      "          8,\r\n",
+      "          9,\r\n",
+      "          10,\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14,\r\n",
+      "          15,\r\n",
+      "          16\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimples-input --inference-mode grpc \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1cd53dd6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload tfsimples-input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "91cce2e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fca716ed",
+   "metadata": {},
+   "source": [
+    "### Model Join\n",
+    "\n",
+    "Join two flows of data from two models as input to a third model. This shows how individual flows of data can be combined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5e41768d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple3\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./models/tfsimple2.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./models/tfsimple3.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "fcbc23cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model load -f ./models/tfsimple2.yaml \n",
+    "!seldon model load -f ./models/tfsimple3.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "f8bb2efb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple3 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "145b0851",
+   "metadata": {},
+   "source": [
+    "In the pipeline below for the input to `tfsimple3` we join 1 output tensor each from the two previous models `tfsimple1` and `tfsimple2`. We need to use the `tensorMap` feature to rename each output tensor to one of the expected input tensors for the `tfsimple3` model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cd58ecba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: join\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "    - name: tfsimple2\r\n",
+      "    - name: tfsimple3      \r\n",
+      "      inputs:\r\n",
+      "      - tfsimple1.outputs.OUTPUT0\r\n",
+      "      - tfsimple2.outputs.OUTPUT1\r\n",
+      "      tensorMap:\r\n",
+      "        tfsimple1.outputs.OUTPUT0: INPUT0\r\n",
+      "        tfsimple2.outputs.OUTPUT1: INPUT1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple3\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimples-join.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ce9e163b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimples-join.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7593c77f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"join\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"join\",\r\n",
+      "        \"uid\": \"ciep2k2i8ufs73flairg\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple1\"\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple2\"\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple3\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"tfsimple1.outputs.OUTPUT0\",\r\n",
+      "              \"tfsimple2.outputs.OUTPUT1\"\r\n",
+      "            ],\r\n",
+      "            \"tensorMap\": {\r\n",
+      "              \"tfsimple1.outputs.OUTPUT0\": \"INPUT0\",\r\n",
+      "              \"tfsimple2.outputs.OUTPUT1\": \"INPUT1\"\r\n",
+      "            }\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple3.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T14:12:32.938603415Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status join -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f82fe9c0",
+   "metadata": {},
+   "source": [
+    "The outputs are the sequence \"2,4,6...\" which conforms to the logic of this model (addition and subtraction) when fed the output of the first two models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "2151ee4c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"datatype\": \"INT32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"1\",\r\n",
+      "        \"16\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"intContents\": [\r\n",
+      "          2,\r\n",
+      "          4,\r\n",
+      "          6,\r\n",
+      "          8,\r\n",
+      "          10,\r\n",
+      "          12,\r\n",
+      "          14,\r\n",
+      "          16,\r\n",
+      "          18,\r\n",
+      "          20,\r\n",
+      "          22,\r\n",
+      "          24,\r\n",
+      "          26,\r\n",
+      "          28,\r\n",
+      "          30,\r\n",
+      "          32\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer join --inference-mode grpc \\\n",
+    "    '{\"model_name\":\"simple\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"contents\":{\"int_contents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "06575cca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload join"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "8bd56a9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2\n",
+    "!seldon model unload tfsimple3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6666e04",
+   "metadata": {},
+   "source": [
+    "### Conditional\n",
+    "\n",
+    "Shows conditional data flows - one of two models is run based on output tensors from first. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "e3a2c7f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: conditional\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/conditional\"\n",
+      "  requirements:\n",
+      "  - triton\n",
+      "  - python\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: add10\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/add10\"\n",
+      "  requirements:\n",
+      "  - triton\n",
+      "  - python\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: mul10\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/mul10\"\n",
+      "  requirements:\n",
+      "  - triton\n",
+      "  - python\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/conditional.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./models/add10.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./models/mul10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "2104f9bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/conditional.yaml \n",
+    "!seldon model load -f ./models/add10.yaml \n",
+    "!seldon model load -f ./models/mul10.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7af88e3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status conditional -w ModelAvailable | jq -M .\n",
+    "!seldon model status add10 -w ModelAvailable | jq -M .\n",
+    "!seldon model status mul10 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00e827fb",
+   "metadata": {},
+   "source": [
+    "Here we assume the `conditional` model can output two tensors OUTPUT0 and OUTPUT1 but only outputs the former if the CHOICE input tensor is set to 0 otherwise it outputs tensor OUTPUT1. By this means only one of the two downstream models will receive data and run. The `output` steps does an `any` join from both models and whichever data appears first will be sent as output to pipeline. As in this case only 1 of the two models `add10` and `mul10` runs we will receive their output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "f5bbe84d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple-conditional\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "  - name: conditional\r\n",
+      "  - name: mul10\r\n",
+      "    inputs:\r\n",
+      "    - conditional.outputs.OUTPUT0\r\n",
+      "    tensorMap:\r\n",
+      "      conditional.outputs.OUTPUT0: INPUT\r\n",
+      "  - name: add10\r\n",
+      "    inputs:\r\n",
+      "    - conditional.outputs.OUTPUT1\r\n",
+      "    tensorMap:\r\n",
+      "      conditional.outputs.OUTPUT1: INPUT\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - mul10\r\n",
+      "    - add10\r\n",
+      "    stepsJoin: any\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/conditional.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "829f9b18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/conditional.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "ae202d26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimple-conditional\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimple-conditional\",\r\n",
+      "        \"uid\": \"ciepga2i8ufs73flais0\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"add10\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"conditional.outputs.OUTPUT1\"\r\n",
+      "            ],\r\n",
+      "            \"tensorMap\": {\r\n",
+      "              \"conditional.outputs.OUTPUT1\": \"INPUT\"\r\n",
+      "            }\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"conditional\"\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"mul10\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"conditional.outputs.OUTPUT0\"\r\n",
+      "            ],\r\n",
+      "            \"tensorMap\": {\r\n",
+      "              \"conditional.outputs.OUTPUT0\": \"INPUT\"\r\n",
+      "            }\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"mul10.outputs\",\r\n",
+      "            \"add10.outputs\"\r\n",
+      "          ],\r\n",
+      "          \"stepsJoin\": \"ANY\"\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T14:41:45.133142725Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple-conditional -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a98583af",
+   "metadata": {},
+   "source": [
+    "The `mul10` model will run as the CHOICE tensor is set to 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "0d03d5fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          10,\r\n",
+      "          20,\r\n",
+      "          30,\r\n",
+      "          40\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple-conditional --inference-mode grpc \\\n",
+    " '{\"model_name\":\"conditional\",\"inputs\":[{\"name\":\"CHOICE\",\"contents\":{\"int_contents\":[0]},\"datatype\":\"INT32\",\"shape\":[1]},{\"name\":\"INPUT0\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]},{\"name\":\"INPUT1\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8aab3a9c",
+   "metadata": {},
+   "source": [
+    "The `add10` model will run as the CHOICE tensor is not set to zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "def4e8e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple-conditional --inference-mode grpc \\\n",
+    " '{\"model_name\":\"conditional\",\"inputs\":[{\"name\":\"CHOICE\",\"contents\":{\"int_contents\":[1]},\"datatype\":\"INT32\",\"shape\":[1]},{\"name\":\"INPUT0\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]},{\"name\":\"INPUT1\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "3042fe42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload tfsimple-conditional"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "3bda8710",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload conditional\n",
+    "!seldon model unload add10\n",
+    "!seldon model unload mul10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ada5634",
+   "metadata": {},
+   "source": [
+    "### Pipeline Input Tensors\n",
+    "Access to indivudal tensors in pipeline inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "baa2cb5f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: mul10\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/mul10\"\n",
+      "  requirements:\n",
+      "  - triton\n",
+      "  - python\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: add10\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/add10\"\n",
+      "  requirements:\n",
+      "  - triton\n",
+      "  - python\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/mul10.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./models/add10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "33692146",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/mul10.yaml \n",
+    "!seldon model load -f ./models/add10.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "e0493925",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status mul10 -w ModelAvailable | jq -M .\n",
+    "!seldon model status add10 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c5bb324",
+   "metadata": {},
+   "source": [
+    "This pipeline shows how we can access pipeline inputs INPUT0 and INPUT1 from different steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "c8d27ff3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: pipeline-inputs\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "  - name: mul10\r\n",
+      "    inputs:\r\n",
+      "    - pipeline-inputs.inputs.INPUT0\r\n",
+      "    tensorMap:\r\n",
+      "      pipeline-inputs.inputs.INPUT0: INPUT\r\n",
+      "  - name: add10\r\n",
+      "    inputs:\r\n",
+      "    - pipeline-inputs.inputs.INPUT1\r\n",
+      "    tensorMap:\r\n",
+      "      pipeline-inputs.inputs.INPUT1: INPUT\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - mul10\r\n",
+      "    - add10\r\n",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/pipeline-inputs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "74768602",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/pipeline-inputs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "922b975e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"pipeline-inputs\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"pipeline-inputs\",\r\n",
+      "        \"uid\": \"ciepgeqi8ufs73flaisg\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"add10\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"pipeline-inputs.inputs.INPUT1\"\r\n",
+      "            ],\r\n",
+      "            \"tensorMap\": {\r\n",
+      "              \"pipeline-inputs.inputs.INPUT1\": \"INPUT\"\r\n",
+      "            }\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"mul10\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"pipeline-inputs.inputs.INPUT0\"\r\n",
+      "            ],\r\n",
+      "            \"tensorMap\": {\r\n",
+      "              \"pipeline-inputs.inputs.INPUT0\": \"INPUT\"\r\n",
+      "            }\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"mul10.outputs\",\r\n",
+      "            \"add10.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T14:42:04.202598715Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status pipeline-inputs -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "792680b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          10,\r\n",
+      "          20,\r\n",
+      "          30,\r\n",
+      "          40\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer pipeline-inputs --inference-mode grpc \\\n",
+    "    '{\"model_name\":\"pipeline\",\"inputs\":[{\"name\":\"INPUT0\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]},{\"name\":\"INPUT1\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "d6fd0d45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload pipeline-inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "3d5bb23c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload mul10\n",
+    "!seldon model unload add10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd13b4fe",
+   "metadata": {},
+   "source": [
+    "### Trigger Joins\n",
+    "\n",
+    "Shows how joins can be used for triggers as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "1a09de5c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: mul10\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/mul10\"\n",
+      "  requirements:\n",
+      "  - triton\n",
+      "  - python\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: add10\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/scv2/samples/triton_23-03/add10\"\n",
+      "  requirements:\n",
+      "  - triton\n",
+      "  - python\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/mul10.yaml\n",
+    "!cat ./models/add10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "85eb4ef2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/mul10.yaml \n",
+    "!seldon model load -f ./models/add10.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "d11e29c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status mul10 -w ModelAvailable | jq -M .\n",
+    "!seldon model status add10 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78d314ab",
+   "metadata": {},
+   "source": [
+    "Here we required tensors names `ok1` or `ok2` to exist on pipeline inputs to run the `mul10` model but require tensor `ok3` to exist on pipeline inputs to run the `add10` model. The logic on `mul10` is handled by a trigger join of `any` meaning either of these input data can exist to satisfy the trigger join."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "227e06e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: trigger-joins\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "  - name: mul10\r\n",
+      "    inputs:\r\n",
+      "    - trigger-joins.inputs.INPUT\r\n",
+      "    triggers:\r\n",
+      "    - trigger-joins.inputs.ok1\r\n",
+      "    - trigger-joins.inputs.ok2\r\n",
+      "    triggersJoinType: any\r\n",
+      "  - name: add10\r\n",
+      "    inputs:\r\n",
+      "    - trigger-joins.inputs.INPUT\r\n",
+      "    triggers:\r\n",
+      "    - trigger-joins.inputs.ok3\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - mul10\r\n",
+      "    - add10\r\n",
+      "    stepsJoin: any\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/trigger-joins.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "2bce48e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/trigger-joins.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "66c60286",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"trigger-joins\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"trigger-joins\",\r\n",
+      "        \"uid\": \"ciepgkqi8ufs73flait0\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"add10\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"trigger-joins.inputs.INPUT\"\r\n",
+      "            ],\r\n",
+      "            \"triggers\": [\r\n",
+      "              \"trigger-joins.inputs.ok3\"\r\n",
+      "            ]\r\n",
+      "          },\r\n",
+      "          {\r\n",
+      "            \"name\": \"mul10\",\r\n",
+      "            \"inputs\": [\r\n",
+      "              \"trigger-joins.inputs.INPUT\"\r\n",
+      "            ],\r\n",
+      "            \"triggers\": [\r\n",
+      "              \"trigger-joins.inputs.ok1\",\r\n",
+      "              \"trigger-joins.inputs.ok2\"\r\n",
+      "            ],\r\n",
+      "            \"triggersJoin\": \"ANY\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"mul10.outputs\",\r\n",
+      "            \"add10.outputs\"\r\n",
+      "          ],\r\n",
+      "          \"stepsJoin\": \"ANY\"\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T14:42:27.595300698Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status trigger-joins -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "403fcbd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          10,\r\n",
+      "          20,\r\n",
+      "          30,\r\n",
+      "          40\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer trigger-joins --inference-mode grpc \\\n",
+    "    '{\"model_name\":\"pipeline\",\"inputs\":[{\"name\":\"ok1\",\"contents\":{\"fp32_contents\":[1]},\"datatype\":\"FP32\",\"shape\":[1]},{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M . "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "fdba4c3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer trigger-joins --inference-mode grpc \\\n",
+    "    '{\"model_name\":\"pipeline\",\"inputs\":[{\"name\":\"ok3\",\"contents\":{\"fp32_contents\":[1]},\"datatype\":\"FP32\",\"shape\":[1]},{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M . "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "52a2bfd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload trigger-joins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "914d23f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload mul10\n",
+    "!seldon model unload add10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e017099a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/pipeline-ready-and-metadata.ipynb b/docs/examples/notebooks/pipeline-ready-and-metadata.ipynb
new file mode 100644
index 0000000000..8efa161bd2
--- /dev/null
+++ b/docs/examples/notebooks/pipeline-ready-and-metadata.ipynb
@@ -0,0 +1,845 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Pipeline Readiness Check and Metdata Calls\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f91799c2",
+   "metadata": {},
+   "source": [
+    "Local example settings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "901ec6a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: INFER_REST_ENDPOINT=http://0.0.0.0:9000\n",
+      "env: INFER_GRPC_ENDPOINT=0.0.0.0:9000\n",
+      "env: SELDON_SCHEDULE_HOST=0.0.0.0:9004\n"
+     ]
+    }
+   ],
+   "source": [
+    "%env INFER_REST_ENDPOINT=http://0.0.0.0:9000\n",
+    "%env INFER_GRPC_ENDPOINT=0.0.0.0:9000\n",
+    "%env SELDON_SCHEDULE_HOST=0.0.0.0:9004"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac2a9783",
+   "metadata": {},
+   "source": [
+    "Remote k8s cluster example settings - change as neeed for your needs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "925ec380",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#%env INFER_REST_ENDPOINT=http://172.19.255.1:80\n",
+    "#%env INFER_GRPC_ENDPOINT=172.19.255.1:80\n",
+    "#%env SELDON_SCHEDULE_HOST=172.19.255.2:9004"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "running-antarctica",
+   "metadata": {},
+   "source": [
+    "### Model Chain - Ready Check\n",
+    "\n",
+    "We will check the readiness of the Pipeline after every change to model and pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7b2adc80",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimples\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "    - name: tfsimple2\r\n",
+      "      inputs:\r\n",
+      "      - tfsimple1\r\n",
+      "      tensorMap:\r\n",
+      "        tfsimple1.outputs.OUTPUT0: INPUT0\r\n",
+      "        tfsimple1.outputs.OUTPUT1: INPUT1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple2\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimples.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9c7fdd82",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HTTP/1.1 404 Not Found\r",
+      "\r\n",
+      "\u001b[1mdate\u001b[0m: Thu, 29 Jun 2023 14:47:12 GMT\r",
+      "\r\n",
+      "\u001b[1mserver\u001b[0m: envoy\r",
+      "\r\n",
+      "\u001b[1mtransfer-encoding\u001b[0m: chunked\r",
+      "\r\n",
+      "\r",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "61ea0374",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ERROR:\r\n",
+      "  Code: Unimplemented\r\n",
+      "  Message: \r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"name\":\"tfsimples\"}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../apis \\\n",
+    "    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    -rpc-header seldon-model:tfsimples.pipeline \\\n",
+    "    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5b9c616e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"pipelineName\":\"tfsimples\", \"versions\":[{\"pipeline\":{\"name\":\"tfsimples\", \"uid\":\"ciepit2i8ufs73flaitg\", \"version\":1, \"steps\":[{\"name\":\"tfsimple1\"}, {\"name\":\"tfsimple2\", \"inputs\":[\"tfsimple1.outputs\"], \"tensorMap\":{\"tfsimple1.outputs.OUTPUT0\":\"INPUT0\", \"tfsimple1.outputs.OUTPUT1\":\"INPUT1\"}}], \"output\":{\"steps\":[\"tfsimple2.outputs\"]}, \"kubernetesMeta\":{}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T14:47:16.365934922Z\"}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimples.yaml\n",
+    "!seldon pipeline status tfsimples -w PipelineReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "79e5f931",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;30mnull\u001b[0m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimples | jq .versions[0].state.modelsReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9e1907d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HTTP/1.1 424 Failed Dependency\r",
+      "\r\n",
+      "\u001b[1mdate\u001b[0m: Thu, 29 Jun 2023 14:47:21 GMT\r",
+      "\r\n",
+      "\u001b[1mx-envoy-upstream-service-time\u001b[0m: 1\r",
+      "\r\n",
+      "\u001b[1mx-seldon-route\u001b[0m: :tfsimples.pipeline:\r",
+      "\r\n",
+      "\u001b[1mserver\u001b[0m: envoy\r",
+      "\r\n",
+      "\u001b[1mtransfer-encoding\u001b[0m: chunked\r",
+      "\r\n",
+      "\r",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bb0064af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"name\":\"tfsimples\"}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../apis \\\n",
+    "    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    -rpc-header seldon-model:tfsimples.pipeline \\\n",
+    "    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f9e073d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model status tfsimple1 -w ModelAvailable "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e7112952",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HTTP/1.1 424 Failed Dependency\r",
+      "\r\n",
+      "\u001b[1mdate\u001b[0m: Thu, 29 Jun 2023 14:47:31 GMT\r",
+      "\r\n",
+      "\u001b[1mx-envoy-upstream-service-time\u001b[0m: 2\r",
+      "\r\n",
+      "\u001b[1mx-seldon-route\u001b[0m: :tfsimples.pipeline:\r",
+      "\r\n",
+      "\u001b[1mserver\u001b[0m: envoy\r",
+      "\r\n",
+      "\u001b[1mtransfer-encoding\u001b[0m: chunked\r",
+      "\r\n",
+      "\r",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7488bec5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"name\":\"tfsimples\"}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../apis \\\n",
+    "    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    -rpc-header seldon-model:tfsimples.pipeline \\\n",
+    "    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "997b4028",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple2.yaml \n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "45f2f89c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HTTP/1.1 200 OK\r",
+      "\r\n",
+      "\u001b[1mdate\u001b[0m: Thu, 29 Jun 2023 14:47:36 GMT\r",
+      "\r\n",
+      "\u001b[1mx-envoy-upstream-service-time\u001b[0m: 1\r",
+      "\r\n",
+      "\u001b[1mx-seldon-route\u001b[0m: :tfsimples.pipeline:\r",
+      "\r\n",
+      "\u001b[1mserver\u001b[0m: envoy\r",
+      "\r\n",
+      "\u001b[1mtransfer-encoding\u001b[0m: chunked\r",
+      "\r\n",
+      "\r",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8258b96c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"ready\": true\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"name\":\"tfsimples\"}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../apis \\\n",
+    "    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    -rpc-header seldon-model:tfsimples.pipeline \\\n",
+    "    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c92d0438",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[0;39mtrue\u001b[0m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimples | jq .versions[0].state.modelsReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "dimensional-hours",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload tfsimples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a9076e85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HTTP/1.1 404 Not Found\r",
+      "\r\n",
+      "\u001b[1mdate\u001b[0m: Thu, 29 Jun 2023 14:47:43 GMT\r",
+      "\r\n",
+      "\u001b[1mserver\u001b[0m: envoy\r",
+      "\r\n",
+      "\u001b[1mtransfer-encoding\u001b[0m: chunked\r",
+      "\r\n",
+      "\r",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1174f2da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ERROR:\r\n",
+      "  Code: Unimplemented\r\n",
+      "  Message: \r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"name\":\"tfsimples\"}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../apis \\\n",
+    "    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    -rpc-header seldon-model:tfsimples.pipeline \\\n",
+    "    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba57c305",
+   "metadata": {},
+   "source": [
+    "Models will still be ready even though Pipeline terminated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "607b9eb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[0;39mtrue\u001b[0m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimples | jq .versions[0].state.modelsReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "da02fcd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"pipelineName\":\"tfsimples\", \"versions\":[{\"pipeline\":{\"name\":\"tfsimples\", \"uid\":\"ciepj5qi8ufs73flaiu0\", \"version\":1, \"steps\":[{\"name\":\"tfsimple1\"}, {\"name\":\"tfsimple2\", \"inputs\":[\"tfsimple1.outputs\"], \"tensorMap\":{\"tfsimple1.outputs.OUTPUT0\":\"INPUT0\", \"tfsimple1.outputs.OUTPUT1\":\"INPUT1\"}}], \"output\":{\"steps\":[\"tfsimple2.outputs\"]}, \"kubernetesMeta\":{}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T14:47:51.626155116Z\", \"modelsReady\":true}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimples.yaml\n",
+    "!seldon pipeline status tfsimples -w PipelineReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f6075200",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HTTP/1.1 200 OK\r",
+      "\r\n",
+      "\u001b[1mdate\u001b[0m: Thu, 29 Jun 2023 14:47:53 GMT\r",
+      "\r\n",
+      "\u001b[1mx-envoy-upstream-service-time\u001b[0m: 2\r",
+      "\r\n",
+      "\u001b[1mx-seldon-route\u001b[0m: :tfsimples.pipeline:\r",
+      "\r\n",
+      "\u001b[1mserver\u001b[0m: envoy\r",
+      "\r\n",
+      "\u001b[1mtransfer-encoding\u001b[0m: chunked\r",
+      "\r\n",
+      "\r",
+      "\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "082aa69b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"ready\": true\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!grpcurl -d '{\"name\":\"tfsimples\"}' \\\n",
+    "    -plaintext \\\n",
+    "    -import-path ../apis \\\n",
+    "    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \\\n",
+    "    -rpc-header seldon-model:tfsimples.pipeline \\\n",
+    "    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "4cdc413a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[0;39mtrue\u001b[0m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimples | jq .versions[0].state.modelsReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "outside-inspiration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6b7b199b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;30mnull\u001b[0m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimples | jq .versions[0].state.modelsReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e017099a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload tfsimples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d0ef494",
+   "metadata": {},
+   "source": [
+    "### Kubernetes Resource Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4cd1ddca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"NAMESPACE\"] = \"seldon-mesh\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cd4514c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'172.19.255.1'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'\n",
+    "MESH_IP=MESH_IP[0]\n",
+    "import os\n",
+    "os.environ['MESH_IP'] = MESH_IP\n",
+    "MESH_IP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f0bae6ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/tfsimples created\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./pipelines/tfsimples.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "956500af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "error: timed out waiting for the condition on pipelines/tfsimples\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=1s pipeline --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "aa7ef10e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"lastTransitionTime\":\"2022-11-14T10:25:31Z\",\"status\":\"False\",\"type\":\"ModelsReady\"}"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get pipeline tfsimples -o jsonpath='{.status.conditions[0]}' -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d9fc608b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io/tfsimple1 created\n",
+      "model.mlops.seldon.io/tfsimple2 created\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl create -f ./models/tfsimple1.yaml -n ${NAMESPACE}\n",
+    "!kubectl create -f ./models/tfsimple2.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e9bc1be2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pipeline.mlops.seldon.io/tfsimples condition met\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl wait --for condition=ready --timeout=300s pipeline --all -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "757cda1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"lastTransitionTime\":\"2022-11-14T10:25:49Z\",\"status\":\"True\",\"type\":\"ModelsReady\"}"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl get pipeline tfsimples -o jsonpath='{.status.conditions[0]}' -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "2c1fb9fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model.mlops.seldon.io \"tfsimple1\" deleted\n",
+      "model.mlops.seldon.io \"tfsimple2\" deleted\n",
+      "pipeline.mlops.seldon.io \"tfsimples\" deleted\n"
+     ]
+    }
+   ],
+   "source": [
+    "!kubectl delete -f ./models/tfsimple1.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./models/tfsimple2.yaml -n ${NAMESPACE}\n",
+    "!kubectl delete -f ./pipelines/tfsimples.yaml -n ${NAMESPACE}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9e700fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/pipeline-to-pipeline.ipynb b/docs/examples/notebooks/pipeline-to-pipeline.ipynb
new file mode 100644
index 0000000000..738b14d1dd
--- /dev/null
+++ b/docs/examples/notebooks/pipeline-to-pipeline.ipynb
@@ -0,0 +1,2693 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Pipeline to Pipeline Examples\n",
+    "\n",
+    "This notebook illustrates a series of Pipelines that are joined together.\n",
+    "\n",
+    "### Models Used\n",
+    "\n",
+    " * `gs://seldon-models/triton/simple` an example Triton tensorflow model that takes 2 inputs INPUT0 and INPUT1 and adds them to produce OUTPUT0 and also subtracts INPUT1 from INPUT0 to produce OUTPUT1. See [here](https://github.com/triton-inference-server/server/tree/main/docs/examples/model_repository/simple) for the original source code and license.\n",
+    " * Other models can be found at https://github.com/SeldonIO/triton-python-examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "running-antarctica",
+   "metadata": {},
+   "source": [
+    "### Pipeline pulling from one other Pipeline\n",
+    "\n",
+    "![pipeline-to-pipeline](img_pipeline1.jpg)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "0f2ab2a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml\n",
+    "!cat ./models/tfsimple2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "f9e073d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model load -f ./models/tfsimple2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "997b4028",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "3d017ede",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple1\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "following-winning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "artistic-kentucky",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimple\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimple\",\r\n",
+      "        \"uid\": \"cieq5dqi8ufs73flaj4g\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple1\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple1.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T15:26:48.074696631Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "87f10a5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        2,\r\n",
+      "        4,\r\n",
+      "        6,\r\n",
+      "        8,\r\n",
+      "        10,\r\n",
+      "        12,\r\n",
+      "        14,\r\n",
+      "        16,\r\n",
+      "        18,\r\n",
+      "        20,\r\n",
+      "        22,\r\n",
+      "        24,\r\n",
+      "        26,\r\n",
+      "        28,\r\n",
+      "        30,\r\n",
+      "        32\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "00575d89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple-extended\r\n",
+      "spec:\r\n",
+      "  input:\r\n",
+      "    externalInputs:\r\n",
+      "      - tfsimple.outputs\r\n",
+      "    tensorMap:\r\n",
+      "      tfsimple.outputs.OUTPUT0: INPUT0\r\n",
+      "      tfsimple.outputs.OUTPUT1: INPUT1\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple2\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple2\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple-extended.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "8fcd8947",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple-extended.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "5a69008c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimple-extended\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimple-extended\",\r\n",
+      "        \"uid\": \"cieq5h2i8ufs73flaj50\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple2\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple2.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {},\r\n",
+      "        \"input\": {\r\n",
+      "          \"externalInputs\": [\r\n",
+      "            \"tfsimple.outputs\"\r\n",
+      "          ],\r\n",
+      "          \"tensorMap\": {\r\n",
+      "            \"tfsimple.outputs.OUTPUT0\": \"INPUT0\",\r\n",
+      "            \"tfsimple.outputs.OUTPUT1\": \"INPUT1\"\r\n",
+      "          }\r\n",
+      "        }\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T15:27:01.095715504Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple-extended -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "e7e19259",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"\",\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2,\r\n",
+      "\t\t\t\t4,\r\n",
+      "\t\t\t\t6,\r\n",
+      "\t\t\t\t8,\r\n",
+      "\t\t\t\t10,\r\n",
+      "\t\t\t\t12,\r\n",
+      "\t\t\t\t14,\r\n",
+      "\t\t\t\t16,\r\n",
+      "\t\t\t\t18,\r\n",
+      "\t\t\t\t20,\r\n",
+      "\t\t\t\t22,\r\n",
+      "\t\t\t\t24,\r\n",
+      "\t\t\t\t26,\r\n",
+      "\t\t\t\t28,\r\n",
+      "\t\t\t\t30,\r\n",
+      "\t\t\t\t32\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT0\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t},\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT1\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple --header x-request-id=test-id \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "7e943b73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple1.inputs\ttest-id\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}\r\n",
+      "seldon.default.model.tfsimple1.outputs\ttest-id\t{\"modelName\":\"tfsimple1_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple.inputs\ttest-id\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple.outputs\ttest-id\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "46db0594",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple2.inputs\ttest-id\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.model.tfsimple2.outputs\ttest-id\t{\"modelName\":\"tfsimple2_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended.inputs\ttest-id\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended.outputs\ttest-id\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-extended"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "dimensional-hours",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload tfsimple-extended\n",
+    "!seldon pipeline unload tfsimple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "outside-inspiration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca596799",
+   "metadata": {},
+   "source": [
+    "### Pipeline pulling from two other Pipelines\n",
+    "\n",
+    "![pipeline-to-pipeline](img_pipeline2.jpg)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "2080d433",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml\n",
+    "!cat ./models/tfsimple2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "bd345e86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model load -f ./models/tfsimple2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "5cf517d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "61230cb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple1\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "3ddcd6c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "761c6bbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimple\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimple\",\r\n",
+      "        \"uid\": \"cieq6aai8ufs73flaj5g\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple1\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple1.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T15:28:41.766794892Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "7ac2e813",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        2,\r\n",
+      "        4,\r\n",
+      "        6,\r\n",
+      "        8,\r\n",
+      "        10,\r\n",
+      "        12,\r\n",
+      "        14,\r\n",
+      "        16,\r\n",
+      "        18,\r\n",
+      "        20,\r\n",
+      "        22,\r\n",
+      "        24,\r\n",
+      "        26,\r\n",
+      "        28,\r\n",
+      "        30,\r\n",
+      "        32\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "ae92d1db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Pipeline\n",
+      "metadata:\n",
+      "  name: tfsimple-extended\n",
+      "spec:\n",
+      "  input:\n",
+      "    externalInputs:\n",
+      "      - tfsimple.outputs\n",
+      "    tensorMap:\n",
+      "      tfsimple.outputs.OUTPUT0: INPUT0\n",
+      "      tfsimple.outputs.OUTPUT1: INPUT1\n",
+      "  steps:\n",
+      "    - name: tfsimple2\n",
+      "  output:\n",
+      "    steps:\n",
+      "    - tfsimple2\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Pipeline\n",
+      "metadata:\n",
+      "  name: tfsimple-extended2\n",
+      "spec:\n",
+      "  input:\n",
+      "    externalInputs:\n",
+      "      - tfsimple.outputs\n",
+      "    tensorMap:\n",
+      "      tfsimple.outputs.OUTPUT0: INPUT0\n",
+      "      tfsimple.outputs.OUTPUT1: INPUT1\n",
+      "  steps:\n",
+      "    - name: tfsimple2\n",
+      "  output:\n",
+      "    steps:\n",
+      "    - tfsimple2\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Pipeline\n",
+      "metadata:\n",
+      "  name: tfsimple-combined\n",
+      "spec:\n",
+      "  input:\n",
+      "    externalInputs:\n",
+      "      - tfsimple-extended.outputs.OUTPUT0\n",
+      "      - tfsimple-extended2.outputs.OUTPUT1\n",
+      "    tensorMap:\n",
+      "      tfsimple-extended.outputs.OUTPUT0: INPUT0\n",
+      "      tfsimple-extended2.outputs.OUTPUT1: INPUT1\n",
+      "  steps:\n",
+      "    - name: tfsimple2\n",
+      "  output:\n",
+      "    steps:\n",
+      "    - tfsimple2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple-extended.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./pipelines/tfsimple-extended2.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./pipelines/tfsimple-combined.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "0aca3e43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple-extended.yaml\n",
+    "!seldon pipeline load -f ./pipelines/tfsimple-extended2.yaml\n",
+    "!seldon pipeline load -f ./pipelines/tfsimple-combined.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "5a3cfc5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"pipelineName\":\"tfsimple-extended\", \"versions\":[{\"pipeline\":{\"name\":\"tfsimple-extended\", \"uid\":\"cieq6dai8ufs73flaj60\", \"version\":1, \"steps\":[{\"name\":\"tfsimple2\"}], \"output\":{\"steps\":[\"tfsimple2.outputs\"]}, \"kubernetesMeta\":{}, \"input\":{\"externalInputs\":[\"tfsimple.outputs\"], \"tensorMap\":{\"tfsimple.outputs.OUTPUT0\":\"INPUT0\", \"tfsimple.outputs.OUTPUT1\":\"INPUT1\"}}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T15:28:53.963808852Z\", \"modelsReady\":true}}]}\n",
+      "{\"pipelineName\":\"tfsimple-extended2\", \"versions\":[{\"pipeline\":{\"name\":\"tfsimple-extended2\", \"uid\":\"cieq6dai8ufs73flaj6g\", \"version\":1, \"steps\":[{\"name\":\"tfsimple2\"}], \"output\":{\"steps\":[\"tfsimple2.outputs\"]}, \"kubernetesMeta\":{}, \"input\":{\"externalInputs\":[\"tfsimple.outputs\"], \"tensorMap\":{\"tfsimple.outputs.OUTPUT0\":\"INPUT0\", \"tfsimple.outputs.OUTPUT1\":\"INPUT1\"}}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T15:28:54.087670106Z\", \"modelsReady\":true}}]}\n",
+      "{\"pipelineName\":\"tfsimple-combined\", \"versions\":[{\"pipeline\":{\"name\":\"tfsimple-combined\", \"uid\":\"cieq6dii8ufs73flaj70\", \"version\":1, \"steps\":[{\"name\":\"tfsimple2\"}], \"output\":{\"steps\":[\"tfsimple2.outputs\"]}, \"kubernetesMeta\":{}, \"input\":{\"externalInputs\":[\"tfsimple-extended.outputs.OUTPUT0\", \"tfsimple-extended2.outputs.OUTPUT1\"], \"tensorMap\":{\"tfsimple-extended.outputs.OUTPUT0\":\"INPUT0\", \"tfsimple-extended2.outputs.OUTPUT1\":\"INPUT1\"}}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T15:28:54.330770841Z\", \"modelsReady\":true}}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple-extended -w PipelineReady\n",
+    "!seldon pipeline status tfsimple-extended2 -w PipelineReady\n",
+    "!seldon pipeline status tfsimple-combined -w PipelineReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "6d48a725",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"\",\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2,\r\n",
+      "\t\t\t\t4,\r\n",
+      "\t\t\t\t6,\r\n",
+      "\t\t\t\t8,\r\n",
+      "\t\t\t\t10,\r\n",
+      "\t\t\t\t12,\r\n",
+      "\t\t\t\t14,\r\n",
+      "\t\t\t\t16,\r\n",
+      "\t\t\t\t18,\r\n",
+      "\t\t\t\t20,\r\n",
+      "\t\t\t\t22,\r\n",
+      "\t\t\t\t24,\r\n",
+      "\t\t\t\t26,\r\n",
+      "\t\t\t\t28,\r\n",
+      "\t\t\t\t30,\r\n",
+      "\t\t\t\t32\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT0\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t},\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT1\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple --header x-request-id=test-id2 \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "2232df0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple1.inputs\ttest-id2\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}\r\n",
+      "seldon.default.model.tfsimple1.outputs\ttest-id2\t{\"modelName\":\"tfsimple1_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple.inputs\ttest-id2\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple.outputs\ttest-id2\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "b583fc48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple2.inputs\ttest-id2\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\t\tx-request-id=[test-id2]\tx-forwarded-proto=[http]\tx-seldon-route=[:tfsimple1_1:]\tx-envoy-upstream-service-time=[1]\tpipeline=[tfsimple-extended]\ttraceparent=[00-e438b82ad361ac2d5481bcfc494074d2-e468d06afdab8f52-01]\tx-envoy-expected-rq-timeout-ms=[60000]\r\n",
+      "seldon.default.model.tfsimple2.outputs\ttest-id2\t{\"modelName\":\"tfsimple2_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\t\tx-envoy-expected-rq-timeout-ms=[60000]\tx-request-id=[test-id2]\tx-forwarded-proto=[http]\tx-seldon-route=[:tfsimple1_1: :tfsimple2_1:]\tx-envoy-upstream-service-time=[1]\tpipeline=[tfsimple-extended]\ttraceparent=[00-e438b82ad361ac2d5481bcfc494074d2-73bd1ee54a94d8fb-01]\r\n",
+      "seldon.default.pipeline.tfsimple-extended.inputs\ttest-id\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\t\tpipeline=[tfsimple-extended]\ttraceparent=[00-3a6047efa647efc2b3fc5266ae023d23-fee12926788ce3b6-01]\tx-envoy-expected-rq-timeout-ms=[60000]\tx-request-id=[test-id]\tx-forwarded-proto=[http]\tx-envoy-upstream-service-time=[5]\tx-seldon-route=[:tfsimple1_1:]\r\n",
+      "seldon.default.pipeline.tfsimple-extended.inputs\ttest-id2\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\t\tx-forwarded-proto=[http]\tx-seldon-route=[:tfsimple1_1:]\tx-envoy-upstream-service-time=[1]\tpipeline=[tfsimple-extended]\ttraceparent=[00-e438b82ad361ac2d5481bcfc494074d2-4df8459a992e0278-01]\tx-envoy-expected-rq-timeout-ms=[60000]\tx-request-id=[test-id2]\r\n",
+      "seldon.default.pipeline.tfsimple-extended.outputs\ttest-id\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\t\tpipeline=[tfsimple-extended]\ttraceparent=[00-3a6047efa647efc2b3fc5266ae023d23-b2f899a739c5cafd-01]\tx-envoy-expected-rq-timeout-ms=[60000]\tx-request-id=[test-id]\tx-forwarded-proto=[http]\tx-envoy-upstream-service-time=[5]\tx-seldon-route=[:tfsimple1_1: :tfsimple2_1:]\r\n",
+      "seldon.default.pipeline.tfsimple-extended.outputs\ttest-id2\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\t\tx-envoy-upstream-service-time=[1]\tpipeline=[tfsimple-extended]\ttraceparent=[00-e438b82ad361ac2d5481bcfc494074d2-dfa399143feec23d-01]\tx-envoy-expected-rq-timeout-ms=[60000]\tx-request-id=[test-id2]\tx-forwarded-proto=[http]\tx-seldon-route=[:tfsimple1_1: :tfsimple2_1:]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-extended --offset 2 --verbose"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "4da09374",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.pipeline.tfsimple-extended2.inputs\ttest-id3\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended2.inputs\ttest-id\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended2.outputs\ttest-id3\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended2.outputs\ttest-id\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-extended2 --offset 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "95fe7d91",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple2.inputs\ttest-id\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\"]}\r\n",
+      "seldon.default.model.tfsimple2.outputs\ttest-id\t{\"modelName\":\"tfsimple2_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-combined.inputs\ttest-id\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-combined.outputs\ttest-id\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-combined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "7baed85b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload tfsimple-extended\n",
+    "!seldon pipeline unload tfsimple-extended2\n",
+    "!seldon pipeline unload tfsimple-combined\n",
+    "!seldon pipeline unload tfsimple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e4e9c31c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e0ece15",
+   "metadata": {},
+   "source": [
+    "### Pipeline pullin from one pipeline with a trigger to another\n",
+    "\n",
+    "![pipeline-to-pipeline](img_pipeline3.jpg)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "2edfe335",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml\n",
+    "!cat ./models/tfsimple2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "2cb492ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model load -f ./models/tfsimple2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "61177684",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "2f05e948",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple1\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "d51beb10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "754b5288",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimple\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimple\",\r\n",
+      "        \"uid\": \"ciepkmii8ufs73flaj2g\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple1\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple1.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-06-29T14:51:06.822716088Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "cf723042",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        2,\r\n",
+      "        4,\r\n",
+      "        6,\r\n",
+      "        8,\r\n",
+      "        10,\r\n",
+      "        12,\r\n",
+      "        14,\r\n",
+      "        16,\r\n",
+      "        18,\r\n",
+      "        20,\r\n",
+      "        22,\r\n",
+      "        24,\r\n",
+      "        26,\r\n",
+      "        28,\r\n",
+      "        30,\r\n",
+      "        32\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "54d4d628",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Pipeline\n",
+      "metadata:\n",
+      "  name: tfsimple-extended\n",
+      "spec:\n",
+      "  input:\n",
+      "    externalInputs:\n",
+      "      - tfsimple.outputs\n",
+      "    tensorMap:\n",
+      "      tfsimple.outputs.OUTPUT0: INPUT0\n",
+      "      tfsimple.outputs.OUTPUT1: INPUT1\n",
+      "  steps:\n",
+      "    - name: tfsimple2\n",
+      "  output:\n",
+      "    steps:\n",
+      "    - tfsimple2\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Pipeline\n",
+      "metadata:\n",
+      "  name: tfsimple-extended2\n",
+      "spec:\n",
+      "  input:\n",
+      "    externalInputs:\n",
+      "      - tfsimple.outputs\n",
+      "    tensorMap:\n",
+      "      tfsimple.outputs.OUTPUT0: INPUT0\n",
+      "      tfsimple.outputs.OUTPUT1: INPUT1\n",
+      "  steps:\n",
+      "    - name: tfsimple2\n",
+      "  output:\n",
+      "    steps:\n",
+      "    - tfsimple2\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Pipeline\n",
+      "metadata:\n",
+      "  name: tfsimple-combined-trigger\n",
+      "spec:\n",
+      "  input:\n",
+      "    externalInputs:\n",
+      "      - tfsimple-extended.outputs\n",
+      "    externalTriggers:\n",
+      "      - tfsimple-extended2.outputs\n",
+      "    tensorMap:\n",
+      "      tfsimple-extended.outputs.OUTPUT0: INPUT0\n",
+      "      tfsimple-extended.outputs.OUTPUT1: INPUT1\n",
+      "  steps:\n",
+      "    - name: tfsimple2\n",
+      "  output:\n",
+      "    steps:\n",
+      "    - tfsimple2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple-extended.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./pipelines/tfsimple-extended2.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./pipelines/tfsimple-combined-trigger.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "3d8db4fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple-extended.yaml\n",
+    "!seldon pipeline load -f ./pipelines/tfsimple-extended2.yaml\n",
+    "!seldon pipeline load -f ./pipelines/tfsimple-combined-trigger.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "1bb001e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"pipelineName\":\"tfsimple-extended\", \"versions\":[{\"pipeline\":{\"name\":\"tfsimple-extended\", \"uid\":\"ciepkoii8ufs73flaj30\", \"version\":1, \"steps\":[{\"name\":\"tfsimple2\"}], \"output\":{\"steps\":[\"tfsimple2.outputs\"]}, \"kubernetesMeta\":{}, \"input\":{\"externalInputs\":[\"tfsimple.outputs\"], \"tensorMap\":{\"tfsimple.outputs.OUTPUT0\":\"INPUT0\", \"tfsimple.outputs.OUTPUT1\":\"INPUT1\"}}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T14:51:14.937544974Z\", \"modelsReady\":true}}]}\n",
+      "{\"pipelineName\":\"tfsimple-extended2\", \"versions\":[{\"pipeline\":{\"name\":\"tfsimple-extended2\", \"uid\":\"ciepkoii8ufs73flaj3g\", \"version\":1, \"steps\":[{\"name\":\"tfsimple2\"}], \"output\":{\"steps\":[\"tfsimple2.outputs\"]}, \"kubernetesMeta\":{}, \"input\":{\"externalInputs\":[\"tfsimple.outputs\"], \"tensorMap\":{\"tfsimple.outputs.OUTPUT0\":\"INPUT0\", \"tfsimple.outputs.OUTPUT1\":\"INPUT1\"}}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T14:51:15.062097751Z\", \"modelsReady\":true}}]}\n",
+      "{\"pipelineName\":\"tfsimple-combined-trigger\", \"versions\":[{\"pipeline\":{\"name\":\"tfsimple-combined-trigger\", \"uid\":\"ciepkoqi8ufs73flaj40\", \"version\":1, \"steps\":[{\"name\":\"tfsimple2\"}], \"output\":{\"steps\":[\"tfsimple2.outputs\"]}, \"kubernetesMeta\":{}, \"input\":{\"externalInputs\":[\"tfsimple-extended.outputs\"], \"externalTriggers\":[\"tfsimple-extended2.outputs\"], \"tensorMap\":{\"tfsimple-extended.outputs.OUTPUT0\":\"INPUT0\", \"tfsimple-extended.outputs.OUTPUT1\":\"INPUT1\"}}}, \"state\":{\"pipelineVersion\":1, \"status\":\"PipelineReady\", \"reason\":\"created pipeline\", \"lastChangeTimestamp\":\"2023-06-29T14:51:15.326170068Z\", \"modelsReady\":true}}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple-extended -w PipelineReady\n",
+    "!seldon pipeline status tfsimple-extended2 -w PipelineReady\n",
+    "!seldon pipeline status tfsimple-combined-trigger -w PipelineReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "dac6c62c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"\",\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2,\r\n",
+      "\t\t\t\t4,\r\n",
+      "\t\t\t\t6,\r\n",
+      "\t\t\t\t8,\r\n",
+      "\t\t\t\t10,\r\n",
+      "\t\t\t\t12,\r\n",
+      "\t\t\t\t14,\r\n",
+      "\t\t\t\t16,\r\n",
+      "\t\t\t\t18,\r\n",
+      "\t\t\t\t20,\r\n",
+      "\t\t\t\t22,\r\n",
+      "\t\t\t\t24,\r\n",
+      "\t\t\t\t26,\r\n",
+      "\t\t\t\t28,\r\n",
+      "\t\t\t\t30,\r\n",
+      "\t\t\t\t32\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT0\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t},\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT1\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple --header x-request-id=test-id3 \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "965e5f92",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple1.inputs\ttest-id3\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}\r\n",
+      "seldon.default.model.tfsimple1.outputs\ttest-id3\t{\"modelName\":\"tfsimple1_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple.inputs\ttest-id3\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple.outputs\ttest-id3\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "079b4e27",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple2.outputs\ttest-id3\t{\"modelName\":\"tfsimple2_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended.inputs\ttest-id3\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended.inputs\ttest-id3\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended.outputs\ttest-id3\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended.outputs\ttest-id3\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-extended --offset 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "233f42f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple2.inputs\ttest-id3\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended2.inputs\ttest-id3\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended2.inputs\ttest-id3\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended2.outputs\ttest-id3\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended2.outputs\ttest-id3\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-extended2 --offset 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "e674c8d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple2.inputs\ttest-id3\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\"]}\r\n",
+      "seldon.default.model.tfsimple2.outputs\ttest-id3\t{\"modelName\":\"tfsimple2_1\", \"modelVersion\":\"1\", \"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-combined-trigger.inputs\ttest-id3\t{\"inputs\":[{\"name\":\"INPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {\"name\":\"INPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}], \"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\", \"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-combined-trigger.outputs\ttest-id3\t{\"outputs\":[{\"name\":\"OUTPUT0\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64]}}, {\"name\":\"OUTPUT1\", \"datatype\":\"INT32\", \"shape\":[\"1\", \"16\"], \"contents\":{\"intContents\":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-combined-trigger"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "37e1399e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon pipeline unload tfsimple-extended\n",
+    "!seldon pipeline unload tfsimple-extended2\n",
+    "!seldon pipeline unload tfsimple-combined-trigger\n",
+    "!seldon pipeline unload tfsimple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "81480753",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2614d43",
+   "metadata": {},
+   "source": [
+    "### Pipeline pulling from one other Pipeline Step\n",
+    "\n",
+    "![pipeline-to-pipeline](img_pipeline4.jpg)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "d006d9b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml\n",
+    "!cat ./models/tfsimple2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "30f4ad48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model load -f ./models/tfsimple2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "ae024817",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "e180a273",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple1\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "48dde798",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "77ee161a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimple\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimple\",\r\n",
+      "        \"uid\": \"cg5g6m46dpcs73c4qhl0\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple1\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple1.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-03-10T10:15:52.515491456Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "86280675",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        2,\r\n",
+      "        4,\r\n",
+      "        6,\r\n",
+      "        8,\r\n",
+      "        10,\r\n",
+      "        12,\r\n",
+      "        14,\r\n",
+      "        16,\r\n",
+      "        18,\r\n",
+      "        20,\r\n",
+      "        22,\r\n",
+      "        24,\r\n",
+      "        26,\r\n",
+      "        28,\r\n",
+      "        30,\r\n",
+      "        32\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "b06ff6ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple-extended-step\r\n",
+      "spec:\r\n",
+      "  input:\r\n",
+      "    externalInputs:\r\n",
+      "      - tfsimple.step.tfsimple1.outputs\r\n",
+      "    tensorMap:\r\n",
+      "      tfsimple.step.tfsimple1.outputs.OUTPUT0: INPUT0\r\n",
+      "      tfsimple.step.tfsimple1.outputs.OUTPUT1: INPUT1\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple2\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple2\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple-extended-step.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "008a1465",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple-extended-step.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "7c5c2065",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimple-extended-step\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimple-extended-step\",\r\n",
+      "        \"uid\": \"cg5g6ns6dpcs73c4qhlg\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple2\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple2.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {},\r\n",
+      "        \"input\": {\r\n",
+      "          \"externalInputs\": [\r\n",
+      "            \"tfsimple.step.tfsimple1.outputs\"\r\n",
+      "          ],\r\n",
+      "          \"tensorMap\": {\r\n",
+      "            \"tfsimple.step.tfsimple1.outputs.OUTPUT0\": \"INPUT0\",\r\n",
+      "            \"tfsimple.step.tfsimple1.outputs.OUTPUT1\": \"INPUT1\"\r\n",
+      "          }\r\n",
+      "        }\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-03-10T10:15:59.634720740Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple-extended-step -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "0706c855",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"\",\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2,\r\n",
+      "\t\t\t\t4,\r\n",
+      "\t\t\t\t6,\r\n",
+      "\t\t\t\t8,\r\n",
+      "\t\t\t\t10,\r\n",
+      "\t\t\t\t12,\r\n",
+      "\t\t\t\t14,\r\n",
+      "\t\t\t\t16,\r\n",
+      "\t\t\t\t18,\r\n",
+      "\t\t\t\t20,\r\n",
+      "\t\t\t\t22,\r\n",
+      "\t\t\t\t24,\r\n",
+      "\t\t\t\t26,\r\n",
+      "\t\t\t\t28,\r\n",
+      "\t\t\t\t30,\r\n",
+      "\t\t\t\t32\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT0\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t},\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT1\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "7d03514f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple1.inputs\tcg5g6ogfh5ss73a44vvg\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}}]}\t\tpipeline=[tfsimple]\ttraceparent=[00-2c66ff815d920ad238365be52a4467f5-90824e4cb70c3242-01]\tx-forwarded-proto=[http]\tx-envoy-expected-rq-timeout-ms=[60000]\tx-request-id=[cg5g6ogfh5ss73a44vvg]\r\n",
+      "seldon.default.model.tfsimple1.outputs\tcg5g6ogfh5ss73a44vvg\t{\"modelName\":\"tfsimple1_1\",\"modelVersion\":\"1\",\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}\t\tx-request-id=[cg5g6ogfh5ss73a44vvg]\tpipeline=[tfsimple]\tx-envoy-upstream-service-time=[8]\tx-seldon-route=[:tfsimple1_1:]\ttraceparent=[00-2c66ff815d920ad238365be52a4467f5-ca023a540fa463b3-01]\tx-forwarded-proto=[http]\tx-envoy-expected-rq-timeout-ms=[60000]\r\n",
+      "seldon.default.pipeline.tfsimple.inputs\tcg5g6ogfh5ss73a44vvg\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}}]}\t\tpipeline=[tfsimple]\tx-request-id=[cg5g6ogfh5ss73a44vvg]\ttraceparent=[00-2c66ff815d920ad238365be52a4467f5-843d6ce39292396d-01]\tx-forwarded-proto=[http]\tx-envoy-expected-rq-timeout-ms=[60000]\r\n",
+      "seldon.default.pipeline.tfsimple.outputs\tcg5g6ogfh5ss73a44vvg\t{\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}\t\tx-envoy-expected-rq-timeout-ms=[60000]\tx-request-id=[cg5g6ogfh5ss73a44vvg]\tx-envoy-upstream-service-time=[8]\tx-seldon-route=[:tfsimple1_1:]\tpipeline=[tfsimple]\ttraceparent=[00-2c66ff815d920ad238365be52a4467f5-ee7527353e9fe5a2-01]\tx-forwarded-proto=[http]\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple --verbose"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "36b2133e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple2.inputs\tcg5g6ogfh5ss73a44vvg\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}],\"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\",\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.model.tfsimple2.outputs\tcg5g6ogfh5ss73a44vvg\t{\"modelName\":\"tfsimple2_1\",\"modelVersion\":\"1\",\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended-step.inputs\tcg5g6ogfh5ss73a44vvg\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}],\"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\",\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended-step.outputs\tcg5g6ogfh5ss73a44vvg\t{\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-extended-step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "09e5293f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline unload tfsimple-extended-step\n",
+    "!seldon pipeline unload tfsimple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "dad54862",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec3fbaeb",
+   "metadata": {},
+   "source": [
+    "### Pipeline pulling from two other Pipeline steps from same model\n",
+    "\n",
+    "![pipeline-to-pipeline](img_pipeline5.jpg)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "c5ff042a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple1\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Model\n",
+      "metadata:\n",
+      "  name: tfsimple2\n",
+      "spec:\n",
+      "  storageUri: \"gs://seldon-models/triton/simple\"\n",
+      "  requirements:\n",
+      "  - tensorflow\n",
+      "  memory: 100Ki\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/tfsimple1.yaml\n",
+    "!cat ./models/tfsimple2.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "1218d50c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/tfsimple1.yaml \n",
+    "!seldon model load -f ./models/tfsimple2.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "c853259a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status tfsimple1 -w ModelAvailable | jq -M .\n",
+    "!seldon model status tfsimple2 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "e141244a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: tfsimple\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: tfsimple1\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - tfsimple1\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "2d4e1f37",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "6adb818a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"tfsimple\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"tfsimple\",\r\n",
+      "        \"uid\": \"cg5g6u46dpcs73c4qhm0\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"tfsimple1\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"tfsimple1.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-03-10T10:16:24.433333171Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "d922d330",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"model_name\": \"\",\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        2,\r\n",
+      "        4,\r\n",
+      "        6,\r\n",
+      "        8,\r\n",
+      "        10,\r\n",
+      "        12,\r\n",
+      "        14,\r\n",
+      "        16,\r\n",
+      "        18,\r\n",
+      "        20,\r\n",
+      "        22,\r\n",
+      "        24,\r\n",
+      "        26,\r\n",
+      "        28,\r\n",
+      "        30,\r\n",
+      "        32\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT0\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    },\r\n",
+      "    {\r\n",
+      "      \"data\": [\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0,\r\n",
+      "        0\r\n",
+      "      ],\r\n",
+      "      \"name\": \"OUTPUT1\",\r\n",
+      "      \"shape\": [\r\n",
+      "        1,\r\n",
+      "        16\r\n",
+      "      ],\r\n",
+      "      \"datatype\": \"INT32\"\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "00f60705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Pipeline\n",
+      "metadata:\n",
+      "  name: tfsimple-extended\n",
+      "spec:\n",
+      "  input:\n",
+      "    externalInputs:\n",
+      "      - tfsimple.outputs\n",
+      "    tensorMap:\n",
+      "      tfsimple.outputs.OUTPUT0: INPUT0\n",
+      "      tfsimple.outputs.OUTPUT1: INPUT1\n",
+      "  steps:\n",
+      "    - name: tfsimple2\n",
+      "  output:\n",
+      "    steps:\n",
+      "    - tfsimple2\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Pipeline\n",
+      "metadata:\n",
+      "  name: tfsimple-extended2\n",
+      "spec:\n",
+      "  input:\n",
+      "    externalInputs:\n",
+      "      - tfsimple.outputs\n",
+      "    tensorMap:\n",
+      "      tfsimple.outputs.OUTPUT0: INPUT0\n",
+      "      tfsimple.outputs.OUTPUT1: INPUT1\n",
+      "  steps:\n",
+      "    - name: tfsimple2\n",
+      "  output:\n",
+      "    steps:\n",
+      "    - tfsimple2\n",
+      "---\n",
+      "apiVersion: mlops.seldon.io/v1alpha1\n",
+      "kind: Pipeline\n",
+      "metadata:\n",
+      "  name: tfsimple-combined-step\n",
+      "spec:\n",
+      "  input:\n",
+      "    externalInputs:\n",
+      "      - tfsimple-extended.step.tfsimple2.outputs.OUTPUT0\n",
+      "      - tfsimple-extended2.step.tfsimple2.outputs.OUTPUT0\n",
+      "    tensorMap:\n",
+      "      tfsimple-extended.step.tfsimple2.outputs.OUTPUT0: INPUT0\n",
+      "      tfsimple-extended2.step.tfsimple2.outputs.OUTPUT0: INPUT1\n",
+      "  steps:\n",
+      "    - name: tfsimple2\n",
+      "  output:\n",
+      "    steps:\n",
+      "    - tfsimple2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/tfsimple-extended.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./pipelines/tfsimple-extended2.yaml\n",
+    "!echo \"---\"\n",
+    "!cat ./pipelines/tfsimple-combined-step.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "ff620079",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/tfsimple-extended.yaml\n",
+    "!seldon pipeline load -f ./pipelines/tfsimple-extended2.yaml\n",
+    "!seldon pipeline load -f ./pipelines/tfsimple-combined-step.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "a9a381a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"pipelineName\":\"tfsimple-extended\",\"versions\":[{\"pipeline\":{\"name\":\"tfsimple-extended\",\"uid\":\"cg5g7046dpcs73c4qhmg\",\"version\":1,\"steps\":[{\"name\":\"tfsimple2\"}],\"output\":{\"steps\":[\"tfsimple2.outputs\"]},\"kubernetesMeta\":{},\"input\":{\"externalInputs\":[\"tfsimple.outputs\"],\"tensorMap\":{\"tfsimple.outputs.OUTPUT0\":\"INPUT0\",\"tfsimple.outputs.OUTPUT1\":\"INPUT1\"}}},\"state\":{\"pipelineVersion\":1,\"status\":\"PipelineReady\",\"reason\":\"created pipeline\",\"lastChangeTimestamp\":\"2023-03-10T10:16:32.576588675Z\",\"modelsReady\":true}}]}\n",
+      "{\"pipelineName\":\"tfsimple-extended2\",\"versions\":[{\"pipeline\":{\"name\":\"tfsimple-extended2\",\"uid\":\"cg5g7046dpcs73c4qhn0\",\"version\":1,\"steps\":[{\"name\":\"tfsimple2\"}],\"output\":{\"steps\":[\"tfsimple2.outputs\"]},\"kubernetesMeta\":{},\"input\":{\"externalInputs\":[\"tfsimple.outputs\"],\"tensorMap\":{\"tfsimple.outputs.OUTPUT0\":\"INPUT0\",\"tfsimple.outputs.OUTPUT1\":\"INPUT1\"}}},\"state\":{\"pipelineVersion\":1,\"status\":\"PipelineReady\",\"reason\":\"created pipeline\",\"lastChangeTimestamp\":\"2023-03-10T10:16:32.711813099Z\",\"modelsReady\":true}}]}\n",
+      "{\"pipelineName\":\"tfsimple-combined-step\",\"versions\":[{\"pipeline\":{\"name\":\"tfsimple-combined-step\",\"uid\":\"cg5g7046dpcs73c4qhng\",\"version\":1,\"steps\":[{\"name\":\"tfsimple2\"}],\"output\":{\"steps\":[\"tfsimple2.outputs\"]},\"kubernetesMeta\":{},\"input\":{\"externalInputs\":[\"tfsimple-extended.step.tfsimple2.outputs.OUTPUT0\",\"tfsimple-extended2.step.tfsimple2.outputs.OUTPUT0\"],\"tensorMap\":{\"tfsimple-extended.step.tfsimple2.outputs.OUTPUT0\":\"INPUT0\",\"tfsimple-extended2.step.tfsimple2.outputs.OUTPUT0\":\"INPUT1\"}}},\"state\":{\"pipelineVersion\":1,\"status\":\"PipelineReady\",\"reason\":\"created pipeline\",\"lastChangeTimestamp\":\"2023-03-10T10:16:33.017843490Z\",\"modelsReady\":true}}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status tfsimple-extended -w PipelineReady\n",
+    "!seldon pipeline status tfsimple-extended2 -w PipelineReady\n",
+    "!seldon pipeline status tfsimple-combined-step -w PipelineReady"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "b65a28de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "\t\"model_name\": \"\",\r\n",
+      "\t\"outputs\": [\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t2,\r\n",
+      "\t\t\t\t4,\r\n",
+      "\t\t\t\t6,\r\n",
+      "\t\t\t\t8,\r\n",
+      "\t\t\t\t10,\r\n",
+      "\t\t\t\t12,\r\n",
+      "\t\t\t\t14,\r\n",
+      "\t\t\t\t16,\r\n",
+      "\t\t\t\t18,\r\n",
+      "\t\t\t\t20,\r\n",
+      "\t\t\t\t22,\r\n",
+      "\t\t\t\t24,\r\n",
+      "\t\t\t\t26,\r\n",
+      "\t\t\t\t28,\r\n",
+      "\t\t\t\t30,\r\n",
+      "\t\t\t\t32\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT0\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t},\r\n",
+      "\t\t{\r\n",
+      "\t\t\t\"data\": [\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0,\r\n",
+      "\t\t\t\t0\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"name\": \"OUTPUT1\",\r\n",
+      "\t\t\t\"shape\": [\r\n",
+      "\t\t\t\t1,\r\n",
+      "\t\t\t\t16\r\n",
+      "\t\t\t],\r\n",
+      "\t\t\t\"datatype\": \"INT32\"\r\n",
+      "\t\t}\r\n",
+      "\t]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer tfsimple \\\n",
+    "    '{\"inputs\":[{\"name\":\"INPUT0\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]},{\"name\":\"INPUT1\",\"data\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],\"datatype\":\"INT32\",\"shape\":[1,16]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "bc1ad2dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple1.inputs\tcg5g710fh5ss73a4500g\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}}]}\r\n",
+      "seldon.default.model.tfsimple1.outputs\tcg5g710fh5ss73a4500g\t{\"modelName\":\"tfsimple1_1\",\"modelVersion\":\"1\",\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple.inputs\tcg5g710fh5ss73a4500g\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple.outputs\tcg5g710fh5ss73a4500g\t{\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "2eb548ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple2.inputs\tcg5g710fh5ss73a4500g\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}],\"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\",\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\"]}\r\n",
+      "seldon.default.model.tfsimple2.outputs\tcg5g710fh5ss73a4500g\t{\"modelName\":\"tfsimple2_1\",\"modelVersion\":\"1\",\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended.inputs\tcg5g710fh5ss73a4500g\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}],\"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\",\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended.outputs\tcg5g710fh5ss73a4500g\t{\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-extended"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "108e2c24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple2.inputs\tcg5g710fh5ss73a4500g\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}],\"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\",\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\"]}\r\n",
+      "seldon.default.model.tfsimple2.outputs\tcg5g710fh5ss73a4500g\t{\"modelName\":\"tfsimple2_1\",\"modelVersion\":\"1\",\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended2.inputs\tcg5g710fh5ss73a4500g\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}],\"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\",\"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-extended2.outputs\tcg5g710fh5ss73a4500g\t{\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-extended2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "239a6b9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seldon.default.model.tfsimple2.inputs\tcg5g710fh5ss73a4500g\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}],\"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\",\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\"]}\r\n",
+      "seldon.default.model.tfsimple2.outputs\tcg5g710fh5ss73a4500g\t{\"modelName\":\"tfsimple2_1\",\"modelVersion\":\"1\",\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}\r\n",
+      "seldon.default.pipeline.tfsimple-combined-step.inputs\tcg5g710fh5ss73a4500g\t{\"inputs\":[{\"name\":\"INPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}},{\"name\":\"INPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32]}}],\"rawInputContents\":[\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\",\"AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==\"]}\r\n",
+      "seldon.default.pipeline.tfsimple-combined-step.outputs\tcg5g710fh5ss73a4500g\t{\"outputs\":[{\"name\":\"OUTPUT0\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64]}},{\"name\":\"OUTPUT1\",\"datatype\":\"INT32\",\"shape\":[\"1\",\"16\"],\"contents\":{\"intContents\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline inspect tfsimple-combined-step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "840af9e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline unload tfsimple-extended\n",
+    "!seldon pipeline unload tfsimple-extended2\n",
+    "!seldon pipeline unload tfsimple-combined-step\n",
+    "!seldon pipeline unload tfsimple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "3f2584b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload tfsimple1\n",
+    "!seldon model unload tfsimple2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "503c0778",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/pipeline-versions.ipynb b/docs/examples/notebooks/pipeline-versions.ipynb
new file mode 100644
index 0000000000..24dc1874b6
--- /dev/null
+++ b/docs/examples/notebooks/pipeline-versions.ipynb
@@ -0,0 +1,493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Seldon V2 Non Kubernetes Pipeline Version Updates\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fca716ed",
+   "metadata": {},
+   "source": [
+    "### Model Join\n",
+    "\n",
+    "Join two flows of data from two models as input to a third model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fcbc23cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/add10.yaml \n",
+    "!seldon model load -f ./models/mul10.yaml "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f8bb2efb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status add10 -w ModelAvailable | jq -M .\n",
+    "!seldon model status mul10 -w ModelAvailable | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cd58ecba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: version-test\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: add10\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - add10\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/version-test-a.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ce9e163b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/version-test-a.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7593c77f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"version-test\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"version-test\",\r\n",
+      "        \"uid\": \"cg5g7ck6dpcs73c4qho0\",\r\n",
+      "        \"version\": 1,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"add10\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"add10.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 1,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-03-10T10:17:22.883632333Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status version-test -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0d03d5fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer version-test --inference-mode grpc \\\n",
+    " '{\"model_name\":\"outlier\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1d9878b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: version-test\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: mul10\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "    - mul10\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/version-test-b.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a1d77358",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/version-test-b.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8b92a65c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"version-test\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"version-test\",\r\n",
+      "        \"uid\": \"cg5g7ek6dpcs73c4qhog\",\r\n",
+      "        \"version\": 2,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"mul10\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"mul10.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 2,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-03-10T10:17:30.576466739Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status version-test -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "73e78ada",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          10,\r\n",
+      "          20,\r\n",
+      "          30,\r\n",
+      "          40\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer version-test --inference-mode grpc \\\n",
+    " '{\"model_name\":\"outlier\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "77c8cb8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/version-test-a.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cec42a5b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"pipelineName\": \"version-test\",\r\n",
+      "  \"versions\": [\r\n",
+      "    {\r\n",
+      "      \"pipeline\": {\r\n",
+      "        \"name\": \"version-test\",\r\n",
+      "        \"uid\": \"cg5g7g46dpcs73c4qhp0\",\r\n",
+      "        \"version\": 3,\r\n",
+      "        \"steps\": [\r\n",
+      "          {\r\n",
+      "            \"name\": \"add10\"\r\n",
+      "          }\r\n",
+      "        ],\r\n",
+      "        \"output\": {\r\n",
+      "          \"steps\": [\r\n",
+      "            \"add10.outputs\"\r\n",
+      "          ]\r\n",
+      "        },\r\n",
+      "        \"kubernetesMeta\": {}\r\n",
+      "      },\r\n",
+      "      \"state\": {\r\n",
+      "        \"pipelineVersion\": 3,\r\n",
+      "        \"status\": \"PipelineReady\",\r\n",
+      "        \"reason\": \"created pipeline\",\r\n",
+      "        \"lastChangeTimestamp\": \"2023-03-10T10:17:36.568170759Z\",\r\n",
+      "        \"modelsReady\": true\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status version-test -w PipelineReady | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2e54f9b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\r\n",
+      "  \"outputs\": [\r\n",
+      "    {\r\n",
+      "      \"name\": \"OUTPUT\",\r\n",
+      "      \"datatype\": \"FP32\",\r\n",
+      "      \"shape\": [\r\n",
+      "        \"4\"\r\n",
+      "      ],\r\n",
+      "      \"contents\": {\r\n",
+      "        \"fp32Contents\": [\r\n",
+      "          11,\r\n",
+      "          12,\r\n",
+      "          13,\r\n",
+      "          14\r\n",
+      "        ]\r\n",
+      "      }\r\n",
+      "    }\r\n",
+      "  ]\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline infer version-test --inference-mode grpc \\\n",
+    " '{\"model_name\":\"outlier\",\"inputs\":[{\"name\":\"INPUT\",\"contents\":{\"fp32_contents\":[1,2,3,4]},\"datatype\":\"FP32\",\"shape\":[4]}]}' | jq -M ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ea766ec6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline unload version-test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5e597644",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload add10\n",
+    "!seldon model unload mul10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84d6f01a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/python-examples.ipynb b/docs/examples/notebooks/python-examples.ipynb
new file mode 100644
index 0000000000..d9e3631b7f
--- /dev/null
+++ b/docs/examples/notebooks/python-examples.ipynb
@@ -0,0 +1,217 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "438f8364",
+   "metadata": {},
+   "source": [
+    "# Python example\n",
+    "\n",
+    "We will test a simple Pipeline with a cifar10 image classification model with batch requests. We assume a locally running Seldon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "138c1b53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-03-10 10:17:52.202780: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2023-03-10 10:17:52.202792: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "from typing import Dict, List\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import tensorflow as tf\n",
+    "tf.keras.backend.clear_session()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9294d563",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train, test = tf.keras.datasets.cifar10.load_data()\n",
+    "X_train, y_train = train\n",
+    "X_test, y_test = test\n",
+    "\n",
+    "X_train = X_train.astype('float32') / 255\n",
+    "X_test = X_test.astype('float32') / 255\n",
+    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d9c4686f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Model\r\n",
+      "metadata:\r\n",
+      "  name: cifar10\r\n",
+      "spec:\r\n",
+      "  storageUri: \"gs://seldon-models/triton/tf_cifar10\"\r\n",
+      "  requirements:\r\n",
+      "  - tensorflow\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./models/cifar10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4bddb7ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/cifar10.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5d5c9181",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;39m{}\u001b[0m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status cifar10 -w ModelAvailable | jq ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "838fca72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "headers = {\"Content-Type\": \"application/json\", \"seldon-model\":\"cifar10\"}\n",
+    "reqJson = json.loads('{\"inputs\":[{\"name\":\"input_1\",\"data\":[],\"datatype\":\"FP32\",\"shape\":[]}]}')\n",
+    "url = \"http://0.0.0.0:9000/v2/models/cifar10/infer\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a60c2151",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batchSz = 2\n",
+    "rows = X_train[0:0+batchSz]\n",
+    "reqJson[\"inputs\"][0][\"data\"] = rows.flatten().tolist()\n",
+    "reqJson[\"inputs\"][0][\"shape\"] = [batchSz, 32, 32, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "60d15624",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Response [200]>\n",
+      "{'model_name': 'cifar10_1', 'model_version': '1', 'outputs': [{'name': 'fc10', 'datatype': 'FP32', 'shape': [2, 10], 'data': [1.4500123768357298e-08, 1.2525751547087793e-09, 1.6298442062634422e-07, 0.11529377847909927, 1.7431396770462015e-07, 6.185648544487776e-06, 0.8846994638442993, 6.073928471295176e-09, 7.437921567543526e-08, 4.7317341156372095e-09, 1.2644900380109902e-06, 4.881486770358379e-09, 1.51534673697995e-09, 8.490656178139488e-09, 5.513119405442524e-10, 1.161723717224561e-09, 5.772940459003451e-10, 2.883980414480902e-07, 0.0006149015971459448, 0.9993835687637329]}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "response_raw = requests.post(url, json=reqJson, headers=headers)\n",
+    "print(response_raw)\n",
+    "print(response_raw.json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2b93057d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload cifar10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5c62fae",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/rolling-update.ipynb b/docs/examples/notebooks/rolling-update.ipynb
new file mode 100644
index 0000000000..35dfb4e4a8
--- /dev/null
+++ b/docs/examples/notebooks/rolling-update.ipynb
@@ -0,0 +1,131 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lesbian-springer",
+   "metadata": {},
+   "source": [
+    "## Rolling Update Examples\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b1925df",
+   "metadata": {},
+   "source": [
+    "### SKLearn Iris Model\n",
+    "\n",
+    "We use a simple sklearn iris classification model and do a rolling update"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "interracial-secret",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{\n",
+      "\t\"model_name\": \"iris_1\",\n",
+      "\t\"model_version\": \"1\",\n",
+      "\t\"id\": \"e36b1d20-ddbb-4bbc-9649-290a8bb14ac4\",\n",
+      "\t\"parameters\": {},\n",
+      "\t\"outputs\": [\n",
+      "\t\t{\n",
+      "\t\t\t\"name\": \"predict\",\n",
+      "\t\t\t\"shape\": [\n",
+      "\t\t\t\t1,\n",
+      "\t\t\t\t1\n",
+      "\t\t\t],\n",
+      "\t\t\t\"datatype\": \"INT64\",\n",
+      "\t\t\t\"data\": [\n",
+      "\t\t\t\t2\n",
+      "\t\t\t]\n",
+      "\t\t}\n",
+      "\t]\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/iris-v1.yaml\n",
+    "!seldon model status iris -w ModelAvailable \n",
+    "!seldon model infer iris -i 1 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1f15ac54",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "Success: map[:iris_1::216 :iris_2::984]\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/iris-v2.yaml\n",
+    "!seldon model infer iris --seconds 5 \\\n",
+    "  '{\"inputs\": [{\"name\": \"predict\", \"shape\": [1, 4], \"datatype\": \"FP32\", \"data\": [[1, 2, 3, 4]]}]}' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ceramic-illness",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload iris"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3720658f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/train.ipynb b/docs/examples/notebooks/train.ipynb
new file mode 100644
index 0000000000..cbdd6e2c66
--- /dev/null
+++ b/docs/examples/notebooks/train.ipynb
@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "95804711",
+   "metadata": {},
+   "source": [
+    "## Train Outlier Detector\n",
+    "\n",
+    "Based on [Alibi Detect Example](https://docs.seldon.io/projects/alibi-detect/en/stable/examples/od_vae_cifar10.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "97fa4aae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-03-09 17:05:36.275254: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
+      "2023-03-09 17:05:36.275268: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import logging\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "tf.keras.backend.clear_session()\n",
+    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Dense, Layer, Reshape, InputLayer\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from alibi_detect.models.tensorflow import elbo\n",
+    "from alibi_detect.od import OutlierVAE\n",
+    "from alibi_detect.utils.fetching import fetch_detector\n",
+    "from alibi_detect.utils.perturbation import apply_mask\n",
+    "from alibi_detect.saving import save_detector, load_detector\n",
+    "from alibi_detect.utils.visualize import plot_instance_score, plot_feature_outlier_image\n",
+    "\n",
+    "logger = tf.get_logger()\n",
+    "logger.setLevel(logging.ERROR)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f6f421df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train, test = tf.keras.datasets.cifar10.load_data()\n",
+    "X_train, y_train = train\n",
+    "X_test, y_test = test\n",
+    "\n",
+    "X_train = X_train.astype('float32') / 255\n",
+    "X_test = X_test.astype('float32') / 255\n",
+    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b80363be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-03-09 17:05:46.653642: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/clive/miniconda3/envs/scv2/lib/python3.9/site-packages/cv2/../../lib64:\n",
+      "2023-03-09 17:05:46.653676: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
+      "2023-03-09 17:05:46.653706: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (clive-ThinkPad-P1-Gen-5): /proc/driver/nvidia/version does not exist\n",
+      "2023-03-09 17:05:46.654247: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2023-03-09 17:05:46.954048: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 614400000 exceeds 10% of free system memory.\n",
+      "2023-03-09 17:05:47.164088: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 614400000 exceeds 10% of free system memory.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "782/782 [=] - 123s 157ms/step - loss_ma: 8047.0571\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-03-09 17:07:50.493324: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 614400000 exceeds 10% of free system memory.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "782/782 [=] - 121s 154ms/step - loss_ma: -2349.7290\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-03-09 17:09:51.538684: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 614400000 exceeds 10% of free system memory.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "782/782 [=] - 124s 158ms/step - loss_ma: -3547.2254\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-03-09 17:11:55.269597: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 614400000 exceeds 10% of free system memory.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 64/782 [.] - ETA: 1:50 - loss_ma: -3935.1125"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 29>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m od \u001b[38;5;241m=\u001b[39m OutlierVAE(threshold\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.015\u001b[39m,  \u001b[38;5;66;03m# threshold for outlier score\u001b[39;00m\n\u001b[1;32m     23\u001b[0m                 score_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmse\u001b[39m\u001b[38;5;124m'\u001b[39m,  \u001b[38;5;66;03m# use MSE of reconstruction error for outlier detection\u001b[39;00m\n\u001b[1;32m     24\u001b[0m                 encoder_net\u001b[38;5;241m=\u001b[39mencoder_net,  \u001b[38;5;66;03m# can also pass VAE model instead\u001b[39;00m\n\u001b[1;32m     25\u001b[0m                 decoder_net\u001b[38;5;241m=\u001b[39mdecoder_net,  \u001b[38;5;66;03m# of separate encoder and decoder\u001b[39;00m\n\u001b[1;32m     26\u001b[0m                 latent_dim\u001b[38;5;241m=\u001b[39mlatent_dim,\n\u001b[1;32m     27\u001b[0m                 samples\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m# train\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m \u001b[43mod\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     30\u001b[0m \u001b[43m        \u001b[49m\u001b[43mloss_fn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43melbo\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     31\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcov_elbo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m.05\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[43m        \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     33\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;66;03m# save the trained outlier detector\u001b[39;00m\n\u001b[1;32m     36\u001b[0m save_detector(od, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./outlier-detector\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/miniconda3/envs/scv2/lib/python3.9/site-packages/alibi_detect/od/vae.py:134\u001b[0m, in \u001b[0;36mOutlierVAE.fit\u001b[0;34m(self, X, loss_fn, optimizer, cov_elbo, epochs, batch_size, verbose, log_metric, callbacks)\u001b[0m\n\u001b[1;32m    131\u001b[0m     kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss_fn_kwargs\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m {cov_elbo_type: tf\u001b[38;5;241m.\u001b[39mdtypes\u001b[38;5;241m.\u001b[39mcast(cov, tf\u001b[38;5;241m.\u001b[39mfloat32)}\n\u001b[1;32m    133\u001b[0m \u001b[38;5;66;03m# train\u001b[39;00m\n\u001b[0;32m--> 134\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/scv2/lib/python3.9/site-packages/alibi_detect/models/tensorflow/trainer.py:93\u001b[0m, in \u001b[0;36mtrainer\u001b[0;34m(model, loss_fn, x_train, y_train, dataset, optimizer, loss_fn_kwargs, preprocess_fn, epochs, reg_loss_fn, batch_size, buffer_size, verbose, log_metric, callbacks)\u001b[0m\n\u001b[1;32m     90\u001b[0m         loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(model\u001b[38;5;241m.\u001b[39mlosses)\n\u001b[1;32m     91\u001b[0m     loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m reg_loss_fn(model)  \u001b[38;5;66;03m# alternative way they might be specified\u001b[39;00m\n\u001b[0;32m---> 93\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[43mtape\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgradient\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainable_weights\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     94\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mapply_gradients(\u001b[38;5;28mzip\u001b[39m(grads, model\u001b[38;5;241m.\u001b[39mtrainable_weights))\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verbose:\n",
+      "File \u001b[0;32m~/miniconda3/envs/scv2/lib/python3.9/site-packages/tensorflow/python/eager/backprop.py:1081\u001b[0m, in \u001b[0;36mGradientTape.gradient\u001b[0;34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[1;32m   1077\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_gradients \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1078\u001b[0m   output_gradients \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mconvert_to_tensor(x)\n\u001b[1;32m   1079\u001b[0m                       \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m nest\u001b[38;5;241m.\u001b[39mflatten(output_gradients)]\n\u001b[0;32m-> 1081\u001b[0m flat_grad \u001b[38;5;241m=\u001b[39m \u001b[43mimperative_grad\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimperative_grad\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1082\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tape\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1083\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_targets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1084\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_sources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1085\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_gradients\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_gradients\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1086\u001b[0m \u001b[43m    \u001b[49m\u001b[43msources_raw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mflat_sources_raw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1087\u001b[0m \u001b[43m    \u001b[49m\u001b[43munconnected_gradients\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munconnected_gradients\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1089\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_persistent:\n\u001b[1;32m   1090\u001b[0m   \u001b[38;5;66;03m# Keep track of watched variables before setting tape to None\u001b[39;00m\n\u001b[1;32m   1091\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_watched_variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tape\u001b[38;5;241m.\u001b[39mwatched_variables()\n",
+      "File \u001b[0;32m~/miniconda3/envs/scv2/lib/python3.9/site-packages/tensorflow/python/eager/imperative_grad.py:67\u001b[0m, in \u001b[0;36mimperative_grad\u001b[0;34m(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m     64\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     65\u001b[0m       \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown value for unconnected_gradients: \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m unconnected_gradients)\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_TapeGradient\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     68\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtape\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m     69\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     70\u001b[0m \u001b[43m    \u001b[49m\u001b[43msources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_gradients\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     72\u001b[0m \u001b[43m    \u001b[49m\u001b[43msources_raw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_str\u001b[49m\u001b[43m(\u001b[49m\u001b[43munconnected_gradients\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/scv2/lib/python3.9/site-packages/tensorflow/python/eager/backprop.py:156\u001b[0m, in \u001b[0;36m_gradient_function\u001b[0;34m(op_name, attr_tuple, num_inputs, inputs, outputs, out_grads, skip_input_indices, forward_pass_name_scope)\u001b[0m\n\u001b[1;32m    154\u001b[0m     gradient_name_scope \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m forward_pass_name_scope \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    155\u001b[0m   \u001b[38;5;28;01mwith\u001b[39;00m ops\u001b[38;5;241m.\u001b[39mname_scope(gradient_name_scope):\n\u001b[0;32m--> 156\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgrad_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmock_op\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mout_grads\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    157\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    158\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m grad_fn(mock_op, \u001b[38;5;241m*\u001b[39mout_grads)\n",
+      "File \u001b[0;32m~/miniconda3/envs/scv2/lib/python3.9/site-packages/tensorflow/python/ops/math_grad.py:1741\u001b[0m, in \u001b[0;36m_MatMulGrad\u001b[0;34m(op, grad)\u001b[0m\n\u001b[1;32m   1739\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m t_a \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m t_b:\n\u001b[1;32m   1740\u001b[0m   grad_a \u001b[38;5;241m=\u001b[39m gen_math_ops\u001b[38;5;241m.\u001b[39mmat_mul(grad, b, transpose_b\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m-> 1741\u001b[0m   grad_b \u001b[38;5;241m=\u001b[39m \u001b[43mgen_math_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmat_mul\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtranspose_a\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m t_a \u001b[38;5;129;01mand\u001b[39;00m t_b:\n\u001b[1;32m   1743\u001b[0m   grad_a \u001b[38;5;241m=\u001b[39m gen_math_ops\u001b[38;5;241m.\u001b[39mmat_mul(grad, b)\n",
+      "File \u001b[0;32m~/miniconda3/envs/scv2/lib/python3.9/site-packages/tensorflow/python/ops/gen_math_ops.py:6013\u001b[0m, in \u001b[0;36mmat_mul\u001b[0;34m(a, b, transpose_a, transpose_b, name)\u001b[0m\n\u001b[1;32m   6011\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tld\u001b[38;5;241m.\u001b[39mis_eager:\n\u001b[1;32m   6012\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 6013\u001b[0m     _result \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_FastPathExecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   6014\u001b[0m \u001b[43m      \u001b[49m\u001b[43m_ctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mMatMul\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtranspose_a\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtranspose_a\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtranspose_b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   6015\u001b[0m \u001b[43m      \u001b[49m\u001b[43mtranspose_b\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6016\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _result\n\u001b[1;32m   6017\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "latent_dim = 1024\n",
+    "\n",
+    "encoder_net = tf.keras.Sequential(\n",
+    "    [\n",
+    "        InputLayer(input_shape=(32, 32, 3)),\n",
+    "        Conv2D(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
+    "        Conv2D(128, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
+    "        Conv2D(512, 4, strides=2, padding='same', activation=tf.nn.relu)\n",
+    "    ])\n",
+    "\n",
+    "decoder_net = tf.keras.Sequential(\n",
+    "    [\n",
+    "        InputLayer(input_shape=(latent_dim,)),\n",
+    "        Dense(4*4*128),\n",
+    "        Reshape(target_shape=(4, 4, 128)),\n",
+    "        Conv2DTranspose(256, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
+    "        Conv2DTranspose(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
+    "        Conv2DTranspose(3, 4, strides=2, padding='same', activation='sigmoid')\n",
+    "    ])\n",
+    "\n",
+    "# initialize outlier detector\n",
+    "od = OutlierVAE(threshold=.015,  # threshold for outlier score\n",
+    "                score_type='mse',  # use MSE of reconstruction error for outlier detection\n",
+    "                encoder_net=encoder_net,  # can also pass VAE model instead\n",
+    "                decoder_net=decoder_net,  # of separate encoder and decoder\n",
+    "                latent_dim=latent_dim,\n",
+    "                samples=2)\n",
+    "# train\n",
+    "od.fit(X_train,\n",
+    "        loss_fn=elbo,\n",
+    "        cov_elbo=dict(sim=.05),\n",
+    "        epochs=50,\n",
+    "        verbose=True)\n",
+    "\n",
+    "# save the trained outlier detector\n",
+    "save_detector(od, \"./outlier-detector\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a38614a",
+   "metadata": {},
+   "source": [
+    "Create a MLServer model settings file: `model-settings.json`:\n",
+    "\n",
+    "```json\n",
+    "{\n",
+    "  \"name\": \"cifar10-outlier-detect\",\n",
+    "  \"implementation\": \"mlserver_alibi_detect.AlibiDetectRuntime\",\n",
+    "  \"parameters\": {\n",
+    "    \"uri\": \"./\",\n",
+    "    \"version\": \"v0.1.0\"\n",
+    "  }\n",
+    "}\n",
+    "\n",
+    "```\n",
+    "\n",
+    "Save to local or remote storage the directory. Here we saved to Google Storage:\n",
+    "\n",
+    "```bash\n",
+    "gsutil ls -R gs://seldon-models/mlserver/alibi-detect/cifar10-outlier \n",
+    "\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/:\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/OutlierVAE.dill\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/meta.dill\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/model-settings.json\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/model/:\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/model/checkpoint\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/model/decoder_net.h5\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/model/encoder_net.h5\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/model/vae.ckpt.data-00000-of-00001\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-outlier/model/vae.ckpt.index\n",
+    "\n",
+    "```\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "785949c4",
+   "metadata": {},
+   "source": [
+    "## Train Drift Detector\n",
+    "\n",
+    "Based on [Alibi Detect Example](https://docs.seldon.io/projects/alibi-detect/en/stable/examples/cd_ks_cifar10.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "81395f95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "from alibi_detect.cd import KSDrift\n",
+    "from alibi_detect.models.tensorflow import scale_by_instance\n",
+    "from alibi_detect.utils.fetching import fetch_tf_model, fetch_detector\n",
+    "from alibi_detect.saving import save_detector, load_detector\n",
+    "from alibi_detect.datasets import fetch_cifar10c, corruption_types_cifar10c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "5338d603",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
+    "X_train = X_train.astype('float32') / 255\n",
+    "X_test = X_test.astype('float32') / 255\n",
+    "y_train = y_train.astype('int64').reshape(-1,)\n",
+    "y_test = y_test.astype('int64').reshape(-1,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2acb47cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5000, 32, 32, 3) (5000, 32, 32, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "np.random.seed(0)\n",
+    "n_test = X_test.shape[0]\n",
+    "idx = np.random.choice(n_test, size=n_test // 2, replace=False)\n",
+    "idx_h0 = np.delete(np.arange(n_test), idx, axis=0)\n",
+    "X_ref,y_ref = X_test[idx], y_test[idx]\n",
+    "X_h0, y_h0 = X_test[idx_h0], y_test[idx_h0]\n",
+    "print(X_ref.shape, X_h0.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "28d5e887",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Directory drift-detector/preprocess_fn/model does not exist and is now created.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from functools import partial\n",
+    "from tensorflow.keras.layers import Conv2D, Dense, Flatten, InputLayer, Reshape\n",
+    "from alibi_detect.cd.tensorflow import preprocess_drift\n",
+    "\n",
+    "tf.random.set_seed(0)\n",
+    "\n",
+    "# define encoder\n",
+    "encoding_dim = 32\n",
+    "encoder_net = tf.keras.Sequential(\n",
+    "  [\n",
+    "      InputLayer(input_shape=(32, 32, 3)),\n",
+    "      Conv2D(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
+    "      Conv2D(128, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
+    "      Conv2D(512, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
+    "      Flatten(),\n",
+    "      Dense(encoding_dim,)\n",
+    "  ]\n",
+    ")\n",
+    "\n",
+    "# define preprocessing function\n",
+    "preprocess_fn = partial(preprocess_drift, model=encoder_net, batch_size=512)\n",
+    "\n",
+    "# initialise drift detector\n",
+    "p_val = .05\n",
+    "cd = KSDrift(X_ref, p_val=p_val, preprocess_fn=preprocess_fn)\n",
+    "\n",
+    "# we can also save/load an initialised detector\n",
+    "filepath = 'my_path'  # change to directory where detector is saved\n",
+    "save_detector(cd, \"./drift-detector\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeb4314a",
+   "metadata": {},
+   "source": [
+    "Create a MLServer model settings file: `model-settings.json`:\n",
+    "\n",
+    "```json\n",
+    "{\n",
+    "  \"name\": \"cifar10-drift\",\n",
+    "  \"implementation\": \"mlserver_alibi_detect.AlibiDetectRuntime\",\n",
+    "  \"parameters\": {\n",
+    "    \"uri\": \"./\",\n",
+    "    \"version\": \"v0.1.0\"\n",
+    "  }\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "Save to local or remote storage the directory. Here we saved to Google Storage:\n",
+    "\n",
+    "```bash\n",
+    "gsutil ls -R gs://seldon-models/mlserver/alibi-detect/cifar10-drift \n",
+    "\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-drift/:\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-drift/\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-drift/KSDrift.dill\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-drift/meta.dill\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-drift/model-settings.json\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-drift/model/:\n",
+    "gs://seldon-models/mlserver/alibi-detect/cifar10-drift/model/encoder.h5\n",
+    "\n",
+    "```\n",
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/notebooks/trigger_joins.ipynb b/docs/examples/notebooks/trigger_joins.ipynb
new file mode 100644
index 0000000000..b87957ce33
--- /dev/null
+++ b/docs/examples/notebooks/trigger_joins.ipynb
@@ -0,0 +1,527 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trigger Joins\n",
+    "\n",
+    "In this notebook we show use of trigger joins of type \"any\" where we wait for any of the inputs tobe present. We illustrate where we send various combinations of 2 inputs and show we always get an output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "def get_request_string(use_trigger_1=True, use_trigger_2=True):\n",
+    "    request = {\n",
+    "        \"model_name\": \"whatever\",\n",
+    "        \"inputs\": [\n",
+    "            {\"name\": \"INPUT1\", \"contents\": {\"int64Contents\": [1]}, \"datatype\": \"INT64\", \"shape\": [1]},\n",
+    "            {\"name\": \"INPUT2\", \"contents\": {\"int64Contents\": [1]}, \"datatype\": \"INT64\", \"shape\": [1]},\n",
+    "        ]\n",
+    "    }\n",
+    "\n",
+    "    if use_trigger_1:\n",
+    "        request[\"inputs\"].append({\"name\": \"TRIGGER1\", \"contents\": {\"boolContents\": [True]}, \"datatype\": \"BOOL\", \"shape\": [1]})\n",
+    "    \n",
+    "    if use_trigger_2:\n",
+    "        request[\"inputs\"].append({\"name\": \"TRIGGER2\", \"contents\": {\"boolContents\": [True]}, \"datatype\": \"BOOL\", \"shape\": [1]})\n",
+    "    \n",
+    "    request_string = json.dumps(request)\n",
+    "    return request_string"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load models and pipelines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model load -f ./models/id1_node.yaml\n",
+    "!seldon model load -f ./models/id2_node.yaml\n",
+    "!seldon model load -f ./models/join_node.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model status join_node -w ModelAvailable\n",
+    "!seldon model status id1_node -w ModelAvailable\n",
+    "!seldon model status id2_node -w ModelAvailable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/triggers_join_inputs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;39m{\r\n",
+      "  \u001b[0m\u001b[34;1m\"pipelineName\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"triggers_join_inputs\"\u001b[0m\u001b[1;39m,\r\n",
+      "  \u001b[0m\u001b[34;1m\"versions\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "    \u001b[1;39m{\r\n",
+      "      \u001b[0m\u001b[34;1m\"pipeline\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\r\n",
+      "        \u001b[0m\u001b[34;1m\"name\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"triggers_join_inputs\"\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"uid\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"cg5g8ak6dpcs73c4qhpg\"\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"version\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39m1\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"steps\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "          \u001b[1;39m{\r\n",
+      "            \u001b[0m\u001b[34;1m\"name\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"join_node\"\u001b[0m\u001b[1;39m,\r\n",
+      "            \u001b[0m\u001b[34;1m\"inputs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "              \u001b[0;32m\"triggers_join_inputs.inputs.INPUT1\"\u001b[0m\u001b[1;39m,\r\n",
+      "              \u001b[0;32m\"triggers_join_inputs.inputs.INPUT2\"\u001b[0m\u001b[1;39m\r\n",
+      "            \u001b[1;39m]\u001b[0m\u001b[1;39m,\r\n",
+      "            \u001b[0m\u001b[34;1m\"triggers\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "              \u001b[0;32m\"triggers_join_inputs.inputs.TRIGGER1\"\u001b[0m\u001b[1;39m,\r\n",
+      "              \u001b[0;32m\"triggers_join_inputs.inputs.TRIGGER2\"\u001b[0m\u001b[1;39m\r\n",
+      "            \u001b[1;39m]\u001b[0m\u001b[1;39m,\r\n",
+      "            \u001b[0m\u001b[34;1m\"triggersJoin\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ANY\"\u001b[0m\u001b[1;39m\r\n",
+      "          \u001b[1;39m}\u001b[0m\u001b[1;39m\r\n",
+      "        \u001b[1;39m]\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"output\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\r\n",
+      "          \u001b[0m\u001b[34;1m\"steps\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "            \u001b[0;32m\"join_node.outputs\"\u001b[0m\u001b[1;39m\r\n",
+      "          \u001b[1;39m]\u001b[0m\u001b[1;39m\r\n",
+      "        \u001b[1;39m}\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"kubernetesMeta\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m\r\n",
+      "      \u001b[1;39m}\u001b[0m\u001b[1;39m,\r\n",
+      "      \u001b[0m\u001b[34;1m\"state\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\r\n",
+      "        \u001b[0m\u001b[34;1m\"pipelineVersion\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39m1\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"status\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"PipelineReady\"\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"reason\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"created pipeline\"\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"lastChangeTimestamp\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"2023-03-10T10:19:22.997419447Z\"\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"modelsReady\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mtrue\u001b[0m\u001b[1;39m\r\n",
+      "      \u001b[1;39m}\u001b[0m\u001b[1;39m\r\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\r\n",
+      "  \u001b[1;39m]\u001b[0m\u001b[1;39m\r\n",
+      "\u001b[1;39m}\u001b[0m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status triggers_join_inputs -w PipelineReady | jq ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: triggers_join_inputs\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: join_node\r\n",
+      "      inputs:\r\n",
+      "        - triggers_join_inputs.inputs.INPUT1\r\n",
+      "        - triggers_join_inputs.inputs.INPUT2\r\n",
+      "      triggers:\r\n",
+      "        - triggers_join_inputs.inputs.TRIGGER1\r\n",
+      "        - triggers_join_inputs.inputs.TRIGGER2\r\n",
+      "      triggersJoinType: any\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "      - join_node\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/triggers_join_inputs.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"outputs\":[{\"name\":\"OUTPUT1\",\"datatype\":\"INT64\",\"shape\":[\"1\"],\"contents\":{\"int64Contents\":[\"2\"]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "request_string = get_request_string(use_trigger_1=True, use_trigger_2=True)\n",
+    "\n",
+    "!seldon pipeline infer triggers_join_inputs --inference-mode grpc '{request_string}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"outputs\":[{\"name\":\"OUTPUT1\",\"datatype\":\"INT64\",\"shape\":[\"1\"],\"contents\":{\"int64Contents\":[\"2\"]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "request_string = get_request_string(use_trigger_1=True, use_trigger_2=False)\n",
+    "\n",
+    "!seldon pipeline infer triggers_join_inputs --inference-mode grpc '{request_string}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"outputs\":[{\"name\":\"OUTPUT1\",\"datatype\":\"INT64\",\"shape\":[\"1\"],\"contents\":{\"int64Contents\":[\"2\"]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "request_string = get_request_string(use_trigger_1=False, use_trigger_2=True)\n",
+    "\n",
+    "!seldon pipeline infer triggers_join_inputs --inference-mode grpc '{request_string}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline unload triggers_join_inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline load -f ./pipelines/triggers_join_internal.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;39m{\r\n",
+      "  \u001b[0m\u001b[34;1m\"pipelineName\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"triggers_join_internal\"\u001b[0m\u001b[1;39m,\r\n",
+      "  \u001b[0m\u001b[34;1m\"versions\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "    \u001b[1;39m{\r\n",
+      "      \u001b[0m\u001b[34;1m\"pipeline\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\r\n",
+      "        \u001b[0m\u001b[34;1m\"name\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"triggers_join_internal\"\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"uid\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"cg5g8fs6dpcs73c4qhq0\"\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"version\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39m1\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"steps\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "          \u001b[1;39m{\r\n",
+      "            \u001b[0m\u001b[34;1m\"name\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"id1_node\"\u001b[0m\u001b[1;39m,\r\n",
+      "            \u001b[0m\u001b[34;1m\"inputs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "              \u001b[0;32m\"triggers_join_internal.inputs.TRIGGER1\"\u001b[0m\u001b[1;39m\r\n",
+      "            \u001b[1;39m]\u001b[0m\u001b[1;39m,\r\n",
+      "            \u001b[0m\u001b[34;1m\"tensorMap\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\r\n",
+      "              \u001b[0m\u001b[34;1m\"triggers_join_internal.inputs.TRIGGER1\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"INPUT1\"\u001b[0m\u001b[1;39m\r\n",
+      "            \u001b[1;39m}\u001b[0m\u001b[1;39m\r\n",
+      "          \u001b[1;39m}\u001b[0m\u001b[1;39m,\r\n",
+      "          \u001b[1;39m{\r\n",
+      "            \u001b[0m\u001b[34;1m\"name\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"id2_node\"\u001b[0m\u001b[1;39m,\r\n",
+      "            \u001b[0m\u001b[34;1m\"inputs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "              \u001b[0;32m\"triggers_join_internal.inputs.TRIGGER2\"\u001b[0m\u001b[1;39m\r\n",
+      "            \u001b[1;39m]\u001b[0m\u001b[1;39m,\r\n",
+      "            \u001b[0m\u001b[34;1m\"tensorMap\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\r\n",
+      "              \u001b[0m\u001b[34;1m\"triggers_join_internal.inputs.TRIGGER2\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"INPUT1\"\u001b[0m\u001b[1;39m\r\n",
+      "            \u001b[1;39m}\u001b[0m\u001b[1;39m\r\n",
+      "          \u001b[1;39m}\u001b[0m\u001b[1;39m,\r\n",
+      "          \u001b[1;39m{\r\n",
+      "            \u001b[0m\u001b[34;1m\"name\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"join_node\"\u001b[0m\u001b[1;39m,\r\n",
+      "            \u001b[0m\u001b[34;1m\"inputs\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "              \u001b[0;32m\"triggers_join_internal.inputs.INPUT1\"\u001b[0m\u001b[1;39m,\r\n",
+      "              \u001b[0;32m\"triggers_join_internal.inputs.INPUT2\"\u001b[0m\u001b[1;39m\r\n",
+      "            \u001b[1;39m]\u001b[0m\u001b[1;39m,\r\n",
+      "            \u001b[0m\u001b[34;1m\"triggers\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "              \u001b[0;32m\"id1_node.outputs.OUTPUT1\"\u001b[0m\u001b[1;39m,\r\n",
+      "              \u001b[0;32m\"id2_node.outputs.OUTPUT1\"\u001b[0m\u001b[1;39m\r\n",
+      "            \u001b[1;39m]\u001b[0m\u001b[1;39m,\r\n",
+      "            \u001b[0m\u001b[34;1m\"triggersJoin\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"ANY\"\u001b[0m\u001b[1;39m\r\n",
+      "          \u001b[1;39m}\u001b[0m\u001b[1;39m\r\n",
+      "        \u001b[1;39m]\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"output\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\r\n",
+      "          \u001b[0m\u001b[34;1m\"steps\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m[\r\n",
+      "            \u001b[0;32m\"join_node.outputs\"\u001b[0m\u001b[1;39m\r\n",
+      "          \u001b[1;39m]\u001b[0m\u001b[1;39m\r\n",
+      "        \u001b[1;39m}\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"kubernetesMeta\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{}\u001b[0m\u001b[1;39m\r\n",
+      "      \u001b[1;39m}\u001b[0m\u001b[1;39m,\r\n",
+      "      \u001b[0m\u001b[34;1m\"state\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[1;39m{\r\n",
+      "        \u001b[0m\u001b[34;1m\"pipelineVersion\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39m1\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"status\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"PipelineReady\"\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"reason\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"created pipeline\"\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"lastChangeTimestamp\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;32m\"2023-03-10T10:19:43.307156600Z\"\u001b[0m\u001b[1;39m,\r\n",
+      "        \u001b[0m\u001b[34;1m\"modelsReady\"\u001b[0m\u001b[1;39m: \u001b[0m\u001b[0;39mtrue\u001b[0m\u001b[1;39m\r\n",
+      "      \u001b[1;39m}\u001b[0m\u001b[1;39m\r\n",
+      "    \u001b[1;39m}\u001b[0m\u001b[1;39m\r\n",
+      "  \u001b[1;39m]\u001b[0m\u001b[1;39m\r\n",
+      "\u001b[1;39m}\u001b[0m\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline status triggers_join_internal -w PipelineReady | jq ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apiVersion: mlops.seldon.io/v1alpha1\r\n",
+      "kind: Pipeline\r\n",
+      "metadata:\r\n",
+      "  name: triggers_join_internal\r\n",
+      "spec:\r\n",
+      "  steps:\r\n",
+      "    - name: id1_node\r\n",
+      "      inputs:\r\n",
+      "        - triggers_join_internal.inputs.TRIGGER1\r\n",
+      "      tensorMap:\r\n",
+      "        triggers_join_internal.inputs.TRIGGER1: INPUT1\r\n",
+      "    - name: id2_node\r\n",
+      "      inputs:\r\n",
+      "        - triggers_join_internal.inputs.TRIGGER2\r\n",
+      "      tensorMap:\r\n",
+      "        triggers_join_internal.inputs.TRIGGER2: INPUT1\r\n",
+      "    - name: join_node\r\n",
+      "      inputs:\r\n",
+      "        - triggers_join_internal.inputs.INPUT1\r\n",
+      "        - triggers_join_internal.inputs.INPUT2\r\n",
+      "      triggers:\r\n",
+      "        - id1_node.outputs.OUTPUT1\r\n",
+      "        - id2_node.outputs.OUTPUT1\r\n",
+      "      triggersJoinType: any\r\n",
+      "  output:\r\n",
+      "    steps:\r\n",
+      "      - join_node\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./pipelines/triggers_join_internal.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"outputs\":[{\"name\":\"OUTPUT1\",\"datatype\":\"INT64\",\"shape\":[\"1\"],\"contents\":{\"int64Contents\":[\"2\"]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "request_string = get_request_string(use_trigger_1=True, use_trigger_2=True)\n",
+    "\n",
+    "!seldon pipeline infer triggers_join_internal --inference-mode grpc '{request_string}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"outputs\":[{\"name\":\"OUTPUT1\",\"datatype\":\"INT64\",\"shape\":[\"1\"],\"contents\":{\"int64Contents\":[\"2\"]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "request_string = get_request_string(use_trigger_1=True, use_trigger_2=False)\n",
+    "\n",
+    "!seldon pipeline infer triggers_join_internal --inference-mode grpc '{request_string}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\"outputs\":[{\"name\":\"OUTPUT1\",\"datatype\":\"INT64\",\"shape\":[\"1\"],\"contents\":{\"int64Contents\":[\"2\"]}}]}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "request_string = get_request_string(use_trigger_1=False, use_trigger_2=True)\n",
+    "\n",
+    "!seldon pipeline infer triggers_join_internal --inference-mode grpc '{request_string}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon pipeline unload triggers_join_internal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{}\n",
+      "{}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "!seldon model unload id1_node\n",
+    "!seldon model unload id2_node\n",
+    "!seldon model unload join_node"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "d5dfeee30c3d1c2ae1f5e8224888e63391d275d607ef34fbb59d48e23cc490af"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/pandasquery.md b/docs/examples/pandasquery.md
index a42ebee565..90f656020d 100644
--- a/docs/examples/pandasquery.md
+++ b/docs/examples/pandasquery.md
@@ -1,11 +1,298 @@
 # Conditional Pipeline with Pandas Query Model
 
-The model is defined as an MLServer custom runtime and allows the user to pass in a custom pandas query as a parameter defined at model creation to be used to filter the data passed to the model.
+The model is defined as an MLServer custom runtime and allows the user to pass in a custom
+pandas query as a parameter defined at model creation to be used to filter the data passed to the model.
 
-```{literalinclude} ../../../../samples/examples/pandasquery/pandasquery/model.py
-:language: python
+```python
+from mlserver import MLModel
+from mlserver.types import InferenceRequest, InferenceResponse
+from mlserver.codecs import PandasCodec
+from mlserver.errors import MLServerError
+import pandas as pd
+from fastapi import status
+from mlserver.logging import logger
+
+QUERY_KEY = "query"
+
+
+class ModelParametersMissing(MLServerError):
+  def __init__(self, model_name: str, reason: str):
+    super().__init__(
+      f"Parameters missing for model {model_name} {reason}", status.HTTP_400_BAD_REQUEST
+    )
+
+class PandasQueryRuntime(MLModel):
+
+  async def load(self) -> bool:
+    logger.info("Loading with settings %s", self.settings)
+    if self.settings.parameters is None or \
+      self.settings.parameters.extra is None:
+      raise ModelParametersMissing(self.name, "no settings.parameters.extra found")
+    self.query = self.settings.parameters.extra[QUERY_KEY]
+    if self.query is None:
+      raise ModelParametersMissing(self.name, "no settings.parameters.extra.query found")
+    self.ready = True
+
+    return self.ready
+
+  async def predict(self, payload: InferenceRequest) -> InferenceResponse:
+    input_df: pd.DataFrame = PandasCodec.decode_request(payload)
+    # run query on input_df and save in output_df
+    output_df = input_df.query(self.query)
+    if output_df.empty:
+      output_df = pd.DataFrame({'status':["no rows satisfied " + self.query]})
+    else:
+      output_df["status"] = "row satisfied " + self.query
+    return PandasCodec.encode_response(self.name, output_df, self.version)
+```
+
+## Conditional Pipeline using PandasQuery
+
+```bash
+cat ../../models/choice1.yaml
+echo "---"
+cat ../../models/choice2.yaml
+echo "---"
+cat ../../models/add10.yaml
+echo "---"
+cat ../../models/mul10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: choice-is-one
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/pandasquery"
+  requirements:
+  - mlserver
+  - python
+  parameters:
+  - name: query
+    value: "choice == 1"
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: choice-is-two
+spec:
+  storageUri: "gs://seldon-models/scv2/examples/pandasquery"
+  requirements:
+  - mlserver
+  - python
+  parameters:
+  - name: query
+    value: "choice == 2"
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: add10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/add10"
+  requirements:
+  - triton
+  - python
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mul10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mul10"
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+seldon model load -f ../../models/choice1.yaml
+seldon model load -f ../../models/choice2.yaml
+seldon model load -f ../../models/add10.yaml
+seldon model load -f ../../models/mul10.yaml
+```
+
+```json
+{}
+{}
+{}
+{}
+
+```
+
+```bash
+seldon model status choice-is-one -w ModelAvailable
+seldon model status choice-is-two -w ModelAvailable
+seldon model status add10 -w ModelAvailable
+seldon model status mul10 -w ModelAvailable
+```
+
+```json
+{}
+{}
+{}
+{}
+
+```
+
+```bash
+cat ../../pipelines/choice.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: choice
+spec:
+  steps:
+  - name: choice-is-one
+  - name: mul10
+    inputs:
+    - choice.inputs.INPUT
+    triggers:
+    - choice-is-one.outputs.choice
+  - name: choice-is-two
+  - name: add10
+    inputs:
+    - choice.inputs.INPUT
+    triggers:
+    - choice-is-two.outputs.choice
+  output:
+    steps:
+    - mul10
+    - add10
+    stepsJoin: any
+
+```
+
+```bash
+seldon pipeline load -f ../../pipelines/choice.yaml
+```
+
+```bash
+seldon pipeline status choice -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "choice",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "choice",
+        "uid": "cifel9aufmbc73e5intg",
+        "version": 1,
+        "steps": [
+          {
+            "name": "add10",
+            "inputs": [
+              "choice.inputs.INPUT"
+            ],
+            "triggers": [
+              "choice-is-two.outputs.choice"
+            ]
+          },
+          {
+            "name": "choice-is-one"
+          },
+          {
+            "name": "choice-is-two"
+          },
+          {
+            "name": "mul10",
+            "inputs": [
+              "choice.inputs.INPUT"
+            ],
+            "triggers": [
+              "choice-is-one.outputs.choice"
+            ]
+          }
+        ],
+        "output": {
+          "steps": [
+            "mul10.outputs",
+            "add10.outputs"
+          ],
+          "stepsJoin": "ANY"
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-30T14:45:57.284684328Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer choice --inference-mode grpc \
+ '{"model_name":"choice","inputs":[{"name":"choice","contents":{"int_contents":[1]},"datatype":"INT32","shape":[1]},{"name":"INPUT","contents":{"fp32_contents":[5,6,7,8]},"datatype":"FP32","shape":[4]}]}' | jq -M .
 ```
 
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          50,
+          60,
+          70,
+          80
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer choice --inference-mode grpc \
+ '{"model_name":"choice","inputs":[{"name":"choice","contents":{"int_contents":[2]},"datatype":"INT32","shape":[1]},{"name":"INPUT","contents":{"fp32_contents":[5,6,7,8]},"datatype":"FP32","shape":[4]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          15,
+          16,
+          17,
+          18
+        ]
+      }
+    }
+  ]
+}
+
+```
 
-```{include} ../../../../samples/examples/pandasquery/infer.md
+```bash
+seldon model unload choice-is-one
+seldon model unload choice-is-two
+seldon model unload add10
+seldon model unload mul10
+seldon pipeline unload choice
 ```
diff --git a/docs/examples/pipeline-examples.md b/docs/examples/pipeline-examples.md
index 92e55847e0..aabebe4637 100644
--- a/docs/examples/pipeline-examples.md
+++ b/docs/examples/pipeline-examples.md
@@ -1,7 +1,1528 @@
-# Pipeline Examples
+# Seldon V2 Pipeline Examples
 
-Run these examples from the `samples` folder.
+This notebook illustrates a series of Pipelines showing of different ways of combining flows of
+data and conditional logic. We assume you have Seldon Core V2 running locally.
 
+### Models Used
 
-```{include} ../../../../samples/pipeline-examples.md
+* `gs://seldon-models/triton/simple` an example Triton tensorflow model that takes 2 inputs INPUT0
+and INPUT1 and adds them to produce OUTPUT0 and also subtracts INPUT1 from INPUT0 to produce OUTPUT1.
+See [here](https://github.com/triton-inference-server/server/tree/main/docs/examples/model_repository/simple)
+for the original source code and license.
+* Other models can be found at https://github.com/SeldonIO/triton-python-examples
+
+### Model Chaining
+
+Chain the output of one model into the next. Also shows chaning the tensor names via `tensorMap` to
+conform to the expected input tensor names of the second model.
+
+```bash
+cat ./models/tfsimple1.yaml
+cat ./models/tfsimple2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model load -f ./models/tfsimple2.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+seldon model status tfsimple2 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+The pipeline below chains the output of `tfsimple1` into `tfsimple2`. As these models have compatible
+shape and data type this can be done. However, the output tensor names from `tfsimple1` need to be
+renamed to match the input tensor names for `tfsimple2`. We do this with the `tensorMap` feature.
+
+The output of the Pipeline is the output from `tfsimple2`.
+
+```bash
+cat ./pipelines/tfsimples.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimples
+spec:
+  steps:
+    - name: tfsimple1
+    - name: tfsimple2
+      inputs:
+      - tfsimple1
+      tensorMap:
+        tfsimple1.outputs.OUTPUT0: INPUT0
+        tfsimple1.outputs.OUTPUT1: INPUT1
+  output:
+    steps:
+    - tfsimple2
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimples.yaml
+```
+
+```bash
+seldon pipeline status tfsimples -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "tfsimples",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "tfsimples",
+        "uid": "ciep26qi8ufs73flaiqg",
+        "version": 2,
+        "steps": [
+          {
+            "name": "tfsimple1"
+          },
+          {
+            "name": "tfsimple2",
+            "inputs": [
+              "tfsimple1.outputs"
+            ],
+            "tensorMap": {
+              "tfsimple1.outputs.OUTPUT0": "INPUT0",
+              "tfsimple1.outputs.OUTPUT1": "INPUT1"
+            }
+          }
+        ],
+        "output": {
+          "steps": [
+            "tfsimple2.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 2,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-29T14:11:40.101677847Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimples \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "",
+  "outputs": [
+    {
+      "data": [
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+        14,
+        16,
+        18,
+        20,
+        22,
+        24,
+        26,
+        28,
+        30,
+        32
+      ],
+      "name": "OUTPUT0",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    },
+    {
+      "data": [
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+        14,
+        16,
+        18,
+        20,
+        22,
+        24,
+        26,
+        28,
+        30,
+        32
+      ],
+      "name": "OUTPUT1",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimples --inference-mode grpc \
+    '{"model_name":"simple","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+We use the Seldon CLI `pipeline inspect` feature to look at the data for all steps of the pipeline for the last data item passed through the pipeline (the default). This can be useful for debugging.
+
+```bash
+seldon pipeline inspect tfsimples
+```
+
+```
+seldon.default.model.tfsimple1.inputs	ciep298fh5ss73dpdir0	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}
+seldon.default.model.tfsimple1.outputs	ciep298fh5ss73dpdir0	{"modelName":"tfsimple1_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}]}
+seldon.default.model.tfsimple2.inputs	ciep298fh5ss73dpdir0	{"inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}}], "rawInputContents":["AgAAAAQAAAAGAAAACAAAAAoAAAAMAAAADgAAABAAAAASAAAAFAAAABYAAAAYAAAAGgAAABwAAAAeAAAAIAAAAA==", "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=="]}
+seldon.default.model.tfsimple2.outputs	ciep298fh5ss73dpdir0	{"modelName":"tfsimple2_1", "modelVersion":"1", "outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+seldon.default.pipeline.tfsimples.inputs	ciep298fh5ss73dpdir0	{"modelName":"tfsimples", "inputs":[{"name":"INPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}, {"name":"INPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]}}]}
+seldon.default.pipeline.tfsimples.outputs	ciep298fh5ss73dpdir0	{"outputs":[{"name":"OUTPUT0", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}, {"name":"OUTPUT1", "datatype":"INT32", "shape":["1", "16"], "contents":{"intContents":[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]}}]}
+
+```
+
+Next, we look get the output as json and use the `jq` tool to get just one value.
+
+```bash
+seldon pipeline inspect tfsimples --format json | jq -M .topics[0].msgs[0].value
+```
+
+```json
+{
+  "inputs": [
+    {
+      "name": "INPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16
+        ]
+      }
+    },
+    {
+      "name": "INPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline unload tfsimples
+```
+
+```bash
+seldon model unload tfsimple1
+seldon model unload tfsimple2
+```
+
+### Model Chaining from inputs
+
+Chain the output of one model into the next. Shows using the input and outputs and combining.
+
+```bash
+cat ./models/tfsimple1.yaml
+cat ./models/tfsimple2.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model load -f ./models/tfsimple2.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+seldon model status tfsimple2 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+cat ./pipelines/tfsimples-input.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimples-input
+spec:
+  steps:
+    - name: tfsimple1
+    - name: tfsimple2
+      inputs:
+      - tfsimple1.inputs.INPUT0
+      - tfsimple1.outputs.OUTPUT1
+      tensorMap:
+        tfsimple1.outputs.OUTPUT1: INPUT1
+  output:
+    steps:
+    - tfsimple2
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimples-input.yaml
+```
+
+```bash
+seldon pipeline status tfsimples-input -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "tfsimples-input",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "tfsimples-input",
+        "uid": "ciep2fii8ufs73flair0",
+        "version": 1,
+        "steps": [
+          {
+            "name": "tfsimple1"
+          },
+          {
+            "name": "tfsimple2",
+            "inputs": [
+              "tfsimple1.inputs.INPUT0",
+              "tfsimple1.outputs.OUTPUT1"
+            ],
+            "tensorMap": {
+              "tfsimple1.outputs.OUTPUT1": "INPUT1"
+            }
+          }
+        ],
+        "output": {
+          "steps": [
+            "tfsimple2.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-29T14:12:14.711416101Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimples-input \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "model_name": "",
+  "outputs": [
+    {
+      "data": [
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9,
+        10,
+        11,
+        12,
+        13,
+        14,
+        15,
+        16
+      ],
+      "name": "OUTPUT0",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    },
+    {
+      "data": [
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9,
+        10,
+        11,
+        12,
+        13,
+        14,
+        15,
+        16
+      ],
+      "name": "OUTPUT1",
+      "shape": [
+        1,
+        16
+      ],
+      "datatype": "INT32"
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer tfsimples-input --inference-mode grpc \
+    '{"model_name":"simple","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16
+        ]
+      }
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline unload tfsimples-input
+```
+
+```bash
+seldon model unload tfsimple1
+seldon model unload tfsimple2
+```
+
+### Model Join
+
+Join two flows of data from two models as input to a third model. This shows how individual flows of data can be combined.
+
+```bash
+cat ./models/tfsimple1.yaml
+echo "---"
+cat ./models/tfsimple2.yaml
+echo "---"
+cat ./models/tfsimple3.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple1
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple2
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple3
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+
+```
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model load -f ./models/tfsimple2.yaml
+seldon model load -f ./models/tfsimple3.yaml
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+```bash
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+seldon model status tfsimple2 -w ModelAvailable | jq -M .
+seldon model status tfsimple3 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+In the pipeline below for the input to `tfsimple3` we join 1 output tensor each from the two previous models `tfsimple1` and `tfsimple2`. We need to use the `tensorMap` feature to rename each output tensor to one of the expected input tensors for the `tfsimple3` model.
+
+```bash
+cat ./pipelines/tfsimples-join.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: join
+spec:
+  steps:
+    - name: tfsimple1
+    - name: tfsimple2
+    - name: tfsimple3
+      inputs:
+      - tfsimple1.outputs.OUTPUT0
+      - tfsimple2.outputs.OUTPUT1
+      tensorMap:
+        tfsimple1.outputs.OUTPUT0: INPUT0
+        tfsimple2.outputs.OUTPUT1: INPUT1
+  output:
+    steps:
+    - tfsimple3
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimples-join.yaml
+```
+
+```bash
+seldon pipeline status join -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "join",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "join",
+        "uid": "ciep2k2i8ufs73flairg",
+        "version": 1,
+        "steps": [
+          {
+            "name": "tfsimple1"
+          },
+          {
+            "name": "tfsimple2"
+          },
+          {
+            "name": "tfsimple3",
+            "inputs": [
+              "tfsimple1.outputs.OUTPUT0",
+              "tfsimple2.outputs.OUTPUT1"
+            ],
+            "tensorMap": {
+              "tfsimple1.outputs.OUTPUT0": "INPUT0",
+              "tfsimple2.outputs.OUTPUT1": "INPUT1"
+            }
+          }
+        ],
+        "output": {
+          "steps": [
+            "tfsimple3.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-29T14:12:32.938603415Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+The outputs are the sequence "2,4,6..." which conforms to the logic of this model (addition and subtraction) when fed the output of the first two models.
+
+```bash
+seldon pipeline infer join --inference-mode grpc \
+    '{"model_name":"simple","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT0",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    },
+    {
+      "name": "OUTPUT1",
+      "datatype": "INT32",
+      "shape": [
+        "1",
+        "16"
+      ],
+      "contents": {
+        "intContents": [
+          2,
+          4,
+          6,
+          8,
+          10,
+          12,
+          14,
+          16,
+          18,
+          20,
+          22,
+          24,
+          26,
+          28,
+          30,
+          32
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline unload join
+```
+
+```bash
+seldon model unload tfsimple1
+seldon model unload tfsimple2
+seldon model unload tfsimple3
+```
+
+### Conditional
+
+Shows conditional data flows - one of two models is run based on output tensors from first.
+
+```bash
+cat ./models/conditional.yaml
+echo "---"
+cat ./models/add10.yaml
+echo "---"
+cat ./models/mul10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: conditional
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/conditional"
+  requirements:
+  - triton
+  - python
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: add10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/add10"
+  requirements:
+  - triton
+  - python
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mul10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mul10"
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+seldon model load -f ./models/conditional.yaml
+seldon model load -f ./models/add10.yaml
+seldon model load -f ./models/mul10.yaml
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+```bash
+seldon model status conditional -w ModelAvailable | jq -M .
+seldon model status add10 -w ModelAvailable | jq -M .
+seldon model status mul10 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+{}
+
+```
+
+Here we assume the `conditional` model can output two tensors OUTPUT0 and OUTPUT1 but only outputs the former if the CHOICE input tensor is set to 0 otherwise it outputs tensor OUTPUT1. By this means only one of the two downstream models will receive data and run. The `output` steps does an `any` join from both models and whichever data appears first will be sent as output to pipeline. As in this case only 1 of the two models `add10` and `mul10` runs we will receive their output.
+
+```bash
+cat ./pipelines/conditional.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimple-conditional
+spec:
+  steps:
+  - name: conditional
+  - name: mul10
+    inputs:
+    - conditional.outputs.OUTPUT0
+    tensorMap:
+      conditional.outputs.OUTPUT0: INPUT
+  - name: add10
+    inputs:
+    - conditional.outputs.OUTPUT1
+    tensorMap:
+      conditional.outputs.OUTPUT1: INPUT
+  output:
+    steps:
+    - mul10
+    - add10
+    stepsJoin: any
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/conditional.yaml
+```
+
+```bash
+seldon pipeline status tfsimple-conditional -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "tfsimple-conditional",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "tfsimple-conditional",
+        "uid": "ciepga2i8ufs73flais0",
+        "version": 1,
+        "steps": [
+          {
+            "name": "add10",
+            "inputs": [
+              "conditional.outputs.OUTPUT1"
+            ],
+            "tensorMap": {
+              "conditional.outputs.OUTPUT1": "INPUT"
+            }
+          },
+          {
+            "name": "conditional"
+          },
+          {
+            "name": "mul10",
+            "inputs": [
+              "conditional.outputs.OUTPUT0"
+            ],
+            "tensorMap": {
+              "conditional.outputs.OUTPUT0": "INPUT"
+            }
+          }
+        ],
+        "output": {
+          "steps": [
+            "mul10.outputs",
+            "add10.outputs"
+          ],
+          "stepsJoin": "ANY"
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-29T14:41:45.133142725Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+The `mul10` model will run as the CHOICE tensor is set to 0.
+
+```bash
+seldon pipeline infer tfsimple-conditional --inference-mode grpc \
+ '{"model_name":"conditional","inputs":[{"name":"CHOICE","contents":{"int_contents":[0]},"datatype":"INT32","shape":[1]},{"name":"INPUT0","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]},{"name":"INPUT1","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          10,
+          20,
+          30,
+          40
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+The `add10` model will run as the CHOICE tensor is not set to zero.
+
+```bash
+seldon pipeline infer tfsimple-conditional --inference-mode grpc \
+ '{"model_name":"conditional","inputs":[{"name":"CHOICE","contents":{"int_contents":[1]},"datatype":"INT32","shape":[1]},{"name":"INPUT0","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]},{"name":"INPUT1","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          11,
+          12,
+          13,
+          14
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline unload tfsimple-conditional
+```
+
+```bash
+seldon model unload conditional
+seldon model unload add10
+seldon model unload mul10
+```
+
+### Pipeline Input Tensors
+Access to indivudal tensors in pipeline inputs
+
+```bash
+cat ./models/mul10.yaml
+echo "---"
+cat ./models/add10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mul10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mul10"
+  requirements:
+  - triton
+  - python
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: add10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/add10"
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+seldon model load -f ./models/mul10.yaml
+seldon model load -f ./models/add10.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status mul10 -w ModelAvailable | jq -M .
+seldon model status add10 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+This pipeline shows how we can access pipeline inputs INPUT0 and INPUT1 from different steps.
+
+```bash
+cat ./pipelines/pipeline-inputs.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: pipeline-inputs
+spec:
+  steps:
+  - name: mul10
+    inputs:
+    - pipeline-inputs.inputs.INPUT0
+    tensorMap:
+      pipeline-inputs.inputs.INPUT0: INPUT
+  - name: add10
+    inputs:
+    - pipeline-inputs.inputs.INPUT1
+    tensorMap:
+      pipeline-inputs.inputs.INPUT1: INPUT
+  output:
+    steps:
+    - mul10
+    - add10
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/pipeline-inputs.yaml
+```
+
+```bash
+seldon pipeline status pipeline-inputs -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "pipeline-inputs",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "pipeline-inputs",
+        "uid": "ciepgeqi8ufs73flaisg",
+        "version": 1,
+        "steps": [
+          {
+            "name": "add10",
+            "inputs": [
+              "pipeline-inputs.inputs.INPUT1"
+            ],
+            "tensorMap": {
+              "pipeline-inputs.inputs.INPUT1": "INPUT"
+            }
+          },
+          {
+            "name": "mul10",
+            "inputs": [
+              "pipeline-inputs.inputs.INPUT0"
+            ],
+            "tensorMap": {
+              "pipeline-inputs.inputs.INPUT0": "INPUT"
+            }
+          }
+        ],
+        "output": {
+          "steps": [
+            "mul10.outputs",
+            "add10.outputs"
+          ]
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-29T14:42:04.202598715Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer pipeline-inputs --inference-mode grpc \
+    '{"model_name":"pipeline","inputs":[{"name":"INPUT0","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]},{"name":"INPUT1","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          10,
+          20,
+          30,
+          40
+        ]
+      }
+    },
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          11,
+          12,
+          13,
+          14
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline unload pipeline-inputs
+```
+
+```bash
+seldon model unload mul10
+seldon model unload add10
+```
+
+### Trigger Joins
+
+Shows how joins can be used for triggers as well.
+
+```bash
+cat ./models/mul10.yaml
+cat ./models/add10.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mul10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/mul10"
+  requirements:
+  - triton
+  - python
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: add10
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/triton_23-03/add10"
+  requirements:
+  - triton
+  - python
+
+```
+
+```bash
+seldon model load -f ./models/mul10.yaml
+seldon model load -f ./models/add10.yaml
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+seldon model status mul10 -w ModelAvailable | jq -M .
+seldon model status add10 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+Here we required tensors names `ok1` or `ok2` to exist on pipeline inputs to run the `mul10` model but require tensor `ok3` to exist on pipeline inputs to run the `add10` model. The logic on `mul10` is handled by a trigger join of `any` meaning either of these input data can exist to satisfy the trigger join.
+
+```bash
+cat ./pipelines/trigger-joins.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: trigger-joins
+spec:
+  steps:
+  - name: mul10
+    inputs:
+    - trigger-joins.inputs.INPUT
+    triggers:
+    - trigger-joins.inputs.ok1
+    - trigger-joins.inputs.ok2
+    triggersJoinType: any
+  - name: add10
+    inputs:
+    - trigger-joins.inputs.INPUT
+    triggers:
+    - trigger-joins.inputs.ok3
+  output:
+    steps:
+    - mul10
+    - add10
+    stepsJoin: any
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/trigger-joins.yaml
+```
+
+```bash
+seldon pipeline status trigger-joins -w PipelineReady | jq -M .
+```
+
+```json
+{
+  "pipelineName": "trigger-joins",
+  "versions": [
+    {
+      "pipeline": {
+        "name": "trigger-joins",
+        "uid": "ciepgkqi8ufs73flait0",
+        "version": 1,
+        "steps": [
+          {
+            "name": "add10",
+            "inputs": [
+              "trigger-joins.inputs.INPUT"
+            ],
+            "triggers": [
+              "trigger-joins.inputs.ok3"
+            ]
+          },
+          {
+            "name": "mul10",
+            "inputs": [
+              "trigger-joins.inputs.INPUT"
+            ],
+            "triggers": [
+              "trigger-joins.inputs.ok1",
+              "trigger-joins.inputs.ok2"
+            ],
+            "triggersJoin": "ANY"
+          }
+        ],
+        "output": {
+          "steps": [
+            "mul10.outputs",
+            "add10.outputs"
+          ],
+          "stepsJoin": "ANY"
+        },
+        "kubernetesMeta": {}
+      },
+      "state": {
+        "pipelineVersion": 1,
+        "status": "PipelineReady",
+        "reason": "created pipeline",
+        "lastChangeTimestamp": "2023-06-29T14:42:27.595300698Z",
+        "modelsReady": true
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer trigger-joins --inference-mode grpc \
+    '{"model_name":"pipeline","inputs":[{"name":"ok1","contents":{"fp32_contents":[1]},"datatype":"FP32","shape":[1]},{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          10,
+          20,
+          30,
+          40
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline infer trigger-joins --inference-mode grpc \
+    '{"model_name":"pipeline","inputs":[{"name":"ok3","contents":{"fp32_contents":[1]},"datatype":"FP32","shape":[1]},{"name":"INPUT","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[4]}]}' | jq -M .
+```
+
+```json
+{
+  "outputs": [
+    {
+      "name": "OUTPUT",
+      "datatype": "FP32",
+      "shape": [
+        "4"
+      ],
+      "contents": {
+        "fp32Contents": [
+          11,
+          12,
+          13,
+          14
+        ]
+      }
+    }
+  ]
+}
+
+```
+
+```bash
+seldon pipeline unload trigger-joins
+```
+
+```bash
+seldon model unload mul10
+seldon model unload add10
 ```
diff --git a/docs/examples/pipeline-ready-and-metadata.md b/docs/examples/pipeline-ready-and-metadata.md
index cca0dd145b..f39f6813ee 100644
--- a/docs/examples/pipeline-ready-and-metadata.md
+++ b/docs/examples/pipeline-ready-and-metadata.md
@@ -1,8 +1,387 @@
-# Pipeline Readiness
+# Pipeline Readiness Check and Metdata Calls
 
-Run these examples from the `samples` folder.
+Local example settings.
 
+```python
+%env INFER_REST_ENDPOINT=http://0.0.0.0:9000
+%env INFER_GRPC_ENDPOINT=0.0.0.0:9000
+%env SELDON_SCHEDULE_HOST=0.0.0.0:9004
+```
+
+```
+env: INFER_REST_ENDPOINT=http://0.0.0.0:9000
+env: INFER_GRPC_ENDPOINT=0.0.0.0:9000
+env: SELDON_SCHEDULE_HOST=0.0.0.0:9004
+
+```
+
+Remote k8s cluster example settings - change as neeed for your needs.
+
+```python
+#%env INFER_REST_ENDPOINT=http://172.19.255.1:80
+#%env INFER_GRPC_ENDPOINT=172.19.255.1:80
+#%env SELDON_SCHEDULE_HOST=172.19.255.2:9004
+```
+
+### Model Chain - Ready Check
+
+We will check the readiness of the Pipeline after every change to model and pipeline.
+
+```bash
+cat ./pipelines/tfsimples.yaml
+```
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Pipeline
+metadata:
+  name: tfsimples
+spec:
+  steps:
+    - name: tfsimple1
+    - name: tfsimple2
+      inputs:
+      - tfsimple1
+      tensorMap:
+        tfsimple1.outputs.OUTPUT0: INPUT0
+        tfsimple1.outputs.OUTPUT1: INPUT1
+  output:
+    steps:
+    - tfsimple2
+
+```
+
+```bash
+curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready
+```
+
+```
+
+```
+
+```bash
+grpcurl -d '{"name":"tfsimples"}' \
+    -plaintext \
+    -import-path ../apis \
+    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    -rpc-header seldon-model:tfsimples.pipeline \
+    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady
+```
+
+```yaml
+ERROR:
+  Code: Unimplemented
+  Message:
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimples.yaml
+seldon pipeline status tfsimples -w PipelineReady
+```
+
+```json
+{"pipelineName":"tfsimples", "versions":[{"pipeline":{"name":"tfsimples", "uid":"ciepit2i8ufs73flaitg", "version":1, "steps":[{"name":"tfsimple1"}, {"name":"tfsimple2", "inputs":["tfsimple1.outputs"], "tensorMap":{"tfsimple1.outputs.OUTPUT0":"INPUT0", "tfsimple1.outputs.OUTPUT1":"INPUT1"}}], "output":{"steps":["tfsimple2.outputs"]}, "kubernetesMeta":{}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T14:47:16.365934922Z"}}]}
+
+```
+
+```bash
+seldon pipeline status tfsimples | jq .versions[0].state.modelsReady
+```
+
+```
+null
+
+```
+
+```bash
+curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready
+```
+
+```
+
+```
+
+```bash
+grpcurl -d '{"name":"tfsimples"}' \
+    -plaintext \
+    -import-path ../apis \
+    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    -rpc-header seldon-model:tfsimples.pipeline \
+    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady
+```
+
+```json
+{
+
+}
+
+```
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model status tfsimple1 -w ModelAvailable
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready
+```
+
+```
+
+```
+
+```bash
+grpcurl -d '{"name":"tfsimples"}' \
+    -plaintext \
+    -import-path ../apis \
+    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    -rpc-header seldon-model:tfsimples.pipeline \
+    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady
+```
+
+```json
+{
+
+}
+
+```
+
+```bash
+seldon model load -f ./models/tfsimple2.yaml
+seldon model status tfsimple2 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+
+```
+
+```bash
+curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready
+```
+
+```
+
+```
+
+```bash
+grpcurl -d '{"name":"tfsimples"}' \
+    -plaintext \
+    -import-path ../apis \
+    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    -rpc-header seldon-model:tfsimples.pipeline \
+    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady
+```
+
+```json
+{
+  "ready": true
+}
+
+```
+
+```bash
+seldon pipeline status tfsimples | jq .versions[0].state.modelsReady
+```
+
+```
+true
+
+```
+
+```bash
+seldon pipeline unload tfsimples
+```
+
+```bash
+curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready
+```
+
+```
+
+```
+
+```bash
+grpcurl -d '{"name":"tfsimples"}' \
+    -plaintext \
+    -import-path ../apis \
+    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    -rpc-header seldon-model:tfsimples.pipeline \
+    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady
+```
+
+```yaml
+ERROR:
+  Code: Unimplemented
+  Message:
+
+```
+
+Models will still be ready even though Pipeline terminated
+
+```bash
+seldon pipeline status tfsimples | jq .versions[0].state.modelsReady
+```
+
+```
+true
+
+```
+
+```bash
+seldon pipeline load -f ./pipelines/tfsimples.yaml
+seldon pipeline status tfsimples -w PipelineReady
+```
+
+```json
+{"pipelineName":"tfsimples", "versions":[{"pipeline":{"name":"tfsimples", "uid":"ciepj5qi8ufs73flaiu0", "version":1, "steps":[{"name":"tfsimple1"}, {"name":"tfsimple2", "inputs":["tfsimple1.outputs"], "tensorMap":{"tfsimple1.outputs.OUTPUT0":"INPUT0", "tfsimple1.outputs.OUTPUT1":"INPUT1"}}], "output":{"steps":["tfsimple2.outputs"]}, "kubernetesMeta":{}}, "state":{"pipelineVersion":1, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2023-06-29T14:47:51.626155116Z", "modelsReady":true}}]}
+
+```
+
+```bash
+curl -Ik ${INFER_REST_ENDPOINT}/v2/pipelines/tfsimples/ready
+```
+
+```
 
-```{include} ../../../../samples/pipeline-ready-and-metadata.md
 ```
 
+```bash
+grpcurl -d '{"name":"tfsimples"}' \
+    -plaintext \
+    -import-path ../apis \
+    -proto ../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    -rpc-header seldon-model:tfsimples.pipeline \
+    ${INFER_GRPC_ENDPOINT} inference.GRPCInferenceService/ModelReady
+```
+
+```json
+{
+  "ready": true
+}
+
+```
+
+```bash
+seldon pipeline status tfsimples | jq .versions[0].state.modelsReady
+```
+
+```
+true
+
+```
+
+```bash
+seldon model unload tfsimple1
+seldon model unload tfsimple2
+```
+
+```bash
+seldon pipeline status tfsimples | jq .versions[0].state.modelsReady
+```
+
+```
+null
+
+```
+
+```bash
+seldon pipeline unload tfsimples
+```
+
+### Kubernetes Resource Example
+
+```python
+import os
+os.environ["NAMESPACE"] = "seldon-mesh"
+```
+
+```python
+MESH_IP=!kubectl get svc seldon-mesh -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+MESH_IP=MESH_IP[0]
+import os
+os.environ['MESH_IP'] = MESH_IP
+MESH_IP
+```
+
+```
+'172.19.255.1'
+
+```
+
+```bash
+kubectl create -f ./pipelines/tfsimples.yaml -n ${NAMESPACE}
+```
+
+```
+pipeline.mlops.seldon.io/tfsimples created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=1s pipeline --all -n ${NAMESPACE}
+```
+
+```
+error: timed out waiting for the condition on pipelines/tfsimples
+
+```
+
+```bash
+kubectl get pipeline tfsimples -o jsonpath='{.status.conditions[0]}' -n ${NAMESPACE}
+```
+
+```json
+{"lastTransitionTime":"2022-11-14T10:25:31Z","status":"False","type":"ModelsReady"}
+
+```
+
+```bash
+kubectl create -f ./models/tfsimple1.yaml -n ${NAMESPACE}
+kubectl create -f ./models/tfsimple2.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io/tfsimple1 created
+model.mlops.seldon.io/tfsimple2 created
+
+```
+
+```bash
+kubectl wait --for condition=ready --timeout=300s pipeline --all -n ${NAMESPACE}
+```
+
+```
+pipeline.mlops.seldon.io/tfsimples condition met
+
+```
+
+```bash
+kubectl get pipeline tfsimples -o jsonpath='{.status.conditions[0]}' -n ${NAMESPACE}
+```
+
+```json
+{"lastTransitionTime":"2022-11-14T10:25:49Z","status":"True","type":"ModelsReady"}
+
+```
+
+```bash
+kubectl delete -f ./models/tfsimple1.yaml -n ${NAMESPACE}
+kubectl delete -f ./models/tfsimple2.yaml -n ${NAMESPACE}
+kubectl delete -f ./pipelines/tfsimples.yaml -n ${NAMESPACE}
+```
+
+```
+model.mlops.seldon.io "tfsimple1" deleted
+model.mlops.seldon.io "tfsimple2" deleted
+pipeline.mlops.seldon.io "tfsimples" deleted
+
+```
diff --git a/docs/examples/pipeline-to-pipeline.md b/docs/examples/pipeline-to-pipeline.md
index de8692bacd..c445266ebb 100644
--- a/docs/examples/pipeline-to-pipeline.md
+++ b/docs/examples/pipeline-to-pipeline.md
@@ -6,7 +6,8 @@ This notebook illustrates a series of Pipelines that are joined together.
 
 * `gs://seldon-models/triton/simple` an example Triton tensorflow model that takes 2
  inputs INPUT0 and INPUT1 and adds them to produce OUTPUT0 and also subtracts `INPUT1`
- from `INPUT0` to produce OUTPUT1. See [here](https://github.com/triton-inference-server/server/tree/main/docs/examples/model_repository/simple) for the original source code and license.
+ from `INPUT0` to produce OUTPUT1. See [here](https://github.com/triton-inference-server/server/tree/main/docs/examples/model_repository/simple)
+ for the original source code and license.
 * Other models can be found at https://github.com/SeldonIO/triton-python-examples
 
 ### Pipeline pulling from one other Pipeline
diff --git a/docs/examples/speech-to-sentiment.md b/docs/examples/speech-to-sentiment.md
index 9d93b8a011..ba2abca8ae 100644
--- a/docs/examples/speech-to-sentiment.md
+++ b/docs/examples/speech-to-sentiment.md
@@ -1,12 +1,14 @@
 # Huggingface speech to sentiment pipeline
 
-In this example we create a Pipeline to chain two huggingface models to allow speech to sentiment functionalityand add an explainer to understand the result.
+In this example we create a Pipeline to chain two huggingface models to allow speech to
+sentiment functionalityand add an explainer to understand the result.
 
 This example also illustrates how explainers can target pipelines to allow complex explanations flows.
 
 ![architecture](../images/speech-to-sentiment.jpg)
 
-This example requires **ffmpeg** package to be installed locally. run `make install-requirements` for the Python dependencies.
+This example requires **ffmpeg** package to be installed locally. run `make install-requirements`
+for the Python dependencies.
 
 ```python
 from ipywebrtc import AudioRecorder, CameraStream
@@ -19,7 +21,8 @@ import os
 import time
 ```
 
-Create a method to load speech from recorder; transform into mp3 and send at base64 data. On return of the result extract and show the text and sentiment.
+Create a method to load speech from recorder; transform into mp3 and send at base64 data. On
+return of the result extract and show the text and sentiment.
 
 ```python
 reqJson = json.loads('{"inputs":[{"name":"args", "parameters": {"content_type": "base64"}, "data":[],"datatype":"BYTES","shape":[1]}]}')

From d1cae55444f1d5fb2052e5e47122c6f55903c411 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Wed, 21 Aug 2024 21:27:35 +1000
Subject: [PATCH 22/32] added gitbook format to metrics dir

---
 docs/metrics/README.md             |  13 ++
 docs/metrics/local-metrics-test.md | 189 ++++++++++++++++++++++++++++-
 docs/metrics/operational.md        |  84 ++++++++-----
 docs/metrics/usage.md              | 106 +++++++++-------
 4 files changed, 316 insertions(+), 76 deletions(-)
 create mode 100644 docs/metrics/README.md

diff --git a/docs/metrics/README.md b/docs/metrics/README.md
new file mode 100644
index 0000000000..91cabe2916
--- /dev/null
+++ b/docs/metrics/README.md
@@ -0,0 +1,13 @@
+# Metrics
+
+There are two kinds of metrics present in Seldon Core v2:
+* [operational metrics](./operational.md)
+* [usage metrics](./usage.md)
+
+Operational metrics describe the performance of components in the system. Some examples of common operational
+considerations are memory consumption and CPU usage, request latency and throughput, and cache utilisation rates.
+Generally speaking, these are the metrics system administrators, operations teams, and engineers will be interested in.
+
+Usage metrics describe the system at a higher and less dynamic level. Some examples include the number of deployed
+servers and models, and component versions. These are not typically metrics that engineers need insight into, but
+may be relevant to platform providers and operations teams.
diff --git a/docs/metrics/local-metrics-test.md b/docs/metrics/local-metrics-test.md
index 4da3f44454..9aa850bcb8 100644
--- a/docs/metrics/local-metrics-test.md
+++ b/docs/metrics/local-metrics-test.md
@@ -1,8 +1,193 @@
 # Local Metrics
 
-Run these examples from the `samples` folder.
+Run these examples from the `samples` folder at the root of the repo.
 
+This notebook tests the exposed Prometheus metrics of model and pipeline servers.
+
+Requires: `prometheus_client` and `requests` libraries.
+See docs for full set of metrics available.
+
+```python
+mlserver_metrics_host="0.0.0.0:9006"
+triton_metrics_host="0.0.0.0:9007"
+pipeline_metrics_host="0.0.0.0:9009"
+```
+
+```python
+from prometheus_client.parser import text_string_to_metric_families
+import requests
+
+def scrape_metrics(host):
+    data = requests.get(f"http://{host}/metrics").text
+    return {
+        family.name: family for family in text_string_to_metric_families(data)
+    }
+
+def print_sample(family, label, value):
+    for sample in family.samples:
+        if sample.labels[label] == value:
+            print(sample)
+
+def get_model_infer_count(host, model_name):
+    metrics = scrape_metrics(host)
+    family = metrics["seldon_model_infer"]
+    print_sample(family, "model", model_name)
+
+def get_pipeline_infer_count(host, pipeline_name):
+    metrics = scrape_metrics(host)
+    family = metrics["seldon_pipeline_infer"]
+    print_sample(family, "pipeline", pipeline_name)
+```
+
+### MLServer Model
+
+```bash
+seldon model load -f ./models/sklearn-iris-gs.yaml
+seldon model status iris -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
+```
+
+```bash
+seldon model infer iris -i 50 \
+  '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+```
+Success: map[:iris_1::50]
+
+```
+
+```bash
+seldon model infer iris --inference-mode grpc -i 100 \
+   '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}'
+```
+
+```
+Success: map[:iris_1::100]
+
+```
+
+```python
+get_model_infer_count(mlserver_metrics_host,"iris")
+```
+
+```
+Sample(name='seldon_model_infer_total', labels={'code': '200', 'method_type': 'rest', 'model': 'iris', 'model_internal': 'iris_1', 'server': 'mlserver', 'server_replica': '0'}, value=50.0, timestamp=None, exemplar=None)
+Sample(name='seldon_model_infer_total', labels={'code': 'OK', 'method_type': 'grpc', 'model': 'iris', 'model_internal': 'iris_1', 'server': 'mlserver', 'server_replica': '0'}, value=100.0, timestamp=None, exemplar=None)
+```
+
+```bash
+seldon model unload iris
+```
+
+```json
+{}
+
+```
+
+### Triton Model
+
+Load the model.
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+```
+
+```json
+{}
+{}
 
-```{include} ../../../../samples/metrics.md
 ```
 
+```bash
+seldon model infer tfsimple1 -i 50\
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```
+Success: map[:tfsimple1_1::50]
+
+```
+
+```bash
+seldon model infer tfsimple1 --inference-mode grpc -i 100 \
+    '{"model_name":"tfsimple1","inputs":[{"name":"INPUT0","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","contents":{"int_contents":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]},"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```
+Success: map[:tfsimple1_1::100]
+
+```
+
+```python
+get_model_infer_count(triton_metrics_host,"tfsimple1")
+```
+
+```
+Sample(name='seldon_model_infer_total', labels={'code': '200', 'method_type': 'rest', 'model': 'tfsimple1', 'model_internal': 'tfsimple1_1', 'server': 'triton', 'server_replica': '0'}, value=50.0, timestamp=None, exemplar=None)
+Sample(name='seldon_model_infer_total', labels={'code': 'OK', 'method_type': 'grpc', 'model': 'tfsimple1', 'model_internal': 'tfsimple1_1', 'server': 'triton', 'server_replica': '0'}, value=100.0, timestamp=None, exemplar=None)
+
+```
+
+```bash
+seldon model unload tfsimple1
+```
+
+```json
+{}
+
+```
+
+### Pipeline
+
+```bash
+seldon model load -f ./models/tfsimple1.yaml
+seldon model load -f ./models/tfsimple2.yaml
+seldon model status tfsimple1 -w ModelAvailable | jq -M .
+seldon model status tfsimple2 -w ModelAvailable | jq -M .
+seldon pipeline load -f ./pipelines/tfsimples.yaml
+seldon pipeline status tfsimples -w PipelineReady
+```
+
+```json
+{}
+{}
+{}
+{}
+{}
+{"pipelineName":"tfsimples", "versions":[{"pipeline":{"name":"tfsimples", "uid":"cdqji39qa12c739ab3o0", "version":2, "steps":[{"name":"tfsimple1"}, {"name":"tfsimple2", "inputs":["tfsimple1.outputs"], "tensorMap":{"tfsimple1.outputs.OUTPUT0":"INPUT0", "tfsimple1.outputs.OUTPUT1":"INPUT1"}}], "output":{"steps":["tfsimple2.outputs"]}, "kubernetesMeta":{}}, "state":{"pipelineVersion":2, "status":"PipelineReady", "reason":"created pipeline", "lastChangeTimestamp":"2022-11-16T19:25:01.255955114Z"}}]}
+```
+
+```bash
+seldon pipeline infer tfsimples -i 50 \
+    '{"inputs":[{"name":"INPUT0","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]},{"name":"INPUT1","data":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],"datatype":"INT32","shape":[1,16]}]}'
+```
+
+```
+Success: map[:tfsimple1_1::50 :tfsimple2_1::50 :tfsimples.pipeline::50]
+```
+
+```python
+get_pipeline_infer_count(pipeline_metrics_host,"tfsimples")
+```
+
+```
+Sample(name='seldon_pipeline_infer_total', labels={'code': '200', 'method_type': 'rest', 'pipeline': 'tfsimples', 'server': 'pipeline-gateway'}, value=50.0, timestamp=None, exemplar=None)
+```
+
+```bash
+seldon model unload tfsimple1
+seldon model unload tfsimple2
+seldon pipeline unload tfsimples
+```
+
+```json
+{}
+{}
+{}
+```
diff --git a/docs/metrics/operational.md b/docs/metrics/operational.md
index 7806629124..bcf6876ab3 100644
--- a/docs/metrics/operational.md
+++ b/docs/metrics/operational.md
@@ -1,10 +1,9 @@
 # Operational Metrics
 
-While the system is running we collect metrics via Prometheus that allow users to observe different aspects of SCv2 with regards to throughout, latency, memory, CPU etc.
-
-This is in addition to the standard Kubernetes metrics that are scraped by Prometheus.
-
-There is a Grafana dashboard (referenced below) that provides an overview of the system.
+While the system is running we collect metrics via Prometheus that allow users to observe different
+aspects of SCv2 with regards to throughput, latency, memory, CPU etc. This is in addition to the standard
+Kubernetes metrics that are scraped by Prometheus. There is a also a Grafana dashboard (referenced below)
+that provides an overview of the system.
 
 ## List of SCv2 metrics
 
@@ -12,50 +11,73 @@ The list of SCv2 metrics that we are compiling is as follows.
 
 For the agent that sits next to the inference servers:
 
-  ```{literalinclude} ../../../../scheduler/pkg/metrics/agent.go
-   :language: golang
-   :start-after: // Docs Start Metrics
-   :end-before: // Docs End Metrics
-   ```
+```go
+// scheduler/pkg/metrics/agent.go
+const (
+	// Histograms do no include pipeline label for efficiency
+	modelHistogramName = "seldon_model_infer_api_seconds"
+	// We use base infer counters to store core metrics per pipeline
+	modelInferCounterName                 = "seldon_model_infer_total"
+	modelInferLatencyCounterName          = "seldon_model_infer_seconds_total"
+	modelAggregateInferCounterName        = "seldon_model_aggregate_infer_total"
+	modelAggregateInferLatencyCounterName = "seldon_model_aggregate_infer_seconds_total"
+)
+
+// Agent metrics
+const (
+	cacheEvictCounterName                              = "seldon_cache_evict_count"
+	cacheMissCounterName                               = "seldon_cache_miss_count"
+	loadModelCounterName                               = "seldon_load_model_counter"
+	unloadModelCounterName                             = "seldon_unload_model_counter"
+	loadedModelGaugeName                               = "seldon_loaded_model_gauge"
+	loadedModelMemoryGaugeName                         = "seldon_loaded_model_memory_bytes_gauge"
+	evictedModelMemoryGaugeName                        = "seldon_evicted_model_memory_bytes_gauge"
+	serverReplicaMemoryCapacityGaugeName               = "seldon_server_replica_memory_capacity_bytes_gauge"
+	serverReplicaMemoryCapacityWithOverCommitGaugeName = "seldon_server_replica_memory_capacity_overcommit_bytes_gauge"
+)
+```
 
 For the pipeline gateway that handles requests to pipelines:
 
-   ```{literalinclude} ../../../../scheduler/pkg/metrics/gateway.go
-   :language: golang
-   :start-after: // Docs Start Metrics
-   :end-before: // Docs End Metrics
-   ```
+```go
+// scheduler/pkg/metrics/gateway.go
+// The aggregate metrics exist for efficiency, as the summation can be
+// very slow in Prometheus when many pipelines exist.
+const (
+	// Histograms do no include model label for efficiency
+	pipelineHistogramName = "seldon_pipeline_infer_api_seconds"
+	// We use base infer counters to store core metrics per pipeline
+	pipelineInferCounterName                 = "seldon_pipeline_infer_total"
+	pipelineInferLatencyCounterName          = "seldon_pipeline_infer_seconds_total"
+	pipelineAggregateInferCounterName        = "seldon_pipeline_aggregate_infer_total"
+	pipelineAggregateInferLatencyCounterName = "seldon_pipeline_aggregate_infer_seconds_total"
+)
+```
 
-Many of these metrics are model and pipeline level counters and gauges.
-We also aggregate some of these metrics to speed up the display of graphs. We don't presently store per-model histogram metrics for performance reasons. However, we do presently store per-pipeline histogram metrics.
+Many of these metrics are model and pipeline level counters and gauges. We also aggregate some of
+these metrics to speed up the display of graphs. We don't presently store per-model histogram metrics
+for performance reasons. However, we do presently store per-pipeline histogram metrics.
 
-This is experimental and these metrics are bound to change to reflect the trends we want to capture as we get more information about the usage of the system.
+This is experimental and these metrics are bound to change to reflect the trends we want to capture as
+we get more information about the usage of the system.
 
 ## Grafana dashboard
 
 We have a prebuilt Grafana dashboard that makes use of many of the metrics that we expose.
 
-![kafka](dashboard.png)
+![kafka](../images/dashboard.png)
 
 ### Local Use
 
-Grafana and Prometheus are available when you run Seldon locally.
-You will be able to connect to the Grafana dashboard at `http://localhost:3000`.
-Prometheus will be available at `http://localhost:9090`.
+Grafana and Prometheus are available when you run Seldon locally. You will be able to connect to the Grafana
+dashboard at `http://localhost:3000`. Prometheus will be available at `http://localhost:9090`.
 
 ### Kubernetes Installation
 
-Download the dashboard from [SCv2 dashboard](https://github.com/SeldonIO/seldon-core/blob/v2/prometheus/dashboards/seldon.json) and import it in Grafana, making sure that the data source is pointing to the correct Prometheus store.
+Download the dashboard from [SCv2 dashboard](https://github.com/SeldonIO/seldon-core/blob/v2/prometheus/dashboards/seldon.json)
+and import it in Grafana, making sure that the data source is pointing to the correct Prometheus store.
 Find more information on how to import the dashboard [here](https://grafana.com/docs/grafana/latest/dashboards/export-import/).
 
 ### Local Metrics Examples
 
 An [example](./local-metrics-test.md) to show raw metrics that Prometheus will scrape.
-
-```{toctree}
-:maxdepth: 1
-:hidden:
-
-local-metrics-test.md
-```
-
diff --git a/docs/metrics/usage.md b/docs/metrics/usage.md
index 22fdc7dfce..d7e6994df9 100644
--- a/docs/metrics/usage.md
+++ b/docs/metrics/usage.md
@@ -1,17 +1,19 @@
 # Usage Metrics
 
-There are various interesting system metrics about how Seldon Core v2 is used.
-These metrics can be recorded **anonymously** and sent to Seldon by a lightweight, optional, stand-alone component called Hodometer.
+There are various interesting system metrics about how Seldon Core v2 is used. These metrics can be
+recorded **anonymously** and sent to Seldon by a lightweight, optional, stand-alone component called Hodometer.
 
-When provided, these metrics will be used to understand the adoption of Seldon Core v2 and how people interact with it.
-For example, knowing how many clusters Seldon Core v2 is running on, if it is used in Kubernetes or for local development, and how many people are benefitting from features like multi-model serving.
+When provided, these metrics will be used to understand the adoption of Seldon Core v2 and how people interact
+with it. For example, knowing how many clusters Seldon Core v2 is running on, if it is used in Kubernetes or for
+local development, and how many people are benefitting from features like multi-model serving.
 
 ## Architecture
 
-![Hodometer architecture](./hodometer-architecture.png)
+![Hodometer architecture](../images/hodometer-architecture.png)
 
-Hodometer is not an integral part of Seldon Core v2, but rather an independent component which connects to the public APIs of the Seldon Core v2 scheduler.
-If deployed in Kubernetes, it will also try to request some basic information from the Kubernetes API.
+Hodometer is not an integral part of Seldon Core v2, but rather an independent component which connects to
+the public APIs of the Seldon Core v2 scheduler. If deployed in Kubernetes, it will also try to request
+some basic information from the Kubernetes API.
 
 Recorded metrics are sent to Seldon and, optionally, to any [additional endpoints](#extra-publish-urls) you define.
 
@@ -19,25 +21,26 @@ Recorded metrics are sent to Seldon and, optionally, to any [additional endpoint
 
 Hodometer was explicitly designed with privacy of user information and transparency of implementation in mind.
 
-It does not record any sensitive or identifying information.
-For example, it has no knowledge of IP addresses, model names, or user information.
+It does not record any sensitive or identifying information. For example, it has no knowledge of IP addresses,
+model names, or user information. All information sent to Seldon is anonymised with a completely random
+cluster identifier.
 
-All information sent to Seldon is anonymised with a completely random cluster identifier.
+Hodometer supports [different information levels](#metrics-levels), so you have full control over what
+metrics are provided to Seldon, if any.
 
-Hodometer supports [different information levels](#metrics-levels), so you have full control over what metrics are provided to Seldon, if any.
-
-For transparency, the implementation is fully open-source and designed to be easy to read.
-The full source code is available [here](https://github.com/seldonio/seldon-core/tree/v2/hodometer), with metrics defined in code [here](https://github.com/seldonio/seldon-core/tree/v2/hodometer/pkg/hodometer/metrics.go).
-See [below](#list-of-metrics) for an equivalent table of metrics.
+For transparency, the implementation is fully open-source and designed to be easy to read. The full source
+code is available [here](https://github.com/seldonio/seldon-core/tree/v2/hodometer), with metrics defined in
+code [here](https://github.com/seldonio/seldon-core/tree/v2/hodometer/pkg/hodometer/metrics.go). See
+[below](#list-of-metrics) for an equivalent table of metrics.
 
 ## Performance
 
-Metrics are collected as periodic snapshots a few times per day.
-They are lightweight to collect, coming mostly from the Seldon Core v2 scheduler, and are heavily aggregated.
-As such, they should have minimal impact on CPU, memory, and network consumption.
+Metrics are collected as periodic snapshots a few times per day. They are lightweight to collect, coming
+mostly from the Seldon Core v2 scheduler, and are heavily aggregated. As such, they should have minimal
+impact on CPU, memory, and network consumption.
 
-Hodometer does not store anything it records, so does not have any persistent storage.
-As a result, it should not be considered a replacement for tools like Prometheus.
+Hodometer does not store anything it records, so does not have any persistent storage. As a result, it
+should not be considered a replacement for tools like Prometheus.
 
 ## Configuration
 
@@ -70,13 +73,11 @@ The following environment variables control the behaviour of Hodometer, regardle
 
 Hodometer is installed as a separate deployment, by default in the same namespace as the rest of the Seldon components.
 
-`````{tabs}
-
-````{group-tab} Helm
+**Helm**
 
-
-If you install Seldon Core v2 by [Helm chart](../getting-started/kubernetes-installation/helm.md), there are values corresponding to the key environment variables discussed [above](#options).
-These Helm values and their equivalents are provided below:
+If you install Seldon Core v2 by [Helm chart](../getting-started/kubernetes-installation/helm.md), there are
+values corresponding to the key environment variables discussed [above](#options). These Helm values and their
+equivalents are provided below:
 
 | Helm value | Environment variable |
 | --- | --- |
@@ -84,8 +85,9 @@ These Helm values and their equivalents are provided below:
 | `hodometer.extraPublishUrls` | `EXTRA_PUBLISH_URLS` |
 | `hodometer.logLevel` | `LOG_LEVEL` |
 
-If you do not want usage metrics to be recorded, you can disable Hodometer via the `hodometer.disable` Helm value when installing the runtime Helm chart.
-The following command disables collection of usage metrics in fresh installations and also serves to remove Hodometer from an existing installation:
+If you do not want usage metrics to be recorded, you can disable Hodometer via the `hodometer.disable` Helm
+value when installing the runtime Helm chart. The following command disables collection of usage metrics in
+fresh installations and also serves to remove Hodometer from an existing installation:
 
 ```bash
 helm install seldon-v2-runtime k8s/helm-charts/seldon-core-v2-runtime \
@@ -93,20 +95,19 @@ helm install seldon-v2-runtime k8s/helm-charts/seldon-core-v2-runtime \
   --set hodometer.disable=true
 ```
 
-```{note}
+{% hint style="info" %}
 It is a good practice to set Helm values in values file.
 These can be applied by using the `-f <filename>` switch when running Helm.
-```
+{% endhint %}
 
-````
-`````
 
 ### Docker Compose
 
-The [Compose setup](../getting-started/docker-installation/index.md) provides a pre-configured and opinionated, yet still flexible, approach to using Seldon Core v2.
+The [Compose setup](../getting-started/docker-installation/index.md) provides a pre-configured and opinionated,
+yet still flexible, approach to using Seldon Core v2.
 
-Hodometer is defined as a service called `hodometer` in the Docker Compose manifest.
-It is automatically enabled when running as per the installation instructions.
+Hodometer is defined as a service called `hodometer` in the Docker Compose manifest. It is automatically enabled
+when running as per the installation instructions.
 
 You can disable Hodometer in Docker Compose by removing the corresponding service from the base manifest.
 Alternatively, you can gate it behind a [profile](https://docs.docker.com/compose/profiles/).
@@ -118,18 +119,37 @@ The available variables are defined in the Docker Compose environment file, pref
 ### Extra publish URLs
 
 Hodometer can be instructed to publish metrics not only to Seldon, but also to any extra endpoints you specify.
-This is controlled by the `EXTRA_PUBLISH_URLS` environment variable, which expects a comma-separated list of HTTP-compatible URLs.
+This is controlled by the `EXTRA_PUBLISH_URLS` environment variable, which expects a comma-separated list of
+HTTP-compatible URLs.
 
-You might choose to use this for your own usage monitoring.
-For example, you could capture these metrics and expose them to Prometheus or another monitoring system using your own service.
+You might choose to use this for your own usage monitoring. For example, you could capture these metrics and
+expose them to Prometheus or another monitoring system using your own service.
 
 Metrics are recorded in MixPanel-compatible format, which employs a highly flexible JSON schema.
 
-For an example of how to define your own metrics listener, see the [`receiver` Go package](https://github.com/SeldonIO/seldon-core/tree/v2/hodometer/pkg/receiver) in the `hodometer` sub-project.
+For an example of how to define your own metrics listener, see the
+[`receiver` Go package](https://github.com/SeldonIO/seldon-core/tree/v2/hodometer/pkg/receiver) in the `hodometer` sub-project.
 
 ## List of metrics
 
-```{include} ../../../../hodometer/README.md
-:start-after: <!-- start list metrics -->
-:end-before: <!-- end list metrics -->
-```
+<!-- hodometer/README.md -->
+
+| Metric name | Level | Format | Notes |
+| --- | --- | --- | --- |
+| `cluster_id` | cluster | UUID | A random identifier for this cluster for de-duplication |
+| `seldon_core_version` | cluster | Version number | E.g. 1.2.3 |
+| `is_global_installation` | cluster | Boolean | Whether installation is global or namespaced |
+| `is_kubernetes` | cluster | Boolean | Whether or not the installation is in Kubernetes |
+| `kubernetes_version` | cluster | Version number | Kubernetes server version, if inside Kubernetes |
+| `node_count` | cluster | Integer | Number of nodes in the cluster, if inside Kubernetes |
+| `model_count` | resource | Integer | Number of `Model` resources |
+| `pipeline_count` | resource | Integer | Number of `Pipeline` resources |
+| `experiment_count` | resource | Integer | Number of `Experiment` resources |
+| `server_count` | resource | Integer | Number of `Server` resources |
+| `server_replica_count` | resource | Integer | Total number of `Server` resource replicas |
+| `multimodel_enabled_count` | feature | Integer | Number of `Server` resources with multi-model serving enabled |
+| `overcommit_enabled_count` | feature | Integer | Number of `Server` resources with overcommitting enabled |
+| `gpu_enabled_count` | feature | Integer | Number of `Server` resources with GPUs attached |
+| `inference_server_name` | feature | String | Name of inference server, e.g. MLServer or Triton |
+| `server_cpu_cores_sum` | feature | Float | Total of CPU limits across all `Server` resource replicas, in cores |
+| `server_memory_gb_sum` | feature | Float | Total of memory limits across all `Server` resource replicas, in GiB |

From a9c3227a1222cf331272c00125b9ae1312669434 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Wed, 21 Aug 2024 21:28:19 +1000
Subject: [PATCH 23/32] restructured k8s directory to match GitBook's expected
 md flavor

---
 docs/kubernetes/README.md                    |   5 +
 docs/kubernetes/autoscaling.md               | 124 ++++
 docs/kubernetes/kafka.md                     |  93 +++
 docs/kubernetes/metrics.md                   |  48 ++
 docs/kubernetes/resources/README.md          |  19 +
 docs/kubernetes/resources/experiment.md      | 139 +++++
 docs/kubernetes/resources/model.md           |  21 +
 docs/kubernetes/resources/pipeline.md        |  24 +
 docs/kubernetes/resources/seldonconfig.md    |  30 +
 docs/kubernetes/resources/seldonruntime.md   |  61 ++
 docs/kubernetes/resources/server.md          | 141 +++++
 docs/kubernetes/resources/serverconfig.md    |  17 +
 docs/kubernetes/scaling.md                   |  71 +++
 docs/kubernetes/service-meshes/README.md     |  15 +
 docs/kubernetes/service-meshes/ambassador.md | 601 +++++++++++++++++++
 docs/kubernetes/service-meshes/istio.md      | 568 ++++++++++++++++++
 docs/kubernetes/service-meshes/traefik.md    |  25 +
 docs/kubernetes/storage-secrets.md           | 212 +++++++
 docs/kubernetes/tracing.md                   |  12 +
 19 files changed, 2226 insertions(+)
 create mode 100644 docs/kubernetes/README.md
 create mode 100644 docs/kubernetes/autoscaling.md
 create mode 100644 docs/kubernetes/kafka.md
 create mode 100644 docs/kubernetes/metrics.md
 create mode 100644 docs/kubernetes/resources/README.md
 create mode 100644 docs/kubernetes/resources/experiment.md
 create mode 100644 docs/kubernetes/resources/model.md
 create mode 100644 docs/kubernetes/resources/pipeline.md
 create mode 100644 docs/kubernetes/resources/seldonconfig.md
 create mode 100644 docs/kubernetes/resources/seldonruntime.md
 create mode 100644 docs/kubernetes/resources/server.md
 create mode 100644 docs/kubernetes/resources/serverconfig.md
 create mode 100644 docs/kubernetes/scaling.md
 create mode 100644 docs/kubernetes/service-meshes/README.md
 create mode 100644 docs/kubernetes/service-meshes/ambassador.md
 create mode 100644 docs/kubernetes/service-meshes/istio.md
 create mode 100644 docs/kubernetes/service-meshes/traefik.md
 create mode 100644 docs/kubernetes/storage-secrets.md
 create mode 100644 docs/kubernetes/tracing.md

diff --git a/docs/kubernetes/README.md b/docs/kubernetes/README.md
new file mode 100644
index 0000000000..0e40416b44
--- /dev/null
+++ b/docs/kubernetes/README.md
@@ -0,0 +1,5 @@
+# Kubernetes
+
+Kubernetes provides the core platform for production use. However, the architecture in
+agnostic to the underlying cluster technology as shown by the
+[local Docker installation](../getting-started/docker-installation.md).
diff --git a/docs/kubernetes/autoscaling.md b/docs/kubernetes/autoscaling.md
new file mode 100644
index 0000000000..00b2450cf8
--- /dev/null
+++ b/docs/kubernetes/autoscaling.md
@@ -0,0 +1,124 @@
+# Autoscaling
+
+Autoscaling in Seldon applies to various concerns:
+
+* Inference servers autoscaling
+* Model autoscaling
+* Model memory overcommit
+
+## Inference servers autoscaling
+
+
+Autoscaling of servers can be done via `HorizontalPodAutoscaler` (HPA).
+
+HPA can be applied to any deployed `Server` resource. In this case HPA will manage the number of
+server replicas in the corresponding statefulset according to utilisation metrics  (e.g. CPU or memory).
+
+For example assuming that a `triton` server is deployed, then the user can attach an HPA based on
+cpu utilisation as follows:
+
+```sh
+kubectl autoscale server triton --cpu-percent=50 --min=1 --max=5
+```
+
+In this case, according to load, the system will add / remove server replicas to / from the
+`triton` statefulset.
+
+It is worth considering the following points:
+
+- If HPA adds a new server replica, this new replica will be included in any **future** scheduling decisions.
+  In other words, when deploying a new model or rescheduling failed models this new replica will be considered.
+- If HPA deletes an existing server replica, the scheduler will first attempt to drain any loaded model
+on this server replica before the server replica gets actually deleted. This is achieved by leveraging a
+`PreStop` hook on the server replica pod that triggers a process before receiving the termination signal.
+This draining process is capped by `terminationGracePeriodSeconds`, which the user can set (default is 2 minutes).
+
+Therefore there should generally be minimal disruption to the inference workload during scaling.
+
+For more details on HPA check this [Kubernetes walk-through](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/).
+
+{% hint style="info" %}
+Autoscaling of inference servers via `seldon-scheduler` is under consideration for the roadmap. This allow for more fine grained interactions with model autoscaling.
+{% endhint %}
+
+## Model autoscaling
+
+As each model server can serve multiple models, models can scale across the available replicas of the
+server according to load.
+
+Autoscaling of models is enabled if at least `MinReplicas` or `MaxReplicas` is set in the model custom
+resource. Then according to load the system will scale the number of `Replicas` within this range.
+
+For example the following model will be deployed at first with 1 replica and it can scale up according to load.
+```yaml
+# samples/models/tfsimple_scaling.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: tfsimple
+spec:
+  storageUri: "gs://seldon-models/triton/simple"
+  requirements:
+  - tensorflow
+  memory: 100Ki
+  minReplicas: 1
+  replicas: 1
+```
+
+Note that model autoscaling will not attempt to add extra servers if the desired number of replicas
+cannot be currently fulfilled by the current provisioned number of servers. This is a process left to
+be done by server autoscaling.
+
+Additionally when the system autoscales, the initial model spec is not changed (e.g. the number of
+`Replicas`) and therefore the user cannot reset the number of replicas back to the initial specified
+value without an explicit change.
+
+If only `Replicas` is specified by the user, autoscaling of models is disabled and the system will
+have exactly the number of replicas of this model deployed regardless of inference load.
+
+### Architecture
+
+The model autoscaling architecture is designed such as each agent decides on which models to scale up /
+down according to some defined internal metrics and then sends a triggering message to the scheduler.
+The current metrics are collected from the data plane (inference path), representing a proxy on how loaded
+is a given model with fulfilling inference requests.
+
+![architecture](../images/autoscaling_architecture.png)
+
+
+### Agent autoscaling stats collection
+
+#### Scale up logic:
+The main idea is that we keep the "lag" for each model. We define the "lag" as the difference between
+incoming and outgoing requests in a given time period. If the lag crosses a threshold, then we trigger a
+model scale up event. This threshold can be defined via `SELDON_MODEL_INFERENCE_LAG_THRESHOLD` inference
+server environment variable.
+
+#### Scale down logic:
+For now we keep things simple and we trigger model scale down events if a model has not been used for a
+number of seconds. This is defined in `SELDON_MODEL_INACTIVE_SECONDS_THRESHOLD` inference server environment variable.
+
+Each agent checks the above stats periodically and if any model hits the corresponding threshold, then the
+agent sends an event to the scheduler to request model scaling.
+
+How often this process executes can be defined via `SELDON_SCALING_STATS_PERIOD_SECONDS` inference server
+environment variable.
+
+### Scheduler autoscaling
+
+![state](../images/scheduler_autoscaling_state_diagram.png)
+
+The scheduler will perform model autoscale if:
+* The model is stable (no state change in the last 5 minutes) and available.
+* The desired number of replicas is within range. Note we always have a least 1 replica of any deployed
+model and we rely on over commit to reduce the resources used further.
+* For scaling up, there is enough capacity for the new model replica.
+
+## Model memory overcommit
+
+Servers can hold more models than available memory if overcommit is swictched on (default yes). This
+allows under utilized models to be moved from inference server memory to allow for other models to
+take their place. Note that these evicted models are still registered and in the case future inference
+requests arrive, the system will reload the models back to memory before serving the requests. If
+traffic patterns for inference of models vary then this can allow more models than available server
+memory to be run on the system.
diff --git a/docs/kubernetes/kafka.md b/docs/kubernetes/kafka.md
new file mode 100644
index 0000000000..dc21007f09
--- /dev/null
+++ b/docs/kubernetes/kafka.md
@@ -0,0 +1,93 @@
+# Kafka
+
+Seldon Core v2 requires [Kafka](https://kafka.apache.org/) to implement data-centric inference Pipelines.
+See our [architecture](../architecture/README.md) documentation to learn more on how Seldon Core v2 uses Kafka.
+
+
+{% hint style="info" %}
+Kafka integration is required to enable data-centric inference pipelines feature.
+It is highly advice to configure Kafka integration to take full advantage of Seldon Core v2 features.
+{% endhint %}
+
+We list alternatives below.
+
+## Managed Kafka
+
+We recommend to use managed Kafka solution for production installation.
+This allow to take away all the complexity on running secure and scalable Kafka cluster away.
+
+We currently have tested and documented integration with following managed solutions:
+- Confluent Cloud (security: SASL/PLAIN)
+- Confluent Cloud (security: SASL/OAUTHBEARER)
+- Amazon MSK (security: mTLS)
+- Amazon MSK (security: SASL/SCRAM)
+- Azure Event Hub (security: SASL/PLAIN)
+
+See our [Kafka security](../getting-started/kubernetes-installation/security.md#kafka)
+section for configuration examples.
+
+## Self Hosted Kafka
+
+### Strimzi Kafka
+
+Seldon Core v2 requires Kafka to implement data-centric inference Pipelines.
+To install Kafka for testing purposed in your k8s cluster, we recommend to use [Strimzi Operator](https://github.com/strimzi/strimzi-kafka-operator).
+
+{% hint style="info" %}
+This page discuss how to install Strimzi Operator and create Kafka cluster for trial, dev, or testing purposes.
+For production grade installation consult [Strimzi documentation](https://strimzi.io/documentation/) or use one of managed solutions mentioned [here](./index.md).
+{% endhint %}
+
+You can install and configure Strimzi using either Helm charts or our Ansible playbooks, both documented below.
+
+## Helm
+
+The installation of a Kafka cluster requires the Strimzi Kafka operator installed in the same namespace.
+This allows to directly use the mTLS certificates created by Strimzi Operator.
+One option to install the Strimzi operator is via [Helm](https://strimzi.io/docs/operators/in-development/full/deploying.html#deploying-cluster-operator-helm-chart-str).
+
+Note that we are using here KRaft instead of Zookeeper for Kafka.
+You can enable `featureGates` during Helm installation via:
+
+```bash
+helm upgrade --install strimzi-kafka-operator \
+  strimzi/strimzi-kafka-operator \
+  --namespace seldon-mesh --create-namespace \
+  --set featureGates='+UseKRaft\,+UseStrimziPodSets'
+```
+
+{% hint style="warning" %}
+Use with caution!
+Currently Kraft installation of Strimzi is not production ready.
+See Strimzi [documentation](https://strimzi.io/docs/operators/0.35.0/deploying.html#ref-operator-use-kraft-feature-gate-str)
+and related GitHub [issue](https://github.com/strimzi/strimzi-kafka-operator/issues/5615) for further details.
+{% endhint %}
+
+
+Create Kafka cluster in `seldon-mesh` namespace
+
+```bash
+helm upgrade seldon-core-v2-kafka kafka/strimzi -n seldon-mesh --install
+```
+
+Note that a specific strimzi operator version is associated with a subset of supported Kafka versions.
+
+
+## Ansible
+
+We provide automation around the installation of a Kafka cluster for Seldon Core v2 to help with
+development and testing use cases.
+You can follow the steps defined [here](../getting-started/kubernetes-installation/ansible.md) to
+install Kafka via ansible.
+
+You can use our Ansible playbooks to install **only** Strimzi Operator and Kafka cluster by
+setting extra Ansible vars:
+```bash
+ansible-playbook playbooks/setup-ecosystem.yaml -e full_install=no -e install_kafka=yes
+```
+
+
+## Notes
+- You can check [kafka-examples](https://github.com/strimzi/strimzi-kafka-operator/tree/main/examples/kafka) for more details.
+- As we are using [KRaft](https://kafka.apache.org/documentation/#kraft), use Kafka version 3.3 or above.
+- For security settings check [here](../getting-started/kubernetes-installation/security.md#kafka).
diff --git a/docs/kubernetes/metrics.md b/docs/kubernetes/metrics.md
new file mode 100644
index 0000000000..59c8e2bba0
--- /dev/null
+++ b/docs/kubernetes/metrics.md
@@ -0,0 +1,48 @@
+# Metrics
+
+Metrics are exposed for scrapping by [Prometheus](https://prometheus.io/).
+
+## Example Installation
+
+We recommend to install [kube-prometheus](https://github.com/prometheus-operator/kube-prometheus#installing) that
+provides an all-in-one package with the [Prometheus operator](https://github.com/prometheus-operator/prometheus-operator).
+
+
+### RBAC
+
+You will need to modify the default RBAC installed by kube-prometheus as described
+[here](https://github.com/prometheus-operator/prometheus-operator/blob/main/Documentation/user-guides/getting-started.md#deploying-prometheus).
+
+From the `prometheus` folder in the project run:
+
+```bash
+kubectl apply -f rbac/cr.yaml
+```
+
+## Monitors
+
+We use a PodMonitor for scrapping agent metrics. The envoy and server monitors are there for
+completeness but not presently needed.
+
+```bash
+kubectl apply -f monitors
+```
+
+Includes:
+
+* Agent pod monitor. Monitors the metrics port of server inference pods.
+* Server pod monitor. Monitors the server-metrics port of inference server pods.
+* Envoy service monitor. Monitors the Envoy gateway proxies.
+* Pipeline gateway pod monitor. Monitors the metrics port of pipeline gateway pods.
+
+Pod monitors were chosen as ports for metrics are not exposed at service level as we do not have a top
+level service for server replicas but 1 headless service per replica. Future discussions could
+reference [this](https://github.com/prometheus-operator/prometheus-operator/issues/3119).
+
+## Example Grafana Dashboard
+
+Check [metrics](../metrics.md) for more information.
+
+## Reference
+
+[Prometheus CRDs](https://github.com/prometheus-operator/prometheus-operator/blob/main/Documentation/api.md)
diff --git a/docs/kubernetes/resources/README.md b/docs/kubernetes/resources/README.md
new file mode 100644
index 0000000000..b7e02142f9
--- /dev/null
+++ b/docs/kubernetes/resources/README.md
@@ -0,0 +1,19 @@
+# Resources
+
+For Kubernetes usage we provide a set of custom resources for interacting with Seldon.
+
+* [SeldonRuntime](./seldonruntime.md) - for installing Seldon in a particular namespace.
+* [Servers](./server.md) - for deploying sets of replicas of core inference servers (MLServer or Triton).
+* [Models](./model.md) - for deploying single machine learning models, custom transformation logic, drift detectors, outliers detectors and explainers.
+* [Experiments](./experiment.md) - for testing new versions of models.
+* [Pipelines](./pipeline.md) - for connecting together flows of data between models.
+
+## Advanced Customization
+
+SeldonConfig and ServerConfig define the core installation configuration and machine learning inference server
+configuration for Seldon. Normally, you would not need to customize these but this may be required for
+your particular custom installation within your organisation.
+
+* [ServerConfigs](./serverconfig.md) - for defining new types of inference server that can
+be reference by a Server resource.
+* [SeldonConfig](./seldonconfig.md) - for defining how seldon is installed
diff --git a/docs/kubernetes/resources/experiment.md b/docs/kubernetes/resources/experiment.md
new file mode 100644
index 0000000000..8928c87249
--- /dev/null
+++ b/docs/kubernetes/resources/experiment.md
@@ -0,0 +1,139 @@
+# Experiment
+
+An Experiment defines a traffic split between Models or Pipelines. This allows new versions of models and pipelines to be tested.
+
+An experiment spec has three sections:
+
+* `candidates` (required) : a set of candidate models to split traffic.
+* `default` (optional) : an existing candidate who endpoint should be modified to split traffic as defined by the candidates.
+  * Each candidate has a traffic weight. The percentage of traffic will be this weight divided by the sum of traffic weights.
+* `mirror` (optional) : a single model to mirror traffic to the candidates. Responses from this model
+will not be returned to the caller.
+
+An example experiment with a `defaultModel` is shown below:
+
+```yaml
+# samples/experiments/ab-default-model.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: experiment-sample
+spec:
+  default: iris
+  candidates:
+  - name: iris
+    weight: 50
+  - name: iris2
+    weight: 50
+```
+
+This defines a split of 50% traffic between two models `iris` and `iris2`. In this case we want to
+expose this traffic split on the existing endpoint created for the `iris` model. This allows us to
+test new versions of models (in this case `iris2`) on an existing endpoint (in this case `iris`).
+The `default` key defines the model whose endpoint we want to change. The experiment will become
+active when both underplying models are in Ready status.
+
+An experiment over two separate models which exposes a new API endpoint is shown below:
+
+```yaml
+# samples/experiments/ab.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: experiment-iris
+spec:
+  candidates:
+  - name: iris
+    weight: 50
+  - name: iris2
+    weight: 50
+```
+
+To call the endpoint add the header `seldon-model: <experiment-name>.experiment` in this case:
+`seldon-model: experiment-iris.experiment`. For example with curl:
+
+```bash
+curl http://${MESH_IP}/v2/models/experiment-iris/infer \
+   -H "Content-Type: application/json" \
+   -H "seldon-model: experiment-iris.experiment" \
+   -d '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+For examples see the [local experiments notebook](../../examples/local-experiments.md).
+
+## Pipeline Experiments
+
+Running an experiment between some pipelines is very similar. The difference is `resourceType: pipeline`
+needs to be defined and in this case the candidates or mirrors will refer to pipelines. An example is
+shown below:
+
+```yaml
+# samples/experiments/addmul10.yaml
+
+```
+For an example see the [local experiments notebook](../../examples/local-experiments.md).
+
+## Mirror Experiments
+
+A mirror can be added easily for model or pipeline experiments. An example model mirror experiment is
+shown below:
+
+```yaml
+# samples/experiments/sklearn-mirror.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: sklearn-mirror
+spec:
+  default: iris
+  candidates:
+  - name: iris
+    weight: 100
+  mirror:
+    name: iris2
+    percent: 100
+```
+For an example see the [local experiments notebook](../../examples/local-experiments.md).
+
+An example pipeline mirror experiment is shown below:
+
+```yaml
+# samples/experiments/addmul10-mirror.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Experiment
+metadata:
+  name: addmul10-mirror
+spec:
+  default: pipeline-add10
+  resourceType: pipeline
+  candidates:
+  - name: pipeline-add10
+    weight: 100
+  mirror:
+    name: pipeline-mul10
+    percent: 100
+```
+For an example see the [local experiments notebook](../../examples/local-experiments.md).
+
+
+## Sticky Sessions
+
+To allow cohorts to get consistent views in an experiment each inference request passes back
+a response header `x-seldon-route` which can be passed in future requests to an experiment to
+bypass the random traffic splits and get a prediction from the sequence of models and pipelines
+used in the initial request.
+
+Note: you must pass the normal `seldon-model` header along with the `x-seldon-route` header.
+
+This is illustrated in the [local experiments notebook](../../examples/local-experiments.md).
+
+{% hint style="info" %}
+Caveats: the models used will be the same but not necessarily the same replica instances. This
+means at present this will not work for stateful models that need to go to the same model replica instance.
+{% endhint %}
+
+## Service Meshes
+
+As an alternative you can choose to run experiments at the service mesh level if you use one of
+the popular service meshes that allow header based routing in traffic splits. For further discussion
+see [here](../service-meshes.md).
diff --git a/docs/kubernetes/resources/model.md b/docs/kubernetes/resources/model.md
new file mode 100644
index 0000000000..3cc8b0195a
--- /dev/null
+++ b/docs/kubernetes/resources/model.md
@@ -0,0 +1,21 @@
+# Model
+
+A Model is the core atomic building block. It specifies a machine learning artifact that will be loaded onto one of the running Servers. A model could be a standard machine learning inference component such as
+
+ * a Tensorflow model, PyTorch model or SKLearn model.
+ * an inference transformation component such as a SKLearn pipeline or a piece of custom python logic.
+ a monitoring component such as an outlier detector or drift detector.
+ * An alibi-explain model explainer
+
+An example is shown below for a SKLearn model for iris classification:
+
+```{literalinclude} ../../../../../../samples/models/sklearn-iris-gs.yaml 
+:language: yaml
+```
+
+Its Kubernetes `spec` has two core requirements
+
+ * A `storageUri` specifying the location of the artifact. This can be any rclone URI specification.
+ * A `requirements` list which provides tags that need to be matched by the Server that can run this artifact type. By default when you install Seldon we provide a set of Servers that cover a range of artifact types.
+
+
diff --git a/docs/kubernetes/resources/pipeline.md b/docs/kubernetes/resources/pipeline.md
new file mode 100644
index 0000000000..0080f62067
--- /dev/null
+++ b/docs/kubernetes/resources/pipeline.md
@@ -0,0 +1,24 @@
+# Pipeline
+
+Pipelines allow one to connect flows of inference data transformed by `Model` components. A directed acyclic graph (DAG) of steps can be defined to join Models together. Each Model will need to be capable of receiving a V2 inference request and respond with a V2 inference response. An example Pipeline is shown below:
+
+```{literalinclude} ../../../../../../samples/pipelines/tfsimples-join.yaml
+:language: yaml
+```
+
+The `steps` list shows three models: `tfsimple1`, `tfsimple2` and `tfsimple3`. These three models each take two tensors called `INPUT0` and `INPUT1` of integers. The models produce two outputs `OUTPUT0` (the sum of the inputs) and `OUTPUT1` (subtraction of the second input from the first).
+
+`tfsimple1` and `tfsimple2` take as inputs the input to the Pipeline: the default assumption when no explicit inputs are defined. `tfsimple3` takes one V2 tensor input from each of the outputs of `tfsimple1` and `tfsimple2`. As the outputs of `tfsimple1` and `tfsimple2` have tensors named `OUTPUT0` and `OUTPUT1` their names need to be changed to respect the expected input tensors and this is done with a `tensorMap` component providing this tensor renaming. This is only required if your models can not be directly chained together.
+
+The output of the Pipeline is the output from the `tfsimple3` model.
+
+## Detailed Specification
+
+The full GoLang specification for a Pipeline is shown below:
+
+```{literalinclude} ../../../../../../operator/apis/mlops/v1alpha1/pipeline_types.go
+:language: golang
+:start-after: // PipelineSpec
+:end-before: // PipelineStatus
+```
+
diff --git a/docs/kubernetes/resources/seldonconfig.md b/docs/kubernetes/resources/seldonconfig.md
new file mode 100644
index 0000000000..abdbd6cab7
--- /dev/null
+++ b/docs/kubernetes/resources/seldonconfig.md
@@ -0,0 +1,30 @@
+# Seldon Config
+
+{% hint style="info" %}
+This section is for advanced usage where you want to define how seldon is installed in each namespace.
+{% endhint %}
+
+The SeldonConfig resource defines the core installation components installed by Seldon. If you wish to
+install Seldon, you can use the [SeldonRuntime](seldonruntime.md) resource which allows easy
+overriding of some parts defined in this specification. In general, we advise core DevOps to use
+the default SeldonConfig or customize it for their usage. Individual installation of Seldon can
+then use the SeldonRuntime with a few overrides for special customisation needed in that namespace.
+
+The specification contains core PodSpecs for each core component and a section for general configuration
+including the ConfigMaps that are created for the Agent (rclone defaults), Kafka and Tracing (open telemetry).
+
+
+```go
+// operator/apis/mlops/v1alpha1/seldonconfig_types.go
+// :language: golang
+// :start-after: // SeldonConfigSpec
+// :end-before: // SeldonConfigStatus
+```
+Some of these values can be overridden on a per namespace basis via the SeldonRuntime resource.
+
+The default configuration is shown below.
+
+
+```yaml
+# operator/config/seldonconfigs/default.yaml
+```
diff --git a/docs/kubernetes/resources/seldonruntime.md b/docs/kubernetes/resources/seldonruntime.md
new file mode 100644
index 0000000000..a7b17f7d6b
--- /dev/null
+++ b/docs/kubernetes/resources/seldonruntime.md
@@ -0,0 +1,61 @@
+# Seldon Runtime
+
+The SeldonRuntime resource is used to create an instance of Seldon installed in a particular namespace.
+
+```go
+// operator/apis/mlops/v1alpha1/seldonruntime_types.go
+// :start-after: // SeldonRuntimeSpec
+// :end-before: // SeldonRuntimeStatus
+```
+
+For the definition of `SeldonConfiguration` above see the [SeldonConfig resource](seldonconfig.md).
+
+The specification above contains overrides for the chosen `SeldonConfig`.
+To override the `PodSpec` for a given component, the `overrides` field needs to specify the component
+name and the `PodSpec` needs to specify the container name, along with fields to override.
+
+For instance, the following overrides the resource limits for `cpu` and `memory` in the `hodometer`
+component in the `seldon-mesh` namespace, while using values specified in the `seldonConfig` elsewhere
+(e.g. `default`).
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: SeldonRuntime
+metadata:
+  name: seldon
+  namespace: seldon-mesh
+spec:
+  overrides:
+  - name: hodometer
+    podSpec:
+      containers:
+      - name: hodometer
+        resources:
+          limits:
+            memory: 64Mi
+            cpu: 20m
+  seldonConfig: default
+```
+
+As a minimal use you should just define the `SeldonConfig` to use as a base for this install, for
+example to install in the `seldon-mesh` namespace with the `SeldonConfig` named `default`:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: SeldonRuntime
+metadata:
+  name: seldon
+  namespace: seldon-mesh
+spec:
+  seldonConfig: default
+```
+
+The helm chart `seldon-core-v2-runtime` allows easy creation of this resource and associated default
+Servers for an installation of Seldon in a particular namespace.
+
+## SeldonConfig Update Propagation
+
+When a [SeldonConfig](seldonconfig.md) resource changes any SeldonRuntime resources that
+reference the changed SeldonConfig will also be updated immediately. If this behaviour is not desired
+you can set `spec.disableAutoUpdate` in the SeldonRuntime resource for it not be be updated immediately
+but only when it changes or any owned resource changes.
diff --git a/docs/kubernetes/resources/server.md b/docs/kubernetes/resources/server.md
new file mode 100644
index 0000000000..cad9a0ce91
--- /dev/null
+++ b/docs/kubernetes/resources/server.md
@@ -0,0 +1,141 @@
+# Server
+
+{% hint style="info" %}
+The default installation will provide two initial servers: one MLServer and one Triton. You only need to define additional servers for advanced use cases.
+{% endhint %}
+
+A Server defines an inference server onto which models will be placed for inference. By default on installation two server StatefulSets will be deployed one MlServer and one Triton. An example Server definition is shown below:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Server
+metadata:
+  name: mlserver
+spec:
+  serverConfig: mlserver
+  replicas: 1
+```
+
+The main requirement is a reference to a ServerConfig resource in this case `mlserver`.
+
+## Detailed Specs
+
+```go
+type ServerSpec struct {
+	// Server definition
+	ServerConfig string `json:"serverConfig"`
+	// The extra capabilities this server will advertise
+	// These are added to the capabilities exposed by the referenced ServerConfig
+	ExtraCapabilities []string `json:"extraCapabilities,omitempty"`
+	// The capabilities this server will advertise
+	// This will override any from the referenced ServerConfig
+	Capabilities []string `json:"capabilities,omitempty"`
+	// Image overrides
+	ImageOverrides *ContainerOverrideSpec `json:"imageOverrides,omitempty"`
+	// PodSpec overrides
+	// Slices such as containers would be appended not overridden
+	PodSpec *PodSpec `json:"podSpec,omitempty"`
+	// Scaling spec
+	ScalingSpec `json:",inline"`
+	// +Optional
+	// If set then when the referenced ServerConfig changes we will NOT update the Server immediately.
+	// Explicit changes to the Server itself will force a reconcile though
+	DisableAutoUpdate bool `json:"disableAutoUpdate,omitempty"`
+}
+
+type ContainerOverrideSpec struct {
+	// The Agent overrides
+	Agent *v1.Container `json:"agent,omitempty"`
+	// The RClone server overrides
+	RClone *v1.Container `json:"rclone,omitempty"`
+}
+
+type ServerDefn struct {
+	// Server config name to match
+	// Required
+	Config string `json:"config"`
+}
+```
+
+## Custom Servers
+
+One can easily utilize a custom image with the existing ServerConfigs. For example, the following defines an MLServer server with a custom image:
+
+
+```yaml
+# samples/servers/custom-mlserver.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Server
+metadata:
+  name: mlserver-134
+spec:
+  serverConfig: mlserver
+  extraCapabilities:
+  - mlserver-1.3.4
+  podSpec:
+    containers:
+    - image: seldonio/mlserver:1.3.4
+      name: mlserver
+```
+
+This server can then be targeted by a particular model by specifying this server name when creating the model, for example:
+
+
+```yaml
+# samples/models/iris-custom-server.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/mlserver/iris"
+  server: mlserver-134
+```
+
+### Server with PVC
+
+One can also create a Server definition to add a persistent volume to your server. This can be used to allow
+models to be loaded directly from the persistent volume.
+
+
+```yaml
+# samples/examples/k8s-pvc/server.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Server
+metadata:
+  name: mlserver-pvc
+spec:
+  serverConfig: mlserver
+  extraCapabilities:
+  - "pvc"
+  podSpec:
+    volumes:
+    - name: models-pvc
+      persistentVolumeClaim:
+        claimName: ml-models-pvc
+    containers:
+    - name: rclone
+      volumeMounts:
+      - name: models-pvc
+        mountPath: /var/models
+```
+
+The server can be targeted by a model whose artifact is on the persistent volume as shown below.
+
+```yaml
+# samples/examples/k8s-pvc/iris.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "/var/models/iris"
+  requirements:
+  - sklearn
+  - pvc
+```
+
+A fully worked example for this can be found [here](../../examples/k8s-pvc.md).
+
+An alternative would be to create your own [ServerConfig](./serverconfig.md) for more complex use cases or you
+want to standardise the Server definition in one place.
diff --git a/docs/kubernetes/resources/serverconfig.md b/docs/kubernetes/resources/serverconfig.md
new file mode 100644
index 0000000000..081d593569
--- /dev/null
+++ b/docs/kubernetes/resources/serverconfig.md
@@ -0,0 +1,17 @@
+# Server Config
+
+{% hint style="info" %}
+This section is for advanced usage where you want to define new types of inference servers.
+{% endhint %}
+
+Server configurations define how to create an inference server. By default one is provided
+for Seldon MLServer and one for NVIDIA Triton Inference Server. Both these servers support
+the V2 inference protocol which is a requirement for all inference servers. They define how
+the Kubernetes ReplicaSet is defined which includes the Seldon Agent reverse proxy as well
+as an Rclone server for downloading artifacts for the server. The Kustomize ServerConfig for
+MlServer is shown below:
+
+```yaml
+# operator/config/serverconfigs/mlserver.yaml
+
+```
diff --git a/docs/kubernetes/scaling.md b/docs/kubernetes/scaling.md
new file mode 100644
index 0000000000..23af1bfb98
--- /dev/null
+++ b/docs/kubernetes/scaling.md
@@ -0,0 +1,71 @@
+# Scaling
+
+## Models
+
+Models can be scaled by setting their replica count, e.g.
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.2.3/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
+  replicas: 3
+```
+
+Currently, the number of replicas will need not to exceed the replicas of the Server the
+model is scheduled to.
+
+## Servers
+
+Servers can be scaled by setting their replica count, e.g.
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Server
+metadata:
+  name: mlserver
+  namespace: seldon
+spec:
+  replicas: 4
+  serverConfig: mlserver
+```
+
+Currently, models scheduled to a server can only scale up to the server replica count.
+
+## Internal Components
+
+Seldon Core v2 runs with several control and dataplane components. The scaling of these resources
+is discussed below:
+
+- Pipeline gateway.
+  - This pipeline gateway handles REST and gRPC synchronous requests to Pipelines. It is stateless
+  and can be scaled based on traffic demand.
+- Model gateway.
+  - This component pulls model requests from Kafka and sends them to inference servers. It can be
+  scaled up to the partition factor of your Kafka topics. At present we set a uniform partition factor
+  for all topics in one installation of Seldon Core V2.
+- Dataflow engine.
+  - The dataflow engine runs KStream topologies to manage Pipelines. It can run as multiple replicas
+  and the scheduler will balance Pipelines to run across it with a consistent hashing load balancer.
+  Each Pipeline is managed up to the partition factor of Kafka (presently hardwired to one).
+- Scheduler.
+  - This manages the control plane operations. It is presently required to be one replica as it maintains
+  internal state within a BadgerDB held on local persistent storage (stateful set in Kubernetes). Performance
+  tests have shown this not to be a bottleneck at present.
+- Kubernetes Controller.
+  - The Kubernetes controller manages resources updates on the cluster which it passes on to the Scheduler.
+  It is by default one replica but has the ability to scale.
+- Envoy
+  - Envoy replicas get their state from the scheduler for routing information and can be scaled as needed.
+
+
+### Future Enhancements
+
+* Allow configuration of partition factor for data plane consistent hashing load balancer.
+* Allow Model gateway and Pipeline gateway to use consistent hashing load balancer.
+* Consider control plane scaling options.
diff --git a/docs/kubernetes/service-meshes/README.md b/docs/kubernetes/service-meshes/README.md
new file mode 100644
index 0000000000..4e4eddfba3
--- /dev/null
+++ b/docs/kubernetes/service-meshes/README.md
@@ -0,0 +1,15 @@
+# Service Meshes
+
+The Seldon models and pipelines are exposed via a single service endpoint in the install
+namespace called `seldon-mesh`. All models, pipelines and experiments can be reached via
+this single Service endpoint by setting appropriate headers on the inference REST/gRPC
+request. By this means Seldon is agnostic to any service mesh you may wish to use in your
+organisation. We provide some example integrations for some example service meshes below
+(alphabetical order):
+
+ * [Ambassador](./ambassador.md)
+ * [Istio](./istio.md)
+ * [Traefik](./traefik.md)
+
+
+We welcome help to extend these to other service meshes.
diff --git a/docs/kubernetes/service-meshes/ambassador.md b/docs/kubernetes/service-meshes/ambassador.md
new file mode 100644
index 0000000000..319490e2f7
--- /dev/null
+++ b/docs/kubernetes/service-meshes/ambassador.md
@@ -0,0 +1,601 @@
+# Ambassador
+
+[Ambassador](https://www.getambassador.io/) provides service mesh and ingress products. Our
+examples here are based on the Emissary ingress.
+
+We will run through some examples as shown in the notebook `service-meshes/ambassador/ambassador.ipynb` in our repo.
+
+## Single Model
+
+* Seldon Iris classifier model
+* Default Ambassador Host and Listener
+* Ambassador Mappings for REST and gRPC endpoints
+
+```yaml
+# service-meshes/ambassador/static/single-model.yaml
+apiVersion: getambassador.io/v3alpha1
+kind: Host
+metadata:
+  name: wildcard
+  namespace: seldon-mesh
+spec:
+  hostname: '*'
+  requestPolicy:
+    insecure:
+      action: Route
+---
+apiVersion: getambassador.io/v3alpha1
+kind: Listener
+metadata:
+  name: emissary-ingress-listener-8080
+  namespace: seldon-mesh
+spec:
+  hostBinding:
+    namespace:
+      from: ALL
+  port: 8080
+  protocol: HTTP
+  securityModel: INSECURE
+---
+apiVersion: getambassador.io/v3alpha1
+kind: Mapping
+metadata:
+  name: iris-grpc
+  namespace: seldon-mesh
+spec:
+  add_request_headers:
+    seldon-model:
+      value: iris
+  grpc: true
+  hostname: '*'
+  prefix: /inference.GRPCInferenceService
+  rewrite: ""
+  service: seldon-mesh:80
+---
+apiVersion: getambassador.io/v3alpha1
+kind: Mapping
+metadata:
+  name: iris-http
+  namespace: seldon-mesh
+spec:
+  add_request_headers:
+    seldon-model:
+      value: iris
+  hostname: '*'
+  prefix: /v2/
+  rewrite: ""
+  service: seldon-mesh:80
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+  namespace: seldon-mesh
+spec:
+  requirements:
+  - sklearn
+  storageUri: gs://seldon-models/mlserver/iris
+```
+
+## Traffic Split
+
+{% hint style="warning" %}
+Traffic splitting does not presently work due to this [issue](https://github.com/emissary-ingress/emissary/issues/4062). We recommend you use a Seldon Experiment instead.
+{% endhint %}
+
+Seldon provides an Experiment resource for service mesh agnostic traffic splitting but if you wish to control this via Ambassador and example is shown below to split traffic between two models.
+
+```yaml
+# service-meshes/ambassador/static/traffic-split.yaml
+apiVersion: getambassador.io/v3alpha1
+kind: Host
+metadata:
+  name: wildcard
+  namespace: seldon-mesh
+spec:
+  hostname: '*'
+  requestPolicy:
+    insecure:
+      action: Route
+---
+apiVersion: getambassador.io/v3alpha1
+kind: Listener
+metadata:
+  name: emissary-ingress-listener-8080
+  namespace: seldon-mesh
+spec:
+  hostBinding:
+    namespace:
+      from: ALL
+  port: 8080
+  protocol: HTTP
+  securityModel: INSECURE
+---
+apiVersion: getambassador.io/v3alpha1
+kind: Mapping
+metadata:
+  name: iris1-grpc
+  namespace: seldon-mesh
+spec:
+  add_request_headers:
+    seldon-model:
+      value: iris1
+  grpc: true
+  hostname: '*'
+  prefix: /inference.GRPCInferenceService
+  rewrite: ""
+  service: seldon-mesh:80
+---
+apiVersion: getambassador.io/v3alpha1
+kind: Mapping
+metadata:
+  name: iris1-http
+  namespace: seldon-mesh
+spec:
+  add_request_headers:
+    seldon-model:
+      value: iris1
+  add_response_headers:
+    seldon_model:
+      value: iris1
+  hostname: '*'
+  prefix: /v2
+  rewrite: ""
+  service: seldon-mesh:80
+---
+apiVersion: getambassador.io/v3alpha1
+kind: Mapping
+metadata:
+  name: iris2-grpc
+  namespace: seldon-mesh
+spec:
+  add_request_headers:
+    seldon-model:
+      value: iris2
+  grpc: true
+  hostname: '*'
+  prefix: /inference.GRPCInferenceService
+  rewrite: ""
+  service: seldon-mesh:80
+  weight: 50
+---
+apiVersion: getambassador.io/v3alpha1
+kind: Mapping
+metadata:
+  name: iris2-http
+  namespace: seldon-mesh
+spec:
+  add_request_headers:
+    seldon-model:
+      value: iris2
+  add_response_headers:
+    seldon_model:
+      value: iris2
+  hostname: '*'
+  prefix: /v2
+  rewrite: ""
+  service: seldon-mesh:80
+  weight: 50
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris1
+  namespace: seldon-mesh
+spec:
+  requirements:
+  - sklearn
+  storageUri: gs://seldon-models/mlserver/iris
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris2
+  namespace: seldon-mesh
+spec:
+  requirements:
+  - sklearn
+  storageUri: gs://seldon-models/mlserver/iris
+```
+
+
+## Ambassador Notebook Example
+
+Assumes
+
+ * You have installed emissary as per their docs
+
+ Tested with
+
+ emissary-ingress-7.3.2 insatlled via [helm](https://www.getambassador.io/docs/emissary/latest/tutorials/getting-started/)
+
+
+```python
+INGRESS_IP=!kubectl get svc emissary-ingress -n emissary -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+INGRESS_IP=INGRESS_IP[0]
+import os
+os.environ['INGRESS_IP'] = INGRESS_IP
+INGRESS_IP
+```
+
+
+
+
+    '172.21.255.1'
+
+
+
+### Ambassador Single Model Example
+
+
+```python
+!kustomize build config/single-model
+```
+
+    apiVersion: getambassador.io/v3alpha1
+    kind: Host
+    metadata:
+      name: wildcard
+      namespace: seldon-mesh
+    spec:
+      hostname: '*'
+      requestPolicy:
+        insecure:
+          action: Route
+    ---
+    apiVersion: getambassador.io/v3alpha1
+    kind: Listener
+    metadata:
+      name: emissary-ingress-listener-8080
+      namespace: seldon-mesh
+    spec:
+      hostBinding:
+        namespace:
+          from: ALL
+      port: 8080
+      protocol: HTTP
+      securityModel: INSECURE
+    ---
+    apiVersion: getambassador.io/v3alpha1
+    kind: Mapping
+    metadata:
+      name: iris-grpc
+      namespace: seldon-mesh
+    spec:
+      add_request_headers:
+        seldon-model:
+          value: iris
+      grpc: true
+      hostname: '*'
+      prefix: /inference.GRPCInferenceService
+      rewrite: ""
+      service: seldon-mesh:80
+    ---
+    apiVersion: getambassador.io/v3alpha1
+    kind: Mapping
+    metadata:
+      name: iris-http
+      namespace: seldon-mesh
+    spec:
+      add_request_headers:
+        seldon-model:
+          value: iris
+      hostname: '*'
+      prefix: /v2/
+      rewrite: ""
+      service: seldon-mesh:80
+    ---
+    apiVersion: mlops.seldon.io/v1alpha1
+    kind: Model
+    metadata:
+      name: iris
+      namespace: seldon-mesh
+    spec:
+      requirements:
+      - sklearn
+      storageUri: gs://seldon-models/mlserver/iris
+
+
+
+```python
+!kustomize build config/single-model | kubectl apply --validate=false -f -
+```
+
+    host.getambassador.io/wildcard created
+    listener.getambassador.io/emissary-ingress-listener-8080 created
+    mapping.getambassador.io/iris-grpc created
+    mapping.getambassador.io/iris-http created
+    model.mlops.seldon.io/iris created
+
+
+
+```python
+!kubectl wait --for condition=ready --timeout=300s model --all -n seldon-mesh
+```
+
+    model.mlops.seldon.io/iris condition met
+
+
+
+```python
+!curl -v http://${INGRESS_IP}/v2/models/iris/infer -H "Content-Type: application/json"\
+        -d '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+    *   Trying 172.21.255.1...
+    * Connected to 172.21.255.1 (172.21.255.1) port 80 (#0)
+    > POST /v2/models/iris/infer HTTP/1.1
+    > Host: 172.21.255.1
+    > User-Agent: curl/7.47.0
+    > Accept: */*
+    > Content-Type: application/json
+    > Content-Length: 94
+    >
+    * upload completely sent off: 94 out of 94 bytes
+    < HTTP/1.1 200 OK
+    < content-length: 196
+    < content-type: application/json
+    < date: Sat, 16 Apr 2022 15:45:43 GMT
+    < server: envoy
+    < x-envoy-upstream-service-time: 792
+    < seldon-route: iris_1
+    <
+    * Connection #0 to host 172.21.255.1 left intact
+    {"model_name":"iris_1","model_version":"1","id":"72ac79f5-b355-4be3-b8c5-2ebedaa39f60","parameters":null,"outputs":[{"name":"predict","shape":[1],"datatype":"INT64","parameters":null,"data":[2]}]}
+
+
+```python
+!grpcurl -d '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' \
+    -plaintext \
+    -import-path ../../apis \
+    -proto ../../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    ${INGRESS_IP}:80 inference.GRPCInferenceService/ModelInfer
+```
+
+    {
+      "modelName": "iris_1",
+      "modelVersion": "1",
+      "outputs": [
+        {
+          "name": "predict",
+          "datatype": "INT64",
+          "shape": [
+            "1"
+          ],
+          "contents": {
+            "int64Contents": [
+              "2"
+            ]
+          }
+        }
+      ]
+    }
+
+
+
+```python
+!kustomize build config/single-model | kubectl delete -f -
+```
+
+    host.getambassador.io "wildcard" deleted
+    listener.getambassador.io "emissary-ingress-listener-8080" deleted
+    mapping.getambassador.io "iris-grpc" deleted
+    mapping.getambassador.io "iris-http" deleted
+    model.mlops.seldon.io "iris" deleted
+
+
+### Traffic Split Two Models
+
+***Currently not working due to this [issue](https://github.com/emissary-ingress/emissary/issues/4062)***
+
+
+
+```python
+!kustomize build config/traffic-split
+```
+
+    apiVersion: getambassador.io/v3alpha1
+    kind: Host
+    metadata:
+      name: wildcard
+      namespace: seldon-mesh
+    spec:
+      hostname: '*'
+      requestPolicy:
+        insecure:
+          action: Route
+    ---
+    apiVersion: getambassador.io/v3alpha1
+    kind: Listener
+    metadata:
+      name: emissary-ingress-listener-8080
+      namespace: seldon-mesh
+    spec:
+      hostBinding:
+        namespace:
+          from: ALL
+      port: 8080
+      protocol: HTTP
+      securityModel: INSECURE
+    ---
+    apiVersion: getambassador.io/v3alpha1
+    kind: Mapping
+    metadata:
+      name: iris1-grpc
+      namespace: seldon-mesh
+    spec:
+      add_request_headers:
+        seldon-model:
+          value: iris1
+      grpc: true
+      hostname: '*'
+      prefix: /inference.GRPCInferenceService
+      rewrite: ""
+      service: seldon-mesh:80
+    ---
+    apiVersion: getambassador.io/v3alpha1
+    kind: Mapping
+    metadata:
+      name: iris1-http
+      namespace: seldon-mesh
+    spec:
+      add_request_headers:
+        seldon-model:
+          value: iris1
+      add_response_headers:
+        seldon_model:
+          value: iris1
+      hostname: '*'
+      prefix: /v2
+      rewrite: ""
+      service: seldon-mesh:80
+    ---
+    apiVersion: getambassador.io/v3alpha1
+    kind: Mapping
+    metadata:
+      name: iris2-grpc
+      namespace: seldon-mesh
+    spec:
+      add_request_headers:
+        seldon-model:
+          value: iris2
+      grpc: true
+      hostname: '*'
+      prefix: /inference.GRPCInferenceService
+      rewrite: ""
+      service: seldon-mesh:80
+      weight: 50
+    ---
+    apiVersion: getambassador.io/v3alpha1
+    kind: Mapping
+    metadata:
+      name: iris2-http
+      namespace: seldon-mesh
+    spec:
+      add_request_headers:
+        seldon-model:
+          value: iris2
+      add_response_headers:
+        seldon_model:
+          value: iris2
+      hostname: '*'
+      prefix: /v2
+      rewrite: ""
+      service: seldon-mesh:80
+      weight: 50
+    ---
+    apiVersion: mlops.seldon.io/v1alpha1
+    kind: Model
+    metadata:
+      name: iris1
+      namespace: seldon-mesh
+    spec:
+      requirements:
+      - sklearn
+      storageUri: gs://seldon-models/mlserver/iris
+    ---
+    apiVersion: mlops.seldon.io/v1alpha1
+    kind: Model
+    metadata:
+      name: iris2
+      namespace: seldon-mesh
+    spec:
+      requirements:
+      - sklearn
+      storageUri: gs://seldon-models/mlserver/iris
+
+
+
+```python
+!kustomize build config/traffic-split | kubectl apply -f -
+```
+
+    host.getambassador.io/wildcard created
+    listener.getambassador.io/emissary-ingress-listener-8080 created
+    mapping.getambassador.io/iris1-grpc created
+    mapping.getambassador.io/iris1-http created
+    mapping.getambassador.io/iris2-grpc created
+    mapping.getambassador.io/iris2-http created
+    model.mlops.seldon.io/iris1 created
+    model.mlops.seldon.io/iris2 created
+
+
+
+```python
+!kubectl wait --for condition=ready --timeout=300s model --all -n seldon-mesh
+```
+
+    model.mlops.seldon.io/iris1 condition met
+    model.mlops.seldon.io/iris2 condition met
+
+
+
+```python
+!curl -v http://${INGRESS_IP}/v2/models/iris/infer -H "Content-Type: application/json" \
+        -d '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+    *   Trying 172.21.255.1...
+    * Connected to 172.21.255.1 (172.21.255.1) port 80 (#0)
+    > POST /v2/models/iris/infer HTTP/1.1
+    > Host: 172.21.255.1
+    > User-Agent: curl/7.47.0
+    > Accept: */*
+    > Content-Type: application/json
+    > Content-Length: 94
+    >
+    * upload completely sent off: 94 out of 94 bytes
+    < HTTP/1.1 200 OK
+    < content-length: 197
+    < content-type: application/json
+    < date: Sat, 16 Apr 2022 15:46:17 GMT
+    < server: envoy
+    < x-envoy-upstream-service-time: 920
+    < seldon-route: iris2_1
+    < seldon_model: iris2
+    <
+    * Connection #0 to host 172.21.255.1 left intact
+    {"model_name":"iris2_1","model_version":"1","id":"ed521c32-cd85-4cb8-90eb-7c896803f271","parameters":null,"outputs":[{"name":"predict","shape":[1],"datatype":"INT64","parameters":null,"data":[2]}]}
+
+
+```python
+!grpcurl -d '{"model_name":"iris1","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' \
+    -plaintext \
+    -import-path ../../apis \
+    -proto ../../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    ${INGRESS_IP}:80 inference.GRPCInferenceService/ModelInfer
+```
+
+    {
+      "modelName": "iris2_1",
+      "modelVersion": "1",
+      "outputs": [
+        {
+          "name": "predict",
+          "datatype": "INT64",
+          "shape": [
+            "1"
+          ],
+          "contents": {
+            "int64Contents": [
+              "2"
+            ]
+          }
+        }
+      ]
+    }
+
+
+
+```python
+!kustomize build config/traffic-split | kubectl delete -f -
+```
+
+    host.getambassador.io "wildcard" deleted
+    listener.getambassador.io "emissary-ingress-listener-8080" deleted
+    mapping.getambassador.io "iris1-grpc" deleted
+    mapping.getambassador.io "iris1-http" deleted
+    mapping.getambassador.io "iris2-grpc" deleted
+    mapping.getambassador.io "iris2-http" deleted
+    model.mlops.seldon.io "iris1" deleted
+    model.mlops.seldon.io "iris2" deleted
diff --git a/docs/kubernetes/service-meshes/istio.md b/docs/kubernetes/service-meshes/istio.md
new file mode 100644
index 0000000000..d1fbd42a15
--- /dev/null
+++ b/docs/kubernetes/service-meshes/istio.md
@@ -0,0 +1,568 @@
+# Istio
+
+[Istio](https://istio.io/) provides a service mesh and ingress solution.
+
+We will run through some examples as shown in the notebook `service-meshes/istio/istio.ipynb` in our repo.
+
+## Single Model
+
+ * A Seldon Iris Model
+ * An istio Gateway
+ * An instio VirtualService to expose REST and gRPC
+
+```yaml
+# service-meshes/istio/static/single-model.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+  namespace: seldon-mesh
+spec:
+  requirements:
+  - sklearn
+  storageUri: gs://seldon-models/mlserver/iris
+---
+apiVersion: networking.istio.io/v1beta1
+kind: Gateway
+metadata:
+  name: seldon-gateway
+  namespace: seldon-mesh
+spec:
+  selector:
+    app: istio-ingressgateway
+    istio: ingressgateway
+  servers:
+  - hosts:
+    - '*'
+    port:
+      name: http
+      number: 80
+      protocol: HTTP
+  - hosts:
+    - '*'
+    port:
+      name: https
+      number: 443
+      protocol: HTTPS
+    tls:
+      mode: SIMPLE
+      privateKey: /etc/istio/ingressgateway-certs/tls.key
+      serverCertificate: /etc/istio/ingressgateway-certs/tls.crt
+---
+apiVersion: networking.istio.io/v1beta1
+kind: VirtualService
+metadata:
+  name: iris-route
+  namespace: seldon-mesh
+spec:
+  gateways:
+  - istio-system/seldon-gateway
+  hosts:
+  - '*'
+  http:
+  - match:
+    - uri:
+        prefix: /v2
+    name: iris-http
+    route:
+    - destination:
+        host: seldon-mesh.seldon-mesh.svc.cluster.local
+      headers:
+        request:
+          set:
+            seldon-model: iris
+  - match:
+    - uri:
+        prefix: /inference.GRPCInferenceService
+    name: iris-grpc
+    route:
+    - destination:
+        host: seldon-mesh.seldon-mesh.svc.cluster.local
+      headers:
+        request:
+          set:
+            seldon-model: iris
+```
+
+## Traffic Split
+
+* Two Iris Models
+* An istio Gateway
+* An istio VirtualService with traffic split
+
+```yaml
+# service-meshes/istio/static/traffic-split.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris1
+  namespace: seldon-mesh
+spec:
+  requirements:
+  - sklearn
+  storageUri: gs://seldon-models/mlserver/iris
+---
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris2
+  namespace: seldon-mesh
+spec:
+  requirements:
+  - sklearn
+  storageUri: gs://seldon-models/mlserver/iris
+---
+apiVersion: networking.istio.io/v1beta1
+kind: Gateway
+metadata:
+  name: seldon-gateway
+  namespace: seldon-mesh
+spec:
+  selector:
+    app: istio-ingressgateway
+    istio: ingressgateway
+  servers:
+  - hosts:
+    - '*'
+    port:
+      name: http
+      number: 80
+      protocol: HTTP
+  - hosts:
+    - '*'
+    port:
+      name: https
+      number: 443
+      protocol: HTTPS
+    tls:
+      mode: SIMPLE
+      privateKey: /etc/istio/ingressgateway-certs/tls.key
+      serverCertificate: /etc/istio/ingressgateway-certs/tls.crt
+---
+apiVersion: networking.istio.io/v1beta1
+kind: VirtualService
+metadata:
+  name: iris-route
+  namespace: seldon-mesh
+spec:
+  gateways:
+  - istio-system/seldon-gateway
+  hosts:
+  - '*'
+  http:
+  - match:
+    - uri:
+        prefix: /v2/models/iris
+    name: iris-http
+    route:
+    - destination:
+        host: seldon-mesh.seldon-mesh.svc.cluster.local
+      headers:
+        request:
+          set:
+            seldon-model: iris1
+      weight: 50
+    - destination:
+        host: seldon-mesh.seldon-mesh.svc.cluster.local
+      headers:
+        request:
+          set:
+            seldon-model: iris2
+      weight: 50
+  - match:
+    - uri:
+        prefix: /inference.GRPCInferenceService
+    name: iris-grpc
+    route:
+    - destination:
+        host: seldon-mesh.seldon-mesh.svc.cluster.local
+      headers:
+        request:
+          set:
+            seldon-model: iris1
+      weight: 50
+    - destination:
+        host: seldon-mesh.seldon-mesh.svc.cluster.local
+      headers:
+        request:
+          set:
+            seldon-model: iris2
+      weight: 50
+```
+
+## Istio Notebook Examples
+
+Assumes
+
+ * You have installed istio as per their docs
+ * You have exposed the ingressgateway as an external loadbalancer
+
+
+tested with:
+
+```
+istioctl version
+1.13.2
+
+istioctl install --set profile=demo -y
+```
+
+
+```python
+INGRESS_IP=!kubectl get svc istio-ingressgateway -n istio-system -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+INGRESS_IP=INGRESS_IP[0]
+import os
+os.environ['INGRESS_IP'] = INGRESS_IP
+INGRESS_IP
+```
+
+
+
+
+    '172.21.255.1'
+
+
+
+### Istio Single Model Example
+
+
+```python
+!kustomize build config/single-model
+```
+
+    apiVersion: mlops.seldon.io/v1alpha1
+    kind: Model
+    metadata:
+      name: iris
+      namespace: seldon-mesh
+    spec:
+      requirements:
+      - sklearn
+      storageUri: gs://seldon-models/mlserver/iris
+    ---
+    apiVersion: networking.istio.io/v1beta1
+    kind: Gateway
+    metadata:
+      name: seldon-gateway
+      namespace: seldon-mesh
+    spec:
+      selector:
+        app: istio-ingressgateway
+        istio: ingressgateway
+      servers:
+      - hosts:
+        - '*'
+        port:
+          name: http
+          number: 80
+          protocol: HTTP
+      - hosts:
+        - '*'
+        port:
+          name: https
+          number: 443
+          protocol: HTTPS
+        tls:
+          mode: SIMPLE
+          privateKey: /etc/istio/ingressgateway-certs/tls.key
+          serverCertificate: /etc/istio/ingressgateway-certs/tls.crt
+    ---
+    apiVersion: networking.istio.io/v1beta1
+    kind: VirtualService
+    metadata:
+      name: iris-route
+      namespace: seldon-mesh
+    spec:
+      gateways:
+      - istio-system/seldon-gateway
+      hosts:
+      - '*'
+      http:
+      - match:
+        - uri:
+            prefix: /v2
+        name: iris-http
+        route:
+        - destination:
+            host: seldon-mesh.seldon-mesh.svc.cluster.local
+          headers:
+            request:
+              set:
+                seldon-model: iris
+      - match:
+        - uri:
+            prefix: /inference.GRPCInferenceService
+        name: iris-grpc
+        route:
+        - destination:
+            host: seldon-mesh.seldon-mesh.svc.cluster.local
+          headers:
+            request:
+              set:
+                seldon-model: iris
+
+
+
+```python
+!kustomize build config/single-model | kubectl apply -f -
+```
+
+    model.mlops.seldon.io/iris unchanged
+    gateway.networking.istio.io/seldon-gateway unchanged
+    virtualservice.networking.istio.io/iris-route configured
+
+
+
+```python
+!kubectl wait --for condition=ready --timeout=300s model --all -n seldon-mesh
+```
+
+    model.mlops.seldon.io/iris condition met
+
+
+
+```python
+!curl -v http://${INGRESS_IP}/v2/models/iris/infer -H "Content-Type: application/json" \
+        -d '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+    *   Trying 172.21.255.1...
+    * Connected to 172.21.255.1 (172.21.255.1) port 80 (#0)
+    > POST /v2/models/iris/infer HTTP/1.1
+    > Host: 172.21.255.1
+    > User-Agent: curl/7.47.0
+    > Accept: */*
+    > Content-Type: application/json
+    > Content-Length: 94
+    >
+    * upload completely sent off: 94 out of 94 bytes
+    < HTTP/1.1 200 OK
+    < content-length: 196
+    < content-type: application/json
+    < date: Sat, 16 Apr 2022 15:34:11 GMT
+    < server: istio-envoy
+    < x-envoy-upstream-service-time: 802
+    < seldon-route: iris_1
+    <
+    * Connection #0 to host 172.21.255.1 left intact
+    {"model_name":"iris_1","model_version":"1","id":"83520c4a-c7f1-4363-9bfd-60c5d8ee2dc5","parameters":null,"outputs":[{"name":"predict","shape":[1],"datatype":"INT64","parameters":null,"data":[2]}]}
+
+
+```python
+!grpcurl -d '{"model_name":"iris","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' \
+    -plaintext \
+    -import-path ../../apis \
+    -proto ../../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    ${INGRESS_IP}:80 inference.GRPCInferenceService/ModelInfer
+```
+
+    {
+      "modelName": "iris_1",
+      "modelVersion": "1",
+      "outputs": [
+        {
+          "name": "predict",
+          "datatype": "INT64",
+          "shape": [
+            "1"
+          ],
+          "contents": {
+            "int64Contents": [
+              "2"
+            ]
+          }
+        }
+      ]
+    }
+
+
+
+```python
+!kustomize build config/single-model | kubectl delete -f -
+```
+
+    model.mlops.seldon.io "iris" deleted
+    gateway.networking.istio.io "seldon-gateway" deleted
+    virtualservice.networking.istio.io "iris-route" deleted
+
+
+### Traffic Split Two Models
+
+
+```python
+!kustomize build config/traffic-split
+```
+
+    apiVersion: mlops.seldon.io/v1alpha1
+    kind: Model
+    metadata:
+      name: iris1
+      namespace: seldon-mesh
+    spec:
+      requirements:
+      - sklearn
+      storageUri: gs://seldon-models/mlserver/iris
+    ---
+    apiVersion: mlops.seldon.io/v1alpha1
+    kind: Model
+    metadata:
+      name: iris2
+      namespace: seldon-mesh
+    spec:
+      requirements:
+      - sklearn
+      storageUri: gs://seldon-models/mlserver/iris
+    ---
+    apiVersion: networking.istio.io/v1beta1
+    kind: Gateway
+    metadata:
+      name: seldon-gateway
+      namespace: seldon-mesh
+    spec:
+      selector:
+        app: istio-ingressgateway
+        istio: ingressgateway
+      servers:
+      - hosts:
+        - '*'
+        port:
+          name: http
+          number: 80
+          protocol: HTTP
+    ---
+    apiVersion: networking.istio.io/v1beta1
+    kind: VirtualService
+    metadata:
+      name: iris-route
+      namespace: seldon-mesh
+    spec:
+      gateways:
+      - seldon-gateway
+      hosts:
+      - '*'
+      http:
+      - match:
+        - uri:
+            prefix: /v2/models/iris
+        name: iris-http
+        route:
+        - destination:
+            host: seldon-mesh.seldon-mesh.svc.cluster.local
+          headers:
+            request:
+              set:
+                seldon-model: iris1
+          weight: 50
+        - destination:
+            host: seldon-mesh.seldon-mesh.svc.cluster.local
+          headers:
+            request:
+              set:
+                seldon-model: iris2
+          weight: 50
+      - match:
+        - uri:
+            prefix: /inference.GRPCInferenceService
+        name: iris-grpc
+        route:
+        - destination:
+            host: seldon-mesh.seldon-mesh.svc.cluster.local
+          headers:
+            request:
+              set:
+                seldon-model: iris1
+          weight: 50
+        - destination:
+            host: seldon-mesh.seldon-mesh.svc.cluster.local
+          headers:
+            request:
+              set:
+                seldon-model: iris2
+          weight: 50
+
+
+
+```python
+!kustomize build config/traffic-split | kubectl apply -f -
+```
+
+    model.mlops.seldon.io/iris1 created
+    model.mlops.seldon.io/iris2 created
+    gateway.networking.istio.io/seldon-gateway created
+    virtualservice.networking.istio.io/iris-route created
+
+
+
+```python
+!kubectl wait --for condition=ready --timeout=300s model --all -n seldon-mesh
+```
+
+    model.mlops.seldon.io/iris1 condition met
+    model.mlops.seldon.io/iris2 condition met
+
+
+
+```python
+!curl -v http://${INGRESS_IP}/v2/models/iris/infer -H "Content-Type: application/json" \
+        -d '{"inputs": [{"name": "predict", "shape": [1, 4], "datatype": "FP32", "data": [[1, 2, 3, 4]]}]}'
+```
+
+    *   Trying 172.21.255.1...
+    * Connected to 172.21.255.1 (172.21.255.1) port 80 (#0)
+    > POST /v2/models/iris/infer HTTP/1.1
+    > Host: 172.21.255.1
+    > User-Agent: curl/7.47.0
+    > Accept: */*
+    > Content-Type: application/json
+    > Content-Length: 94
+    >
+    * upload completely sent off: 94 out of 94 bytes
+    < HTTP/1.1 200 OK
+    < content-length: 197
+    < content-type: application/json
+    < date: Sat, 16 Apr 2022 15:35:01 GMT
+    < server: istio-envoy
+    < x-envoy-upstream-service-time: 801
+    < seldon-route: iris1_1
+    <
+    * Connection #0 to host 172.21.255.1 left intact
+    {"model_name":"iris1_1","model_version":"1","id":"b54e6d8c-d253-4bb9-bb64-02c2ee49e89f","parameters":null,"outputs":[{"name":"predict","shape":[1],"datatype":"INT64","parameters":null,"data":[2]}]}
+
+
+```python
+!grpcurl -d '{"model_name":"iris1","inputs":[{"name":"input","contents":{"fp32_contents":[1,2,3,4]},"datatype":"FP32","shape":[1,4]}]}' \
+    -plaintext \
+    -import-path ../../apis \
+    -proto ../../apis/mlops/v2_dataplane/v2_dataplane.proto \
+    ${INGRESS_IP}:80 inference.GRPCInferenceService/ModelInfer
+```
+
+    {
+      "modelName": "iris1_1",
+      "modelVersion": "1",
+      "outputs": [
+        {
+          "name": "predict",
+          "datatype": "INT64",
+          "shape": [
+            "1"
+          ],
+          "contents": {
+            "int64Contents": [
+              "2"
+            ]
+          }
+        }
+      ]
+    }
+
+
+
+```python
+!kustomize build config/traffic-split | kubectl delete -f -
+```
+
+    model.mlops.seldon.io "iris1" deleted
+    model.mlops.seldon.io "iris2" deleted
+    gateway.networking.istio.io "seldon-gateway" deleted
+    virtualservice.networking.istio.io "iris-route" deleted
diff --git a/docs/kubernetes/service-meshes/traefik.md b/docs/kubernetes/service-meshes/traefik.md
new file mode 100644
index 0000000000..472d6799eb
--- /dev/null
+++ b/docs/kubernetes/service-meshes/traefik.md
@@ -0,0 +1,25 @@
+# Traefik
+
+[Traefik](https://doc.traefik.io/) provides a service mesh and ingress solution.
+
+We will run through some examples as shown in the notebook `service-meshes/traefik/traefik.ipynb`
+
+## Single Model
+
+ * A Seldon Iris Model
+ * Traefik Service
+ * Traefik IngressRoute
+ * Traefik Middleware for adding a header
+
+```{literalinclude} ../../../../../../service-meshes/traefik/static/single-model.yaml 
+:language: yaml
+```
+
+## Traffic Split
+
+```{warning}
+Traffic splitting does not presently work due to this [issue](https://github.com/emissary-ingress/emissary/issues/4062). We recommend you use a Seldon Experiment instead.
+```
+
+```{include} ../../../../../../service-meshes/traefik/README.md
+```
diff --git a/docs/kubernetes/storage-secrets.md b/docs/kubernetes/storage-secrets.md
new file mode 100644
index 0000000000..33d094b3c9
--- /dev/null
+++ b/docs/kubernetes/storage-secrets.md
@@ -0,0 +1,212 @@
+# Storage Secrets
+
+Inference artifacts referenced by Models can be stored in any of the storage backends supported
+by [Rclone](https://rclone.org/). This includes local filesystems, AWS S3, and Google Cloud
+Storage (GCS), among others. Configuration is provided out-of-the-box for public GCS buckets,
+which enables the use of Seldon-provided models like in the below example:
+
+```yaml
+# samples/models/sklearn-iris-gs.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/iris-sklearn"
+  requirements:
+  - sklearn
+  memory: 100Ki
+```
+
+This configuration is provided by the Kubernetes Secret `seldon-rclone-gs-public`.
+It is made available to Servers as a [preloaded secret](#preloaded-secrets).
+You can define and use your own storage configurations in exactly the same way.
+
+## Configuration Format
+
+To define a new storage configuration, you need the following details:
+* Remote name
+* Remote type
+* Provider parameters
+
+A _remote_ is what Rclone calls a storage location.
+The _type_ defines what protocol Rclone should use to talk to this remote.
+A _provider_ is a particular implementation for that storage type.
+Some storage types have multiple providers, such as `s3` having AWS S3 itself, MinIO, Ceph, and so on.
+
+The remote **name** is your choice.
+The prefix you use for models in `spec.storageUri` must be the same as this remote name.
+
+The remote type is one of the values [supported by Rclone](https://rclone.org/docs/).
+For example, for AWS S3 it is `s3` and for Dropbox it is `dropbox`.
+
+The provider parameters depend entirely on the remote _type_ and the specific _provider_ you are using.
+Please check the Rclone documentation for the appropriate provider.
+Note that Rclone docs for storage types call the parameters _properties_ and provide both _config_ and _env var_ formats--you need to use the _config_ format.
+For example, the GCS parameter `--gcs-client-id` described [here](https://rclone.org/googlecloudstorage/#gcs-client-id) should be used as `client_id`.
+
+For reference, this format is described in the [Rclone documentation](https://rclone.org/rc/#config-create).
+Note that we do not support the use of `opts` discussed in that section.
+
+## Kubernetes Secrets
+
+Kubernetes Secrets are used to store Rclone configurations, or _storage secrets_, for use by Servers.
+Each Secret should contain **exactly one** Rclone configuration.
+
+A Server can use storage secrets in one of two ways:
+* It can dynamically load a secret specified by a Model in its `.spec.secretName`
+* It can use global configurations made available via [preloaded secrets](#preloaded-secrets)
+
+The name of a Secret is entirely your choice, as is the name of the data key in that Secret.
+All that matters is that there is a single data key and that its value is in the format described above.
+
+{% hint style="info" %}
+It is possible to use preloaded secrets for some Models and dynamically loaded secrets for others.
+{% endhint %}
+
+### Preloaded Secrets
+
+Rather than Models always having to specify which secret to use, a Server can load storage secrets ahead of time.
+These can then be reused across many Models.
+
+When using a preloaded secret, the Model definition should leave `.spec.secretName` empty.
+The protocol prefix in `.spec.storageUri` still needs to match the remote name specified by a storage secret.
+
+The secrets to preload are named in a centralised ConfigMap called `seldon-agent`.
+This ConfigMap applies to _all_ Servers managed by the same SeldonRuntime.
+By default this ConfigMap only includes `seldon-rclone-gs-public`, but can be extended with your own secrets as shown below:
+
+```yaml
+# samples/auth/agent.yaml
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: seldon-agent
+data:
+  agent.json: |-
+   {
+      "rclone" : {
+          "config_secrets": ["seldon-rclone-gs-public","minio-secret"]
+      },
+   }
+```
+
+The easiest way to change this is to update your SeldonRuntime.
+* If your SeldonRuntime is configured using the `seldon-core-v2-runtime` Helm chart, the corresponding value is `config.agentConfig.rclone.configSecrets`.
+  This can be used as shown below:
+  ```yaml
+  config:
+    agentConfig:
+      rclone:
+        configSecrets:
+          - my-s3
+          - custom-gcs
+          - minio-in-cluster
+  ```
+* Otherwise, if your SeldonRuntime is configured directly, you can add secrets by setting `.spec.config.agentConfig.rclone.config_secrets`.
+  This can be used as follows:
+  ```yaml
+  apiVersion: mlops.seldon.io/v1alpha1
+  kind: SeldonRuntime
+  metadata:
+    name: seldon
+  spec:
+    seldonConfig: default
+    config:
+      agentConfig:
+        rclone:
+          config_secrets:
+            - my-s3
+            - custom-gcs
+            - minio-in-cluster
+    ...
+  ```
+
+## Examples
+
+{% tabs %}
+
+{% tab title="S3 MinIO" %}
+
+Assuming you have installed MinIO in the `minio-system` namespace, a corresponding secret could be:
+
+```yaml
+# samples/auth/minio-secret.yaml
+apiVersion: v1
+kind: Secret
+metadata:
+  name: minio-secret
+  namespace: seldon-mesh
+type: Opaque
+stringData:
+  s3: |
+    type: s3
+    name: s3
+    parameters:
+      provider: minio
+      env_auth: false
+      access_key_id: minioadmin
+      secret_access_key: minioadmin
+      endpoint: http://minio.minio-system:9000
+```
+
+You can then reference this in a Model with `.spec.secretName`:
+
+```yaml
+# samples/models/sklearn-iris-minio.yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: iris
+spec:
+  storageUri: "s3://models/iris"
+  secretName: "minio-secret"
+  requirements:
+  - sklearn
+```
+{% endtab %}
+
+{% tab title="Google Cloud Storage" %}
+
+[GCS](https://rclone.org/googlecloudstorage/) can use [service accounts](https://cloud.google.com/iam/docs/service-accounts) for access.
+You can generate the credentials for a service account using the [gcloud CLI](https://cloud.google.com/sdk/gcloud/reference/iam/service-accounts/keys/create):
+
+```bash
+gcloud iam service-accounts keys create \
+  gcloud-application-credentials.json \
+  --iam-account [SERVICE-ACCOUNT--NAME]@[PROJECT-ID].iam.gserviceaccount.com
+```
+
+The contents of `gcloud-application-credentials.json` can be put into a secret:
+
+```yaml
+apiVersion: v1
+kind: Secret
+metadata:
+  name: gcs-bucket
+type: Opaque
+stringData:
+  gcs: |
+    type: gcs
+    name: gcs
+    parameters:
+      service_account_credentials: '<gcloud-application-credentials.json>'
+```
+
+You can then reference this in a Model with `.spec.secretName`:
+
+```yaml
+apiVersion: mlops.seldon.io/v1alpha1
+kind: Model
+metadata:
+  name: mymodel
+spec:
+  storageUri: "gcs://my-bucket/my-path/my-pytorch-model"
+  secretName: "gcs-bucket"
+  requirements:
+  - pytorch
+```
+
+{% endtab %}
+
+{% endtabs %}
diff --git a/docs/kubernetes/tracing.md b/docs/kubernetes/tracing.md
new file mode 100644
index 0000000000..033571f840
--- /dev/null
+++ b/docs/kubernetes/tracing.md
@@ -0,0 +1,12 @@
+# Tracing
+
+We support Open Telemetry tracing. By default all components will attempt to send OLTP events to
+`seldon-collector.seldon-mesh:4317` which will export to Jaeger at `simplest-collector.seldon-mesh:4317`.
+
+The components can be installed from the `tracing/k8s` folder. In future an Ansible playbook will
+be created. This installs a Open Telemetry collector and a simple Jaeger install with a service that
+can be port forwarded to at `simplest.seldon-mesh:16686`.
+
+An example Jaeger trace is show below:
+
+![trace](../images/jaeger-trace.png)

From c33cd723bb297de8576089e71ef29b09b0eba54b Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Wed, 21 Aug 2024 21:28:45 +1000
Subject: [PATCH 24/32] reformatted cli dir

---
 docs/cli/README.md                   | 192 +++++++++++++++++++++++++++
 docs/cli/seldon.md                   |  18 +++
 docs/cli/seldon_config.md            |  27 ++++
 docs/cli/seldon_config_activate.md   |  22 +++
 docs/cli/seldon_config_add.md        |  22 +++
 docs/cli/seldon_config_deactivate.md |  22 +++
 docs/cli/seldon_config_list.md       |  22 +++
 docs/cli/seldon_config_remove.md     |  22 +++
 docs/cli/seldon_experiment.md        |  26 ++++
 docs/cli/seldon_experiment_list.md   |  24 ++++
 docs/cli/seldon_experiment_start.md  |  27 ++++
 docs/cli/seldon_experiment_status.md |  27 ++++
 docs/cli/seldon_experiment_stop.md   |  26 ++++
 docs/cli/seldon_model.md             |  28 ++++
 docs/cli/seldon_model_infer.md       |  33 +++++
 docs/cli/seldon_model_list.md        |  24 ++++
 docs/cli/seldon_model_load.md        |  27 ++++
 docs/cli/seldon_model_metadata.md    |  24 ++++
 docs/cli/seldon_model_status.md      |  27 ++++
 docs/cli/seldon_model_unload.md      |  26 ++++
 docs/cli/seldon_pipeline.md          |  28 ++++
 docs/cli/seldon_pipeline_infer.md    |  33 +++++
 docs/cli/seldon_pipeline_inspect.md  |  29 ++++
 docs/cli/seldon_pipeline_list.md     |  24 ++++
 docs/cli/seldon_pipeline_load.md     |  27 ++++
 docs/cli/seldon_pipeline_status.md   |  27 ++++
 docs/cli/seldon_pipeline_unload.md   |  26 ++++
 docs/cli/seldon_server.md            |  24 ++++
 docs/cli/seldon_server_list.md       |  24 ++++
 docs/cli/seldon_server_status.md     |  26 ++++
 30 files changed, 934 insertions(+)
 create mode 100644 docs/cli/README.md
 create mode 100644 docs/cli/seldon.md
 create mode 100644 docs/cli/seldon_config.md
 create mode 100644 docs/cli/seldon_config_activate.md
 create mode 100644 docs/cli/seldon_config_add.md
 create mode 100644 docs/cli/seldon_config_deactivate.md
 create mode 100644 docs/cli/seldon_config_list.md
 create mode 100644 docs/cli/seldon_config_remove.md
 create mode 100644 docs/cli/seldon_experiment.md
 create mode 100644 docs/cli/seldon_experiment_list.md
 create mode 100644 docs/cli/seldon_experiment_start.md
 create mode 100644 docs/cli/seldon_experiment_status.md
 create mode 100644 docs/cli/seldon_experiment_stop.md
 create mode 100644 docs/cli/seldon_model.md
 create mode 100644 docs/cli/seldon_model_infer.md
 create mode 100644 docs/cli/seldon_model_list.md
 create mode 100644 docs/cli/seldon_model_load.md
 create mode 100644 docs/cli/seldon_model_metadata.md
 create mode 100644 docs/cli/seldon_model_status.md
 create mode 100644 docs/cli/seldon_model_unload.md
 create mode 100644 docs/cli/seldon_pipeline.md
 create mode 100644 docs/cli/seldon_pipeline_infer.md
 create mode 100644 docs/cli/seldon_pipeline_inspect.md
 create mode 100644 docs/cli/seldon_pipeline_list.md
 create mode 100644 docs/cli/seldon_pipeline_load.md
 create mode 100644 docs/cli/seldon_pipeline_status.md
 create mode 100644 docs/cli/seldon_pipeline_unload.md
 create mode 100644 docs/cli/seldon_server.md
 create mode 100644 docs/cli/seldon_server_list.md
 create mode 100644 docs/cli/seldon_server_status.md

diff --git a/docs/cli/README.md b/docs/cli/README.md
new file mode 100644
index 0000000000..3399ee11b2
--- /dev/null
+++ b/docs/cli/README.md
@@ -0,0 +1,192 @@
+# CLI
+
+Seldon provides a CLI to allow easy management and testing of Model, Experiment, and Pipeline resources.
+
+At present this needs to be built by hand from the operator folder.
+
+```sh
+make build-seldon     # for linux/macOS amd64
+make build-seldon-arm # for macOS ARM
+```
+
+Then place the `bin/seldon` executable in your path.
+* [CLI docs](seldon.md)
+
+
+## Environment Variables and Services
+
+The CLI talks to 3 backend services on default endpoints:
+
+1. The Seldon Core V2 Scheduler: default `0.0.0.0:9004`
+2. The Seldon Core inference endpoint: default `0.0.0.0:9000`
+3. The Seldon Kafka broker: default: `0.0.0.0:9092`
+
+These defaults will be correct when Seldon Core v2 is installed locally as per the docs. For Kubernetes, you will need to change these by defining environment variables.
+
+```go
+const (
+	defaultInferHost     = "0.0.0.0:9000"
+	defaultKafkaHost     = "0.0.0.0:9092"
+	defaultSchedulerHost = "0.0.0.0:9004"
+)
+```
+
+## Kubernetes Usage
+
+### Inference Service
+
+For a default install into the `seldon-mesh` namespace if you have exposed the inference `svc` as a loadbalancer you will find it at:
+
+```sh
+kubectl get svc seldon-mesh -n seldon-mesh -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+```
+
+Use above IP at port 80:
+
+```sh
+export SELDON_INFER_HOST=<ip>:80
+```
+
+### Scheduler Service
+
+For a default install into the `seldon-mesh` namespace if you have exposed the scheduler svc as a loadbalancer you will find it at:
+
+```sh
+kubectl get svc seldon-scheduler -n seldon-mesh -o jsonpath='{.status.loadBalancer.ingress[0].ip}'
+```
+
+Use above IP at port 9004:
+
+```sh
+export SELDON_SCHEDULE_HOST=<ip>:9004
+```
+
+### Kafka Broker
+
+The Kafka broker will depend on how you have installed Kafka into your Kubernetes cluster. Find the broker IP and use:
+
+```sh
+export SELDON_KAFKA_BROKER=<ip>:<port>
+```
+
+## Config file
+
+You can create a config file to manage connections to running seldon core v2 installs. The settings will override any environment variable settings.
+
+The definition is shown below:
+
+```go
+type SeldonCLIConfig struct {
+	Dataplane    *Dataplane    `json:"dataplane,omitempty"`
+	Controlplane *ControlPlane `json:"controlplane,omitempty"`
+	Kafka        *KafkaConfig  `json:"kafka,omitempty"`
+}
+
+type Dataplane struct {
+	InferHost     string `json:"inferHost,omitempty"`
+	Tls           bool   `json:"tls,omitempty"`
+	SkipSSLVerify bool   `json:"skipSSLVerify,omitempty"`
+	KeyPath       string `json:"keyPath,omitempty"`
+	CrtPath       string `json:"crtPath,omitempty"`
+	CaPath        string `json:"caPath,omitempty"`
+}
+
+type ControlPlane struct {
+	SchedulerHost string `json:"schedulerHost,omitempty"`
+	Tls           bool   `json:"tls,omitempty"`
+	KeyPath       string `json:"keyPath,omitempty"`
+	CrtPath       string `json:"crtPath,omitempty"`
+	CaPath        string `json:"caPath,omitempty"`
+}
+
+const (
+	KafkaConfigProtocolSSL          = "ssl"
+	KafkaConfigProtocolSASLSSL      = "sasl_ssl"
+	KafkaConfigProtocolSASLPlaintxt = "sasl_plaintxt"
+)
+
+type KafkaConfig struct {
+	Bootstrap    string `json:"bootstrap,omitempty"`
+	Namespace    string `json:"namespace,omitempty"`
+	Protocol     string `json:"protocol,omitempty"`
+	KeyPath      string `json:"keyPath,omitempty"`
+	CrtPath      string `json:"crtPath,omitempty"`
+	CaPath       string `json:"caPath,omitempty"`
+	SaslUsername string `json:"saslUsername,omitempty"`
+	SaslPassword string `json:"saslPassword,omitempty"`
+	TopicPrefix  string `json:"topicPrefix,omitempty"`
+}
+```
+
+An example below shows an example where we connect via TLS to the Seldon scheduler using our scheduler client certificate:
+
+```json
+{
+  "controlplane": {
+  	"schedulerHost": "seldon-scheduler.svc:9044",
+  	"tls": true,
+  	"keyPath": "/home/certs/seldon-scheduler-client/tls.key",
+  	"crtPath": "/home/certs/seldon-scheduler-client/tls.crt",
+  	"caPath": "/home/certs/seldon-scheduler-client/ca.crt"
+  }
+}
+```
+
+To manage config files and activate them you can use the CLI command `seldon config` which has subcommands to list, add, remove, activate and decative configs.
+
+For example:
+
+```
+$ seldon config list
+config		path						active
+------		----						------
+kind-sasl	/home/work/seldon/cli/config-sasl.json		*
+
+$ seldon config deactivate kind-sasl
+
+$ seldon config list
+config		path						active
+------		----						------
+kind-sasl	/home/work/seldon/cli/config-sasl.json
+
+$ seldon config add gcp-scv2 ~/seldon/cli/gcp.json
+
+$ seldon config list
+config		path						active
+------		----						------
+gcp-scv2	/home/work/seldon/cli/gcp.json
+kind-sasl	/home/work/seldon/cli/config-sasl.json
+
+$ seldon config activate gcp-scv2
+
+$ seldon config list
+config		path						active
+------		----						------
+gcp-scv2	/home/work/seldon/cli/gcp.json	    		*
+kind-sasl	/home/work/seldon/cli/config-sasl.json
+
+$ seldon config list kind-sasl
+{
+  "controlplane": {
+    "schedulerHost": "172.19.255.2:9004"
+  },
+  "kafka": {
+    "bootstrap": "172.19.255.3:9093",
+    "caPath": "/home/work/gcp/scv2/certs/seldon-cluster-ca-cert/ca.crt"
+  }
+}
+```
+
+## TLS Certificates for Local Use
+
+For running with Kubernetes TLS connections on the control and/or data plane, certificates will need to be downloaded locally. We provide an example script which will download certificates from a Kubernetes secret and store them in a folder. It can be found in `hack/download-k8s-certs.sh` and takes 2 or 3 arguments:
+
+```sh
+./download-k8s-certs.sh <namespace> <secret> [<folder>]
+```
+
+e.g.:
+
+```sh
+./download-k8s-certs.sh seldon-mesh seldon-scheduler-client
+```
diff --git a/docs/cli/seldon.md b/docs/cli/seldon.md
new file mode 100644
index 0000000000..3cecdf8d54
--- /dev/null
+++ b/docs/cli/seldon.md
@@ -0,0 +1,18 @@
+## seldon
+
+
+
+### Options
+
+```
+  -h, --help   help for seldon
+```
+
+### SEE ALSO
+
+* [seldon config](seldon_config.md)	 - manage configs
+* [seldon experiment](seldon_experiment.md)	 - manage experiments
+* [seldon model](seldon_model.md)	 - manage models
+* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
+* [seldon server](seldon_server.md)	 - manage servers
+
diff --git a/docs/cli/seldon_config.md b/docs/cli/seldon_config.md
new file mode 100644
index 0000000000..2501fd7c66
--- /dev/null
+++ b/docs/cli/seldon_config.md
@@ -0,0 +1,27 @@
+## seldon config
+
+manage configs
+
+### Synopsis
+
+Manage and activate configuration files for the CLI
+
+```
+seldon config <subcomand> [flags]
+```
+
+### Options
+
+```
+  -h, --help   help for config
+```
+
+### SEE ALSO
+
+* [seldon](seldon.md)	 - 
+* [seldon config activate](seldon_config_activate.md)	 - activate config
+* [seldon config add](seldon_config_add.md)	 - add config
+* [seldon config deactivate](seldon_config_deactivate.md)	 - deactivate config
+* [seldon config list](seldon_config_list.md)	 - list configs
+* [seldon config remove](seldon_config_remove.md)	 - remove config
+
diff --git a/docs/cli/seldon_config_activate.md b/docs/cli/seldon_config_activate.md
new file mode 100644
index 0000000000..fd911fe2cb
--- /dev/null
+++ b/docs/cli/seldon_config_activate.md
@@ -0,0 +1,22 @@
+## seldon config activate
+
+activate config
+
+### Synopsis
+
+activate config
+
+```
+seldon config activate [flags]
+```
+
+### Options
+
+```
+  -h, --help   help for activate
+```
+
+### SEE ALSO
+
+* [seldon config](seldon_config.md)	 - manage configs
+
diff --git a/docs/cli/seldon_config_add.md b/docs/cli/seldon_config_add.md
new file mode 100644
index 0000000000..3ea6f7036b
--- /dev/null
+++ b/docs/cli/seldon_config_add.md
@@ -0,0 +1,22 @@
+## seldon config add
+
+add config
+
+### Synopsis
+
+add config
+
+```
+seldon config add [flags]
+```
+
+### Options
+
+```
+  -h, --help   help for add
+```
+
+### SEE ALSO
+
+* [seldon config](seldon_config.md)	 - manage configs
+
diff --git a/docs/cli/seldon_config_deactivate.md b/docs/cli/seldon_config_deactivate.md
new file mode 100644
index 0000000000..55639bd4ae
--- /dev/null
+++ b/docs/cli/seldon_config_deactivate.md
@@ -0,0 +1,22 @@
+## seldon config deactivate
+
+deactivate config
+
+### Synopsis
+
+deactivate config
+
+```
+seldon config deactivate [flags]
+```
+
+### Options
+
+```
+  -h, --help   help for deactivate
+```
+
+### SEE ALSO
+
+* [seldon config](seldon_config.md)	 - manage configs
+
diff --git a/docs/cli/seldon_config_list.md b/docs/cli/seldon_config_list.md
new file mode 100644
index 0000000000..30b17fe285
--- /dev/null
+++ b/docs/cli/seldon_config_list.md
@@ -0,0 +1,22 @@
+## seldon config list
+
+list configs
+
+### Synopsis
+
+list configs
+
+```
+seldon config list [flags]
+```
+
+### Options
+
+```
+  -h, --help   help for list
+```
+
+### SEE ALSO
+
+* [seldon config](seldon_config.md)	 - manage configs
+
diff --git a/docs/cli/seldon_config_remove.md b/docs/cli/seldon_config_remove.md
new file mode 100644
index 0000000000..0ada8f04b8
--- /dev/null
+++ b/docs/cli/seldon_config_remove.md
@@ -0,0 +1,22 @@
+## seldon config remove
+
+remove config
+
+### Synopsis
+
+remove config
+
+```
+seldon config remove [flags]
+```
+
+### Options
+
+```
+  -h, --help   help for remove
+```
+
+### SEE ALSO
+
+* [seldon config](seldon_config.md)	 - manage configs
+
diff --git a/docs/cli/seldon_experiment.md b/docs/cli/seldon_experiment.md
new file mode 100644
index 0000000000..d56201c12e
--- /dev/null
+++ b/docs/cli/seldon_experiment.md
@@ -0,0 +1,26 @@
+## seldon experiment
+
+manage experiments
+
+### Synopsis
+
+experiments allow you to test models by splitting traffic between candidates.
+
+```
+seldon experiment <subcomand> [flags]
+```
+
+### Options
+
+```
+  -h, --help   help for experiment
+```
+
+### SEE ALSO
+
+* [seldon](seldon.md)	 - 
+* [seldon experiment list](seldon_experiment_list.md)	 - get list of experiments
+* [seldon experiment start](seldon_experiment_start.md)	 - start an experiment
+* [seldon experiment status](seldon_experiment_status.md)	 - get status for experiment
+* [seldon experiment stop](seldon_experiment_stop.md)	 - stop an experiment
+
diff --git a/docs/cli/seldon_experiment_list.md b/docs/cli/seldon_experiment_list.md
new file mode 100644
index 0000000000..bed319a9fe
--- /dev/null
+++ b/docs/cli/seldon_experiment_list.md
@@ -0,0 +1,24 @@
+## seldon experiment list
+
+get list of experiments
+
+### Synopsis
+
+get list of experiments and whether they are active
+
+```
+seldon experiment list [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for list
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+```
+
+### SEE ALSO
+
+* [seldon experiment](seldon_experiment.md)	 - manage experiments
+
diff --git a/docs/cli/seldon_experiment_start.md b/docs/cli/seldon_experiment_start.md
new file mode 100644
index 0000000000..2cb1eedd93
--- /dev/null
+++ b/docs/cli/seldon_experiment_start.md
@@ -0,0 +1,27 @@
+## seldon experiment start
+
+start an experiment
+
+### Synopsis
+
+start an experiment
+
+```
+seldon experiment start [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -f, --file-path string        experiment manifest file (YAML)
+  -h, --help                    help for start
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+```
+
+### SEE ALSO
+
+* [seldon experiment](seldon_experiment.md)	 - manage experiments
+
diff --git a/docs/cli/seldon_experiment_status.md b/docs/cli/seldon_experiment_status.md
new file mode 100644
index 0000000000..c58e58575c
--- /dev/null
+++ b/docs/cli/seldon_experiment_status.md
@@ -0,0 +1,27 @@
+## seldon experiment status
+
+get status for experiment
+
+### Synopsis
+
+get status for experiment
+
+```
+seldon experiment status <experimentName> [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for status
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+  -w, --wait                    wait for experiment to be active
+```
+
+### SEE ALSO
+
+* [seldon experiment](seldon_experiment.md)	 - manage experiments
+
diff --git a/docs/cli/seldon_experiment_stop.md b/docs/cli/seldon_experiment_stop.md
new file mode 100644
index 0000000000..6b0110221c
--- /dev/null
+++ b/docs/cli/seldon_experiment_stop.md
@@ -0,0 +1,26 @@
+## seldon experiment stop
+
+stop an experiment
+
+### Synopsis
+
+stop an experiment
+
+```
+seldon experiment stop <experimentName> [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for stop
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+```
+
+### SEE ALSO
+
+* [seldon experiment](seldon_experiment.md)	 - manage experiments
+
diff --git a/docs/cli/seldon_model.md b/docs/cli/seldon_model.md
new file mode 100644
index 0000000000..669c4ea560
--- /dev/null
+++ b/docs/cli/seldon_model.md
@@ -0,0 +1,28 @@
+## seldon model
+
+manage models
+
+### Synopsis
+
+load and unload and get status for models
+
+```
+seldon model <subcomand> [flags]
+```
+
+### Options
+
+```
+  -h, --help   help for model
+```
+
+### SEE ALSO
+
+* [seldon](seldon.md)	 - 
+* [seldon model infer](seldon_model_infer.md)	 - run inference on a model
+* [seldon model list](seldon_model_list.md)	 - get list of models
+* [seldon model load](seldon_model_load.md)	 - load a model
+* [seldon model metadata](seldon_model_metadata.md)	 - get model metadata
+* [seldon model status](seldon_model_status.md)	 - get status for model
+* [seldon model unload](seldon_model_unload.md)	 - unload a model
+
diff --git a/docs/cli/seldon_model_infer.md b/docs/cli/seldon_model_infer.md
new file mode 100644
index 0000000000..345546d522
--- /dev/null
+++ b/docs/cli/seldon_model_infer.md
@@ -0,0 +1,33 @@
+## seldon model infer
+
+run inference on a model
+
+### Synopsis
+
+call a model with a given input and get a prediction
+
+```
+seldon model infer <modelName> (data) [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -f, --file-path string        inference payload file
+      --header stringArray      add a header, e.g. key=value; use the flag multiple times to add more than one header
+  -h, --help                    help for infer
+      --inference-host string   seldon inference host (default "0.0.0.0:9000")
+      --inference-mode string   inference mode (rest or grpc) (default "rest")
+  -i, --iterations int          how many times to run inference (default 1)
+  -t, --seconds int             number of secs to run inference
+      --show-headers            show request and response headers
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+  -s, --sticky-session          use sticky session from last inference (only works with experiments)
+```
+
+### SEE ALSO
+
+* [seldon model](seldon_model.md)	 - manage models
+
diff --git a/docs/cli/seldon_model_list.md b/docs/cli/seldon_model_list.md
new file mode 100644
index 0000000000..fce34d5e92
--- /dev/null
+++ b/docs/cli/seldon_model_list.md
@@ -0,0 +1,24 @@
+## seldon model list
+
+get list of models
+
+### Synopsis
+
+get the list of all models with their status
+
+```
+seldon model list [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for list
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+```
+
+### SEE ALSO
+
+* [seldon model](seldon_model.md)	 - manage models
+
diff --git a/docs/cli/seldon_model_load.md b/docs/cli/seldon_model_load.md
new file mode 100644
index 0000000000..6a2beaeea5
--- /dev/null
+++ b/docs/cli/seldon_model_load.md
@@ -0,0 +1,27 @@
+## seldon model load
+
+load a model
+
+### Synopsis
+
+load a model
+
+```
+seldon model load [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -f, --file-path string        model manifest file (YAML)
+  -h, --help                    help for load
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+```
+
+### SEE ALSO
+
+* [seldon model](seldon_model.md)	 - manage models
+
diff --git a/docs/cli/seldon_model_metadata.md b/docs/cli/seldon_model_metadata.md
new file mode 100644
index 0000000000..b96688089e
--- /dev/null
+++ b/docs/cli/seldon_model_metadata.md
@@ -0,0 +1,24 @@
+## seldon model metadata
+
+get model metadata
+
+### Synopsis
+
+get model metadata
+
+```
+seldon model metadata <modelName> [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for metadata
+      --inference-host string   seldon inference host (default "0.0.0.0:9000")
+```
+
+### SEE ALSO
+
+* [seldon model](seldon_model.md)	 - manage models
+
diff --git a/docs/cli/seldon_model_status.md b/docs/cli/seldon_model_status.md
new file mode 100644
index 0000000000..abf47c1342
--- /dev/null
+++ b/docs/cli/seldon_model_status.md
@@ -0,0 +1,27 @@
+## seldon model status
+
+get status for model
+
+### Synopsis
+
+get the status for a model
+
+```
+seldon model status <modelName> [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for status
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+  -w, --wait string             model wait condition
+```
+
+### SEE ALSO
+
+* [seldon model](seldon_model.md)	 - manage models
+
diff --git a/docs/cli/seldon_model_unload.md b/docs/cli/seldon_model_unload.md
new file mode 100644
index 0000000000..0add751f8a
--- /dev/null
+++ b/docs/cli/seldon_model_unload.md
@@ -0,0 +1,26 @@
+## seldon model unload
+
+unload a model
+
+### Synopsis
+
+unload a model
+
+```
+seldon model unload <modelName> [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for unload
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+```
+
+### SEE ALSO
+
+* [seldon model](seldon_model.md)	 - manage models
+
diff --git a/docs/cli/seldon_pipeline.md b/docs/cli/seldon_pipeline.md
new file mode 100644
index 0000000000..cbe5c96125
--- /dev/null
+++ b/docs/cli/seldon_pipeline.md
@@ -0,0 +1,28 @@
+## seldon pipeline
+
+manage pipelines
+
+### Synopsis
+
+pipelines allow you to join models together into inference graphs.
+
+```
+seldon pipeline <subcomand> [flags]
+```
+
+### Options
+
+```
+  -h, --help   help for pipeline
+```
+
+### SEE ALSO
+
+* [seldon](seldon.md)	 - 
+* [seldon pipeline infer](seldon_pipeline_infer.md)	 - run inference on a pipeline
+* [seldon pipeline inspect](seldon_pipeline_inspect.md)	 - inspect data in a pipeline
+* [seldon pipeline list](seldon_pipeline_list.md)	 - list pipelines
+* [seldon pipeline load](seldon_pipeline_load.md)	 - load a pipeline
+* [seldon pipeline status](seldon_pipeline_status.md)	 - status of a pipeline
+* [seldon pipeline unload](seldon_pipeline_unload.md)	 - unload a pipeline
+
diff --git a/docs/cli/seldon_pipeline_infer.md b/docs/cli/seldon_pipeline_infer.md
new file mode 100644
index 0000000000..cb5fdb784a
--- /dev/null
+++ b/docs/cli/seldon_pipeline_infer.md
@@ -0,0 +1,33 @@
+## seldon pipeline infer
+
+run inference on a pipeline
+
+### Synopsis
+
+call a pipeline with a given input and get a prediction
+
+```
+seldon pipeline infer <pipelineName> (data) [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -f, --file-path string        inference payload file
+      --header stringArray      add a header, e.g. key=value; use the flag multiple times to add more than one header
+  -h, --help                    help for infer
+      --inference-host string   seldon inference host (default "0.0.0.0:9000")
+      --inference-mode string   inference mode (rest or grpc) (default "rest")
+  -i, --iterations int          how many times to run inference (default 1)
+  -t, --seconds int             number of secs to run inference
+      --show-headers            show request and response headers
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+  -s, --sticky-session          use sticky session from last inference (only works with experiments)
+```
+
+### SEE ALSO
+
+* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
+
diff --git a/docs/cli/seldon_pipeline_inspect.md b/docs/cli/seldon_pipeline_inspect.md
new file mode 100644
index 0000000000..19b5c0cf38
--- /dev/null
+++ b/docs/cli/seldon_pipeline_inspect.md
@@ -0,0 +1,29 @@
+## seldon pipeline inspect
+
+inspect data in a pipeline
+
+### Synopsis
+
+inspect data in a pipeline. Specify as pipelineName or pipelineName.(inputs|outputs) or pipelineName.stepName or pipelineName.stepName.(inputs|outputs) or pipelineName.stepName.(inputs|outputs).tensorName
+
+```
+seldon pipeline inspect <expression> [flags]
+```
+
+### Options
+
+```
+      --format string           inspect output format: raw or json. Default raw (default "raw")
+  -h, --help                    help for inspect
+      --kafka-broker string     kafka broker (default "0.0.0.0:9092")
+      --namespace string        Kubernetes namespace. Default default (default "default")
+      --offset int              message offset to start reading from, i.e. default 1 is the last message only (default 1)
+      --request-id string       request id to show, if not specified will be all messages in offset range
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+      --verbose                 display more details, such as headers
+```
+
+### SEE ALSO
+
+* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
+
diff --git a/docs/cli/seldon_pipeline_list.md b/docs/cli/seldon_pipeline_list.md
new file mode 100644
index 0000000000..be709b810c
--- /dev/null
+++ b/docs/cli/seldon_pipeline_list.md
@@ -0,0 +1,24 @@
+## seldon pipeline list
+
+list pipelines
+
+### Synopsis
+
+list pipelines
+
+```
+seldon pipeline list [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for list
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+```
+
+### SEE ALSO
+
+* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
+
diff --git a/docs/cli/seldon_pipeline_load.md b/docs/cli/seldon_pipeline_load.md
new file mode 100644
index 0000000000..c6d6b49398
--- /dev/null
+++ b/docs/cli/seldon_pipeline_load.md
@@ -0,0 +1,27 @@
+## seldon pipeline load
+
+load a pipeline
+
+### Synopsis
+
+load a pipeline
+
+```
+seldon pipeline load [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -f, --file-path string        pipeline manifest file (YAML)
+  -h, --help                    help for load
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+```
+
+### SEE ALSO
+
+* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
+
diff --git a/docs/cli/seldon_pipeline_status.md b/docs/cli/seldon_pipeline_status.md
new file mode 100644
index 0000000000..0d861f255b
--- /dev/null
+++ b/docs/cli/seldon_pipeline_status.md
@@ -0,0 +1,27 @@
+## seldon pipeline status
+
+status of a pipeline
+
+### Synopsis
+
+status of a pipeline
+
+```
+seldon pipeline status <pipelineName> [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for status
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+  -w, --wait string             pipeline wait condition
+```
+
+### SEE ALSO
+
+* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
+
diff --git a/docs/cli/seldon_pipeline_unload.md b/docs/cli/seldon_pipeline_unload.md
new file mode 100644
index 0000000000..1ab0c2d6a4
--- /dev/null
+++ b/docs/cli/seldon_pipeline_unload.md
@@ -0,0 +1,26 @@
+## seldon pipeline unload
+
+unload a pipeline
+
+### Synopsis
+
+unload a pipeline
+
+```
+seldon pipeline unload <pipelineName> [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for unload
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+```
+
+### SEE ALSO
+
+* [seldon pipeline](seldon_pipeline.md)	 - manage pipelines
+
diff --git a/docs/cli/seldon_server.md b/docs/cli/seldon_server.md
new file mode 100644
index 0000000000..9ef3407200
--- /dev/null
+++ b/docs/cli/seldon_server.md
@@ -0,0 +1,24 @@
+## seldon server
+
+manage servers
+
+### Synopsis
+
+get status for servers
+
+```
+seldon server <subcomand> [flags]
+```
+
+### Options
+
+```
+  -h, --help   help for server
+```
+
+### SEE ALSO
+
+* [seldon](seldon.md)	 - 
+* [seldon server list](seldon_server_list.md)	 - get list of servers
+* [seldon server status](seldon_server_status.md)	 - get status for server
+
diff --git a/docs/cli/seldon_server_list.md b/docs/cli/seldon_server_list.md
new file mode 100644
index 0000000000..a335dd12cc
--- /dev/null
+++ b/docs/cli/seldon_server_list.md
@@ -0,0 +1,24 @@
+## seldon server list
+
+get list of servers
+
+### Synopsis
+
+get the available servers, their replicas and loaded models
+
+```
+seldon server list [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for list
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+```
+
+### SEE ALSO
+
+* [seldon server](seldon_server.md)	 - manage servers
+
diff --git a/docs/cli/seldon_server_status.md b/docs/cli/seldon_server_status.md
new file mode 100644
index 0000000000..aae6e5204a
--- /dev/null
+++ b/docs/cli/seldon_server_status.md
@@ -0,0 +1,26 @@
+## seldon server status
+
+get status for server
+
+### Synopsis
+
+get the status for a server
+
+```
+seldon server status [flags]
+```
+
+### Options
+
+```
+      --authority string        authority (HTTP/2) or virtual host (HTTP/1)
+  -h, --help                    help for status
+      --scheduler-host string   seldon scheduler host (default "0.0.0.0:9004")
+  -r, --show-request            show request
+  -o, --show-response           show response (default true)
+```
+
+### SEE ALSO
+
+* [seldon server](seldon_server.md)	 - manage servers
+

From caeed09350b03e70dec10f86e5ea47a006d3c3fa Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Wed, 21 Aug 2024 21:29:09 +1000
Subject: [PATCH 25/32] typos and links fixed

---
 docs/upgrading.md | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)
 create mode 100644 docs/upgrading.md

diff --git a/docs/upgrading.md b/docs/upgrading.md
new file mode 100644
index 0000000000..f43d2d50d7
--- /dev/null
+++ b/docs/upgrading.md
@@ -0,0 +1,37 @@
+# Upgrading
+
+## Upgrading from 2.6 - 2.7
+
+All pods provisioned through the operator i.e. `SeldonRuntime` and `Server` resources now have the
+label `app.kubernetes.io/name` for identifying the pods.
+
+Previously, the labelling has been inconsistent across different versions of Seldon Core v2, with
+mixture of `app` and `app.kubernetes.io/name` used.
+
+If using the Prometheus operator ("Kube Prometheus"), please apply the v2.7.0 manifests for Core v2
+according to the [metrics documentation](../kubernetes/metrics.md).
+
+Note that these manifests need to be adjusted to discover metrics endpoints based on the existing setup.
+
+If previous pod monitors had `namespaceSelector` fields set, these should be copied over and applied
+to the new manifests.
+
+If namespaces do not matter, cluster-wide metrics endpoint discovery can be setup by modifying the
+`namespaceSelector` field in the pod monitors:
+
+```yaml
+spec:
+  namespaceSelector:
+    any: true
+```
+
+## Upgrading from 2.5 - 2.6
+
+Release 2.6 brings with it new custom resources `SeldonConfig` and `SeldonRuntime`, which provide
+a new way to install Seldon Core V2 in Kubernetes. Upgrading in the same namespace will cause downtime
+while the pods are being recreated. Alternatively  users can have an external service mesh or other
+means to be used over multiple namespaces to bring up the system in a new namespace and redeploy models
+before switch traffic between them.
+
+If the new 2.6 charts are used to upgrade in an existing namespace models will eventually be redeloyed
+but there will be service downtime as the core components are redeployed.

From 86773dfb5a828e9089da1602009d111063d27e18 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Wed, 21 Aug 2024 21:29:19 +1000
Subject: [PATCH 26/32] typos and links fixed

---
 docs/apis/scheduler.md | 423 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 423 insertions(+)
 create mode 100644 docs/apis/scheduler.md

diff --git a/docs/apis/scheduler.md b/docs/apis/scheduler.md
new file mode 100644
index 0000000000..21d017a9ea
--- /dev/null
+++ b/docs/apis/scheduler.md
@@ -0,0 +1,423 @@
+# Scheduler API
+
+The Seldon scheduler API provides a gRPC service to allow Models, Servers, Experiments, and Pipelines to be managed. In Kubernetes the manager deployed by Seldon translates Kubernetes custom resource definitions into calls to the Seldon Scheduler.
+
+In non-Kubernetes environments users of Seldon could create a client to directly control Seldon resources using this API.
+
+## Proto Definition
+
+```proto
+syntax = "proto3";
+
+package seldon.mlops.scheduler;
+
+option go_package = "github.com/seldonio/seldon-core/apis/go/v2/mlops/scheduler";
+
+import "google/protobuf/timestamp.proto";
+
+// [START Messages]
+
+message LoadModelRequest {
+  Model model = 1;
+}
+
+message Model {
+  MetaData meta = 1;
+  ModelSpec modelSpec = 2;
+  DeploymentSpec deploymentSpec = 3;
+  StreamSpec streamSpec = 4;
+}
+
+message MetaData {
+  string name = 1;
+  optional string kind = 2;
+  optional string version = 3;
+  optional KubernetesMeta kubernetesMeta = 4; // Kubernetes specific config
+}
+
+message DeploymentSpec {
+  uint32 replicas = 1;
+  uint32 minReplicas = 2;
+  uint32 maxReplicas = 3;
+  bool logPayloads = 4;
+}
+
+/* ModelDetails
+*/
+message ModelSpec {
+  string uri = 1; // storage uri from where to download the artifacts
+  optional uint32 artifactVersion = 2; // Optional v2 version folder to select
+  optional StorageConfig storageConfig = 3; // Storage auth configuration
+  repeated string requirements = 4; // list of capabilities the server must satisfy to run this model
+  optional uint64 memoryBytes = 5; // Requested memory
+  optional string server = 6; // the particular model server to load the model. If unspecified will be chosen.
+  optional ExplainerSpec explainer = 7; // optional black box explainer details
+  repeated ParameterSpec parameters = 8; // parameters to load with model
+}
+
+message ParameterSpec {
+  string name = 1;
+  string value = 2;
+}
+
+message ExplainerSpec {
+  string type = 1;
+  // 1 of semantic either model or pipeline reference
+  optional string modelRef = 2;
+  optional string pipelineRef = 3;
+}
+
+message KubernetesMeta {
+  string namespace = 1;
+  int64 generation = 2;
+}
+
+message StreamSpec {
+  string inputTopic = 2;
+  string outputTopic = 3;
+}
+
+message StorageConfig {
+  oneof config {
+    string storageSecretName = 1;
+    string storageRcloneConfig = 2;
+  }
+}
+
+message LoadModelResponse {
+
+}
+
+/* ModelReference represents a unique model
+*/
+message ModelReference {
+  string name = 1;
+  optional uint32 version = 2;
+}
+
+message UnloadModelRequest {
+  ModelReference model = 1;
+  optional KubernetesMeta kubernetesMeta = 2;
+}
+
+message UnloadModelResponse {
+}
+
+/* ModelStatusResponse provides the current assignment of the model onto a server
+*/
+message ModelStatusResponse {
+  string modelName = 1;
+  repeated ModelVersionStatus versions = 2;
+  bool deleted = 3;
+}
+
+message ModelVersionStatus {
+  uint32 version = 2;
+  string serverName = 3;
+  optional KubernetesMeta kubernetesMeta = 4;
+  map<int32,ModelReplicaStatus> modelReplicaState = 5;
+  ModelStatus state = 6;
+  optional Model modelDefn = 7;
+}
+
+message ModelStatus {
+  enum ModelState {
+      ModelStateUnknown = 0;
+      ModelProgressing = 1;
+      ModelAvailable = 2;
+      ModelFailed = 3;
+      ModelTerminating = 4;
+      ModelTerminated = 5;
+      ModelTerminateFailed = 6;
+      ScheduleFailed = 7;
+  }
+  ModelState state = 1;
+  string reason = 2;
+  uint32 availableReplicas = 3;
+  uint32 unavailableReplicas = 4;
+  google.protobuf.Timestamp lastChangeTimestamp = 5;
+}
+
+message ModelReplicaStatus {
+  enum ModelReplicaState {
+      ModelReplicaStateUnknown = 0;
+      LoadRequested = 1;
+      Loading = 2;
+      Loaded = 3;
+      LoadFailed = 4;
+      UnloadRequested = 5;
+      Unloading = 6;
+      Unloaded = 7;
+      UnloadFailed = 8;
+      Available = 9;
+      LoadedUnavailable = 10;
+      UnloadEnvoyRequested = 11;
+      Draining = 12;
+  }
+  ModelReplicaState state = 1;
+  string reason = 2;
+  google.protobuf.Timestamp lastChangeTimestamp = 3;
+}
+
+message ServerStatusRequest {
+  string subscriberName = 1;
+  optional string name = 2; // Leave empty for all servers
+}
+
+/* ServerStatusResponse provides details of current server status
+*/
+message ServerStatusResponse {
+  string serverName = 1;
+  repeated ServerReplicaResources resources = 2;
+  int32 expectedReplicas = 3;
+  int32 availableReplicas = 4;
+  int32 numLoadedModelReplicas = 5;
+  optional KubernetesMeta kubernetesMeta = 6;
+}
+
+message ServerReplicaResources {
+  uint32 replicaIdx = 1;
+  uint64 totalMemoryBytes = 2;
+  uint64 availableMemoryBytes = 3;
+  int32 numLoadedModels = 4;
+  uint32 overCommitPercentage = 5;
+}
+
+message ModelSubscriptionRequest {
+  string subscriberName = 1; //Name of the subscription caller
+}
+
+message ModelStatusRequest {
+  string subscriberName = 1;
+  optional ModelReference model = 2;
+  bool allVersions = 3;
+}
+
+message ServerNotifyRequest {
+  string name = 1;
+  int32 expectedReplicas = 2;
+  bool shared = 3;
+  optional KubernetesMeta kubernetesMeta = 4;
+}
+
+message ServerNotifyResponse {
+
+}
+
+message ServerSubscriptionRequest {
+  string subscriberName = 1; //Name of the subscription caller
+}
+
+// Experiments
+
+message StartExperimentRequest {
+  Experiment experiment = 1;
+}
+
+enum ResourceType {
+  MODEL = 0;
+  PIPELINE = 1;
+}
+
+message Experiment {
+  string name = 1;
+  optional string default = 2;
+  repeated ExperimentCandidate candidates = 3;
+  optional ExperimentMirror mirror = 4;
+  optional ExperimentConfig config = 5;
+  optional KubernetesMeta kubernetesMeta = 6;
+  ResourceType resourceType = 7;
+}
+
+message ExperimentConfig {
+  bool stickySessions = 1;
+}
+
+message ExperimentCandidate {
+  string name = 1;
+  uint32 weight = 2;
+}
+
+message ExperimentMirror {
+  string name = 1;
+  uint32 percent = 2;
+}
+
+message StartExperimentResponse {
+}
+
+message StopExperimentRequest {
+  string name = 1;
+}
+
+message StopExperimentResponse {
+}
+
+message ExperimentSubscriptionRequest {
+  string subscriberName = 1; //Name of the subscription caller
+}
+
+message ExperimentStatusResponse {
+  string experimentName = 1;
+  bool active = 2;
+  bool candidatesReady = 3;
+  bool mirrorReady = 4;
+  string statusDescription = 5;
+  optional KubernetesMeta kubernetesMeta = 6;
+}
+
+message LoadPipelineRequest {
+  Pipeline pipeline = 1;
+}
+
+message ExperimentStatusRequest {
+  string subscriberName = 1;
+  optional string name = 2; // Leave empty for all experiments
+}
+
+message Pipeline {
+  string name = 1;
+  string uid = 2;
+  uint32 version = 3;
+  repeated PipelineStep steps = 4;
+  optional PipelineOutput output = 5;
+  optional KubernetesMeta kubernetesMeta = 6;
+  optional PipelineInput input = 7;
+}
+
+message PipelineStep {
+  enum JoinOp {
+    INNER = 0;
+    OUTER = 1;
+    ANY = 2;
+  }
+  string name = 1;
+  repeated string inputs = 2;
+  optional uint32 joinWindowMs = 3; // Join window millisecs, some nonzero default (TBD)
+  map<string,string> tensorMap = 4; // optional map of tensor name mappings
+  JoinOp inputsJoin = 5;
+  repeated string triggers = 6;
+  JoinOp triggersJoin = 7;
+  Batch batch = 8;
+}
+
+message Batch {
+  optional uint32 size = 1;
+  optional uint32 windowMs = 2;
+}
+
+message PipelineInput {
+  enum JoinOp {
+    INNER = 0;
+    OUTER = 1;
+    ANY = 2;
+  }
+  repeated string externalInputs = 1;
+  repeated string externalTriggers = 2;
+  optional uint32 joinWindowMs = 3; // Join window millisecs for output, default 0
+  JoinOp joinType = 4;
+  JoinOp triggersJoin = 5;
+  map<string,string> tensorMap = 6; // optional map of tensor name mappings
+}
+
+message PipelineOutput {
+  enum JoinOp {
+    INNER = 0;
+    OUTER = 1;
+    ANY = 2;
+  }
+  repeated string steps = 1;
+  uint32 joinWindowMs = 2; // Join window millisecs for output, default 0
+  JoinOp stepsJoin = 3;
+  map<string,string> tensorMap = 4; // optional map of tensor name mappings
+}
+
+message LoadPipelineResponse {
+
+}
+
+message UnloadPipelineRequest {
+  string name = 1;
+}
+
+message UnloadPipelineResponse {
+
+}
+
+message PipelineStatusRequest {
+  string subscriberName = 1;
+  optional string name = 2; // Leave empty for all pipelines
+  bool allVersions = 3;
+}
+
+message PipelineSubscriptionRequest {
+  string subscriberName = 1; //Name of the subscription caller
+}
+
+message PipelineStatusResponse {
+  string pipelineName = 1;
+  repeated PipelineWithState versions = 2;
+}
+
+message PipelineWithState {
+  Pipeline pipeline = 1;
+  PipelineVersionState state = 2;
+}
+
+message PipelineVersionState {
+  enum PipelineStatus {
+    PipelineStatusUnknown = 0;
+    PipelineCreate = 1;
+    PipelineCreating = 2;
+    PipelineReady = 3;
+    PipelineFailed = 4;
+    PipelineTerminate = 5;
+    PipelineTerminating = 6;
+    PipelineTerminated = 7;
+  }
+  uint32 pipelineVersion = 1;
+  PipelineStatus status = 2;
+  string reason = 3;
+  google.protobuf.Timestamp lastChangeTimestamp = 4;
+  bool modelsReady = 5;
+}
+
+message SchedulerStatusRequest {
+  string subscriberName = 1;
+}
+
+message SchedulerStatusResponse {
+  string applicationVersion = 1;
+}
+
+// [END Messages]
+
+
+// [START Services]
+
+service Scheduler {
+  rpc ServerNotify(ServerNotifyRequest) returns (ServerNotifyResponse) {};
+
+  rpc LoadModel(LoadModelRequest) returns (LoadModelResponse) {};
+  rpc UnloadModel(UnloadModelRequest) returns (UnloadModelResponse) {};
+
+  rpc LoadPipeline(LoadPipelineRequest) returns (LoadPipelineResponse) {};
+  rpc UnloadPipeline(UnloadPipelineRequest) returns (UnloadPipelineResponse) {};
+
+  rpc StartExperiment(StartExperimentRequest) returns (StartExperimentResponse) {};
+  rpc StopExperiment(StopExperimentRequest) returns (StopExperimentResponse) {};
+
+  rpc ServerStatus(ServerStatusRequest) returns (stream ServerStatusResponse) {}
+  rpc ModelStatus(ModelStatusRequest) returns (stream ModelStatusResponse) {}
+  rpc PipelineStatus(PipelineStatusRequest) returns (stream PipelineStatusResponse) {};
+  rpc ExperimentStatus(ExperimentStatusRequest) returns (stream ExperimentStatusResponse) {};
+  rpc SchedulerStatus(SchedulerStatusRequest) returns (SchedulerStatusResponse) {};
+
+  rpc SubscribeServerStatus(ServerSubscriptionRequest) returns (stream ServerStatusResponse) {};
+  rpc SubscribeModelStatus(ModelSubscriptionRequest) returns (stream ModelStatusResponse) {};
+  rpc SubscribeExperimentStatus(ExperimentSubscriptionRequest) returns (stream ExperimentStatusResponse) {};
+  rpc SubscribePipelineStatus(PipelineSubscriptionRequest) returns (stream PipelineStatusResponse) {};
+}
+
+// [END Services]
+```

From 87694034723dfbe94805958b60391d1a39e6890c Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Wed, 21 Aug 2024 21:29:39 +1000
Subject: [PATCH 27/32] tentative structured added to the root of the docs

---
 docs/SUMMARY.md | 80 ++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 79 insertions(+), 1 deletion(-)

diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index 7e37473075..2c2c534f08 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -25,13 +25,91 @@
 * [Architecture](architecture/README.md)
   * [DataFlow](architecture/dataflow.md)
 * [Examples](examples/README.md)
+  * [Local examples](examples/local-examples.md)
+  * [Kubernetes examples](examples/k8s-examples.md)
+  * [Huggingface models](examples/huggingface.md)
+  * [Model zoo](examples/model-zoo.md)
+  * [Artifact versions](examples/multi-version.md)
+  * [Pipeline examples](examples/pipeline-examples.md)
+  * [Pipeline to pipeline examples](examples/pipeline-to-pipeline.md)
+  * [Explainer examples](examples/explainer-examples.md)
+  * [Custom Servers](examples/custom-servers.md)
+  * [Local experiments](examples/local-experiments.md)
+  * [Experiment version examples](examples/experiment-versions.md)
+  * [Inference examples](examples/inference.md)
+  * [Tritonclient examples](examples/tritonclient-examples.md)
+  * [Batch Inference examples (kubernetes)](examples/batch-examples-k8s.md)
+  * [Batch Inference examples (local)](examples/batch-examples-local.md)
+  * [Checking Pipeline readiness](examples/pipeline-ready-and-metadata.md)
+  * [Multi-Namespace Kubernetes](examples/k8s-clusterwide.md)
+  * [Huggingface speech to sentiment with explanations pipeline](examples/speech-to-sentiment.md)
+  * [Production image classifier with drift and outlier monitoring](examples/cifar10.md)
+  * [Production income classifier with drift, outlier and explanations](examples/income.md)
+  * [Conditional pipeline with pandas query model](examples/pandasquery.md)
+  * [Kubernetes Server with PVC](examples/k8s-pvc.md)
+  * [Local Overcommit](examples/k8s-pvc.md)
+* [Kubernetes](kubernetes/README.md)
+  * [scaling](kubernetes/scaling.md)
+  * [autoscaling](kubernetes/autoscaling.md)
+  * [tracing](kubernetes/tracing.md)
+  * [storage-secrets](kubernetes/storage-secrets.md)
+  * [kafka](kubernetes/kafka.md)
+  * [metrics](kubernetes/metrics.md)
+  * [Resources](kubernetes/resources/README.md)
+    * [Model](kubernetes/resources/model.md)
+    * [Experiment](kubernetes/resources/experiment.md)
+    * [Pipeline](kubernetes/resources/pipeline.md)
+    * [Server](kubernetes/resources/server.md)
+    * [Server Config](kubernetes/resources/serverconfig.md)
+    * [Server Runtime](kubernetes/resources/seldonruntime.md)
+    * [Seldon Config](kubernetes/resources/seldonconfig.md)
+  * [Service Meshes](kubernetes/service-meshes/README.md)
+    * [Ambassador](kubernetes/service-meshes/ambassador.md)
+    * [Istio](kubernetes/service-meshes/istio.md)
+    * [Traefik](kubernetes/service-meshes/traefik.md)
 * [Models](models/README.md)
   * [Multi-Model Serving](models/mms.md)
   * [Inference Artifacts](models/inference-artifacts.md)
   * [rClone](models/rclone.md)
   * [Parameterized Models](models/parameterized-models/README.md)
   * [Pandas Query](models/parameterized-models/pandasquery.md)
-
+* [Metrics](metrics/README.md)
+  * [Usage](metrics/usage.md)
+  * [Operational](metrics/operational.md)
+  * [Local Metrics](metrics/local-metrics-test.md)
+* [Development](development/README.md)
+  * [License](development/licenses.md)
+  * [Release](development/release.md)
+* [CLI](cli/README.md)
+  * [Seldon](cli/seldon.md)
+  * [Config](cli/seldon_config.md)
+    * [Config Activate](cli/seldon_config_activate.md)
+    * [Config Deactivate](cli/seldon_config_deactivate.md)
+    * [Config Add](cli/seldon_config_add.md)
+    * [Config List](cli/seldon_config_list.md)
+    * [Config Remove](cli/seldon_config_remove.md)
+  * [Experiment](cli/seldon_experiment.md)
+    * [Experiment Start](cli/seldon_experiment_start.md)
+    * [Experiment Status](cli/seldon_experiment_status.md)
+    * [Experiment List](cli/seldon_experiment_list.md)
+    * [Experiment Stop](cli/seldon_experiment_stop.md)
+  * [Model](cli/seldon_model.md)
+    * [Model Status](cli/seldon_model_status.md)
+    * [Model Load](cli/seldon_model_load.md)
+    * [Model List](cli/seldon_model_list.md)
+    * [Model Infer](cli/seldon_model_infer.md)
+    * [Model Metadata](cli/seldon_model_metadata.md)
+    * [Model Unload](cli/seldon_model_unload.md)
+  * [Pipeline](cli/seldon_pipeline.md)
+    * [Pipeline Load](cli/seldon_pipeline_load.md)
+    * [Pipeline Status](cli/seldon_pipeline_status.md)
+    * [Pipeline List](cli/seldon_pipeline_list.md)
+    * [Pipeline Inspect](cli/seldon_pipeline_inspect.md)
+    * [Pipeline Infer](cli/seldon_pipeline_infer.md)
+    * [Pipeline Unload](cli/seldon_pipeline_unload.md)
+  * [Server](cli/seldon_server.md)
+    * [Server List](cli/seldon_server_list.md)
+    * [Server Status](cli/seldon_server_status.md)
 * [Pipelines](pipelines.md)
 * [Experiments](experiments.md)
 * [Servers](servers.md)

From cad0b7e64020533a2a5d9eb572dc571e5c1b814f Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Sat, 24 Aug 2024 10:11:12 +1000
Subject: [PATCH 28/32] fixed names in kubernetes section

---
 docs/SUMMARY.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index 2c2c534f08..6c60a22de4 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -49,12 +49,12 @@
   * [Kubernetes Server with PVC](examples/k8s-pvc.md)
   * [Local Overcommit](examples/k8s-pvc.md)
 * [Kubernetes](kubernetes/README.md)
-  * [scaling](kubernetes/scaling.md)
-  * [autoscaling](kubernetes/autoscaling.md)
-  * [tracing](kubernetes/tracing.md)
-  * [storage-secrets](kubernetes/storage-secrets.md)
-  * [kafka](kubernetes/kafka.md)
-  * [metrics](kubernetes/metrics.md)
+  * [Scaling](kubernetes/scaling.md)
+  * [Autoscaling](kubernetes/autoscaling.md)
+  * [Tracing](kubernetes/tracing.md)
+  * [Storage Secrets](kubernetes/storage-secrets.md)
+  * [Kafka](kubernetes/kafka.md)
+  * [Metrics](kubernetes/metrics.md)
   * [Resources](kubernetes/resources/README.md)
     * [Model](kubernetes/resources/model.md)
     * [Experiment](kubernetes/resources/experiment.md)

From 7e34f9d28f1e865d47d6a6f411bf9b16bd5de30a Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramon.perez@seldon.io>
Date: Tue, 27 Aug 2024 03:58:08 +0000
Subject: [PATCH 29/32] GITBOOK-1: changed hard-coded reference to
 scheduler.proto

---
 docs/{about.md => README.md} |   0
 docs/SUMMARY.md              |  60 ++---
 docs/apis/scheduler.md       | 418 +----------------------------------
 3 files changed, 33 insertions(+), 445 deletions(-)
 rename docs/{about.md => README.md} (100%)

diff --git a/docs/about.md b/docs/README.md
similarity index 100%
rename from docs/about.md
rename to docs/README.md
diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index 2c2c534f08..d9b510d6cc 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -1,4 +1,6 @@
-* [Home](about.md)
+# Table of contents
+
+* [Home](README.md)
 * [Getting Started](getting-started/README.md)
   * [Docker Installation](getting-started/docker-installation.md)
   * [Kubernetes Installation](getting-started/kubernetes-installation/README.md)
@@ -82,34 +84,34 @@
   * [Release](development/release.md)
 * [CLI](cli/README.md)
   * [Seldon](cli/seldon.md)
-  * [Config](cli/seldon_config.md)
-    * [Config Activate](cli/seldon_config_activate.md)
-    * [Config Deactivate](cli/seldon_config_deactivate.md)
-    * [Config Add](cli/seldon_config_add.md)
-    * [Config List](cli/seldon_config_list.md)
-    * [Config Remove](cli/seldon_config_remove.md)
-  * [Experiment](cli/seldon_experiment.md)
-    * [Experiment Start](cli/seldon_experiment_start.md)
-    * [Experiment Status](cli/seldon_experiment_status.md)
-    * [Experiment List](cli/seldon_experiment_list.md)
-    * [Experiment Stop](cli/seldon_experiment_stop.md)
-  * [Model](cli/seldon_model.md)
-    * [Model Status](cli/seldon_model_status.md)
-    * [Model Load](cli/seldon_model_load.md)
-    * [Model List](cli/seldon_model_list.md)
-    * [Model Infer](cli/seldon_model_infer.md)
-    * [Model Metadata](cli/seldon_model_metadata.md)
-    * [Model Unload](cli/seldon_model_unload.md)
-  * [Pipeline](cli/seldon_pipeline.md)
-    * [Pipeline Load](cli/seldon_pipeline_load.md)
-    * [Pipeline Status](cli/seldon_pipeline_status.md)
-    * [Pipeline List](cli/seldon_pipeline_list.md)
-    * [Pipeline Inspect](cli/seldon_pipeline_inspect.md)
-    * [Pipeline Infer](cli/seldon_pipeline_infer.md)
-    * [Pipeline Unload](cli/seldon_pipeline_unload.md)
-  * [Server](cli/seldon_server.md)
-    * [Server List](cli/seldon_server_list.md)
-    * [Server Status](cli/seldon_server_status.md)
+  * [Config](cli/seldon\_config.md)
+    * [Config Activate](cli/seldon\_config\_activate.md)
+    * [Config Deactivate](cli/seldon\_config\_deactivate.md)
+    * [Config Add](cli/seldon\_config\_add.md)
+    * [Config List](cli/seldon\_config\_list.md)
+    * [Config Remove](cli/seldon\_config\_remove.md)
+  * [Experiment](cli/seldon\_experiment.md)
+    * [Experiment Start](cli/seldon\_experiment\_start.md)
+    * [Experiment Status](cli/seldon\_experiment\_status.md)
+    * [Experiment List](cli/seldon\_experiment\_list.md)
+    * [Experiment Stop](cli/seldon\_experiment\_stop.md)
+  * [Model](cli/seldon\_model.md)
+    * [Model Status](cli/seldon\_model\_status.md)
+    * [Model Load](cli/seldon\_model\_load.md)
+    * [Model List](cli/seldon\_model\_list.md)
+    * [Model Infer](cli/seldon\_model\_infer.md)
+    * [Model Metadata](cli/seldon\_model\_metadata.md)
+    * [Model Unload](cli/seldon\_model\_unload.md)
+  * [Pipeline](cli/seldon\_pipeline.md)
+    * [Pipeline Load](cli/seldon\_pipeline\_load.md)
+    * [Pipeline Status](cli/seldon\_pipeline\_status.md)
+    * [Pipeline List](cli/seldon\_pipeline\_list.md)
+    * [Pipeline Inspect](cli/seldon\_pipeline\_inspect.md)
+    * [Pipeline Infer](cli/seldon\_pipeline\_infer.md)
+    * [Pipeline Unload](cli/seldon\_pipeline\_unload.md)
+  * [Server](cli/seldon\_server.md)
+    * [Server List](cli/seldon\_server\_list.md)
+    * [Server Status](cli/seldon\_server\_status.md)
 * [Pipelines](pipelines.md)
 * [Experiments](experiments.md)
 * [Servers](servers.md)
diff --git a/docs/apis/scheduler.md b/docs/apis/scheduler.md
index 21d017a9ea..96e2b9ad85 100644
--- a/docs/apis/scheduler.md
+++ b/docs/apis/scheduler.md
@@ -1,4 +1,4 @@
-# Scheduler API
+# Scheduler
 
 The Seldon scheduler API provides a gRPC service to allow Models, Servers, Experiments, and Pipelines to be managed. In Kubernetes the manager deployed by Seldon translates Kubernetes custom resource definitions into calls to the Seldon Scheduler.
 
@@ -6,418 +6,4 @@ In non-Kubernetes environments users of Seldon could create a client to directly
 
 ## Proto Definition
 
-```proto
-syntax = "proto3";
-
-package seldon.mlops.scheduler;
-
-option go_package = "github.com/seldonio/seldon-core/apis/go/v2/mlops/scheduler";
-
-import "google/protobuf/timestamp.proto";
-
-// [START Messages]
-
-message LoadModelRequest {
-  Model model = 1;
-}
-
-message Model {
-  MetaData meta = 1;
-  ModelSpec modelSpec = 2;
-  DeploymentSpec deploymentSpec = 3;
-  StreamSpec streamSpec = 4;
-}
-
-message MetaData {
-  string name = 1;
-  optional string kind = 2;
-  optional string version = 3;
-  optional KubernetesMeta kubernetesMeta = 4; // Kubernetes specific config
-}
-
-message DeploymentSpec {
-  uint32 replicas = 1;
-  uint32 minReplicas = 2;
-  uint32 maxReplicas = 3;
-  bool logPayloads = 4;
-}
-
-/* ModelDetails
-*/
-message ModelSpec {
-  string uri = 1; // storage uri from where to download the artifacts
-  optional uint32 artifactVersion = 2; // Optional v2 version folder to select
-  optional StorageConfig storageConfig = 3; // Storage auth configuration
-  repeated string requirements = 4; // list of capabilities the server must satisfy to run this model
-  optional uint64 memoryBytes = 5; // Requested memory
-  optional string server = 6; // the particular model server to load the model. If unspecified will be chosen.
-  optional ExplainerSpec explainer = 7; // optional black box explainer details
-  repeated ParameterSpec parameters = 8; // parameters to load with model
-}
-
-message ParameterSpec {
-  string name = 1;
-  string value = 2;
-}
-
-message ExplainerSpec {
-  string type = 1;
-  // 1 of semantic either model or pipeline reference
-  optional string modelRef = 2;
-  optional string pipelineRef = 3;
-}
-
-message KubernetesMeta {
-  string namespace = 1;
-  int64 generation = 2;
-}
-
-message StreamSpec {
-  string inputTopic = 2;
-  string outputTopic = 3;
-}
-
-message StorageConfig {
-  oneof config {
-    string storageSecretName = 1;
-    string storageRcloneConfig = 2;
-  }
-}
-
-message LoadModelResponse {
-
-}
-
-/* ModelReference represents a unique model
-*/
-message ModelReference {
-  string name = 1;
-  optional uint32 version = 2;
-}
-
-message UnloadModelRequest {
-  ModelReference model = 1;
-  optional KubernetesMeta kubernetesMeta = 2;
-}
-
-message UnloadModelResponse {
-}
-
-/* ModelStatusResponse provides the current assignment of the model onto a server
-*/
-message ModelStatusResponse {
-  string modelName = 1;
-  repeated ModelVersionStatus versions = 2;
-  bool deleted = 3;
-}
-
-message ModelVersionStatus {
-  uint32 version = 2;
-  string serverName = 3;
-  optional KubernetesMeta kubernetesMeta = 4;
-  map<int32,ModelReplicaStatus> modelReplicaState = 5;
-  ModelStatus state = 6;
-  optional Model modelDefn = 7;
-}
-
-message ModelStatus {
-  enum ModelState {
-      ModelStateUnknown = 0;
-      ModelProgressing = 1;
-      ModelAvailable = 2;
-      ModelFailed = 3;
-      ModelTerminating = 4;
-      ModelTerminated = 5;
-      ModelTerminateFailed = 6;
-      ScheduleFailed = 7;
-  }
-  ModelState state = 1;
-  string reason = 2;
-  uint32 availableReplicas = 3;
-  uint32 unavailableReplicas = 4;
-  google.protobuf.Timestamp lastChangeTimestamp = 5;
-}
-
-message ModelReplicaStatus {
-  enum ModelReplicaState {
-      ModelReplicaStateUnknown = 0;
-      LoadRequested = 1;
-      Loading = 2;
-      Loaded = 3;
-      LoadFailed = 4;
-      UnloadRequested = 5;
-      Unloading = 6;
-      Unloaded = 7;
-      UnloadFailed = 8;
-      Available = 9;
-      LoadedUnavailable = 10;
-      UnloadEnvoyRequested = 11;
-      Draining = 12;
-  }
-  ModelReplicaState state = 1;
-  string reason = 2;
-  google.protobuf.Timestamp lastChangeTimestamp = 3;
-}
-
-message ServerStatusRequest {
-  string subscriberName = 1;
-  optional string name = 2; // Leave empty for all servers
-}
-
-/* ServerStatusResponse provides details of current server status
-*/
-message ServerStatusResponse {
-  string serverName = 1;
-  repeated ServerReplicaResources resources = 2;
-  int32 expectedReplicas = 3;
-  int32 availableReplicas = 4;
-  int32 numLoadedModelReplicas = 5;
-  optional KubernetesMeta kubernetesMeta = 6;
-}
-
-message ServerReplicaResources {
-  uint32 replicaIdx = 1;
-  uint64 totalMemoryBytes = 2;
-  uint64 availableMemoryBytes = 3;
-  int32 numLoadedModels = 4;
-  uint32 overCommitPercentage = 5;
-}
-
-message ModelSubscriptionRequest {
-  string subscriberName = 1; //Name of the subscription caller
-}
-
-message ModelStatusRequest {
-  string subscriberName = 1;
-  optional ModelReference model = 2;
-  bool allVersions = 3;
-}
-
-message ServerNotifyRequest {
-  string name = 1;
-  int32 expectedReplicas = 2;
-  bool shared = 3;
-  optional KubernetesMeta kubernetesMeta = 4;
-}
-
-message ServerNotifyResponse {
-
-}
-
-message ServerSubscriptionRequest {
-  string subscriberName = 1; //Name of the subscription caller
-}
-
-// Experiments
-
-message StartExperimentRequest {
-  Experiment experiment = 1;
-}
-
-enum ResourceType {
-  MODEL = 0;
-  PIPELINE = 1;
-}
-
-message Experiment {
-  string name = 1;
-  optional string default = 2;
-  repeated ExperimentCandidate candidates = 3;
-  optional ExperimentMirror mirror = 4;
-  optional ExperimentConfig config = 5;
-  optional KubernetesMeta kubernetesMeta = 6;
-  ResourceType resourceType = 7;
-}
-
-message ExperimentConfig {
-  bool stickySessions = 1;
-}
-
-message ExperimentCandidate {
-  string name = 1;
-  uint32 weight = 2;
-}
-
-message ExperimentMirror {
-  string name = 1;
-  uint32 percent = 2;
-}
-
-message StartExperimentResponse {
-}
-
-message StopExperimentRequest {
-  string name = 1;
-}
-
-message StopExperimentResponse {
-}
-
-message ExperimentSubscriptionRequest {
-  string subscriberName = 1; //Name of the subscription caller
-}
-
-message ExperimentStatusResponse {
-  string experimentName = 1;
-  bool active = 2;
-  bool candidatesReady = 3;
-  bool mirrorReady = 4;
-  string statusDescription = 5;
-  optional KubernetesMeta kubernetesMeta = 6;
-}
-
-message LoadPipelineRequest {
-  Pipeline pipeline = 1;
-}
-
-message ExperimentStatusRequest {
-  string subscriberName = 1;
-  optional string name = 2; // Leave empty for all experiments
-}
-
-message Pipeline {
-  string name = 1;
-  string uid = 2;
-  uint32 version = 3;
-  repeated PipelineStep steps = 4;
-  optional PipelineOutput output = 5;
-  optional KubernetesMeta kubernetesMeta = 6;
-  optional PipelineInput input = 7;
-}
-
-message PipelineStep {
-  enum JoinOp {
-    INNER = 0;
-    OUTER = 1;
-    ANY = 2;
-  }
-  string name = 1;
-  repeated string inputs = 2;
-  optional uint32 joinWindowMs = 3; // Join window millisecs, some nonzero default (TBD)
-  map<string,string> tensorMap = 4; // optional map of tensor name mappings
-  JoinOp inputsJoin = 5;
-  repeated string triggers = 6;
-  JoinOp triggersJoin = 7;
-  Batch batch = 8;
-}
-
-message Batch {
-  optional uint32 size = 1;
-  optional uint32 windowMs = 2;
-}
-
-message PipelineInput {
-  enum JoinOp {
-    INNER = 0;
-    OUTER = 1;
-    ANY = 2;
-  }
-  repeated string externalInputs = 1;
-  repeated string externalTriggers = 2;
-  optional uint32 joinWindowMs = 3; // Join window millisecs for output, default 0
-  JoinOp joinType = 4;
-  JoinOp triggersJoin = 5;
-  map<string,string> tensorMap = 6; // optional map of tensor name mappings
-}
-
-message PipelineOutput {
-  enum JoinOp {
-    INNER = 0;
-    OUTER = 1;
-    ANY = 2;
-  }
-  repeated string steps = 1;
-  uint32 joinWindowMs = 2; // Join window millisecs for output, default 0
-  JoinOp stepsJoin = 3;
-  map<string,string> tensorMap = 4; // optional map of tensor name mappings
-}
-
-message LoadPipelineResponse {
-
-}
-
-message UnloadPipelineRequest {
-  string name = 1;
-}
-
-message UnloadPipelineResponse {
-
-}
-
-message PipelineStatusRequest {
-  string subscriberName = 1;
-  optional string name = 2; // Leave empty for all pipelines
-  bool allVersions = 3;
-}
-
-message PipelineSubscriptionRequest {
-  string subscriberName = 1; //Name of the subscription caller
-}
-
-message PipelineStatusResponse {
-  string pipelineName = 1;
-  repeated PipelineWithState versions = 2;
-}
-
-message PipelineWithState {
-  Pipeline pipeline = 1;
-  PipelineVersionState state = 2;
-}
-
-message PipelineVersionState {
-  enum PipelineStatus {
-    PipelineStatusUnknown = 0;
-    PipelineCreate = 1;
-    PipelineCreating = 2;
-    PipelineReady = 3;
-    PipelineFailed = 4;
-    PipelineTerminate = 5;
-    PipelineTerminating = 6;
-    PipelineTerminated = 7;
-  }
-  uint32 pipelineVersion = 1;
-  PipelineStatus status = 2;
-  string reason = 3;
-  google.protobuf.Timestamp lastChangeTimestamp = 4;
-  bool modelsReady = 5;
-}
-
-message SchedulerStatusRequest {
-  string subscriberName = 1;
-}
-
-message SchedulerStatusResponse {
-  string applicationVersion = 1;
-}
-
-// [END Messages]
-
-
-// [START Services]
-
-service Scheduler {
-  rpc ServerNotify(ServerNotifyRequest) returns (ServerNotifyResponse) {};
-
-  rpc LoadModel(LoadModelRequest) returns (LoadModelResponse) {};
-  rpc UnloadModel(UnloadModelRequest) returns (UnloadModelResponse) {};
-
-  rpc LoadPipeline(LoadPipelineRequest) returns (LoadPipelineResponse) {};
-  rpc UnloadPipeline(UnloadPipelineRequest) returns (UnloadPipelineResponse) {};
-
-  rpc StartExperiment(StartExperimentRequest) returns (StartExperimentResponse) {};
-  rpc StopExperiment(StopExperimentRequest) returns (StopExperimentResponse) {};
-
-  rpc ServerStatus(ServerStatusRequest) returns (stream ServerStatusResponse) {}
-  rpc ModelStatus(ModelStatusRequest) returns (stream ModelStatusResponse) {}
-  rpc PipelineStatus(PipelineStatusRequest) returns (stream PipelineStatusResponse) {};
-  rpc ExperimentStatus(ExperimentStatusRequest) returns (stream ExperimentStatusResponse) {};
-  rpc SchedulerStatus(SchedulerStatusRequest) returns (SchedulerStatusResponse) {};
-
-  rpc SubscribeServerStatus(ServerSubscriptionRequest) returns (stream ServerStatusResponse) {};
-  rpc SubscribeModelStatus(ModelSubscriptionRequest) returns (stream ModelStatusResponse) {};
-  rpc SubscribeExperimentStatus(ExperimentSubscriptionRequest) returns (stream ExperimentStatusResponse) {};
-  rpc SubscribePipelineStatus(PipelineSubscriptionRequest) returns (stream PipelineStatusResponse) {};
-}
-
-// [END Services]
-```
+{% @github-files/github-code-block url="https://github.com/SeldonIO/seldon-core/blob/v2/apis/mlops/scheduler/scheduler.proto" %}

From 05bb232757e663eb13a2d2bdab2adfba83abe85c Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 27 Aug 2024 15:09:50 +1000
Subject: [PATCH 30/32] added reference to chainer.proto instead of hard-coded
 version

---
 docs/apis/internal/agent.md   | 112 ----------------------------------
 docs/apis/internal/chainer.md |  80 +-----------------------
 2 files changed, 1 insertion(+), 191 deletions(-)

diff --git a/docs/apis/internal/agent.md b/docs/apis/internal/agent.md
index ba417c4856..60aff6fd4e 100644
--- a/docs/apis/internal/agent.md
+++ b/docs/apis/internal/agent.md
@@ -5,115 +5,3 @@ This API is for communication between the Seldon Scheduler and the Seldon Agent
 ## Proto Definition
 
 {% @github-files/github-code-block url="https://github.com/SeldonIO/seldon-core/blob/v2/apis/mlops/agent/agent.proto" %}
-
-
-```proto
-syntax = "proto3";
-
-package seldon.mlops.agent;
-
-option go_package = "github.com/seldonio/seldon-core/apis/go/v2/mlops/agent";
-
-import "mlops/scheduler/scheduler.proto";
-
-// [START Messages]
-
-message ModelEventMessage {
-  string serverName = 1;
-  uint32 replicaIdx = 2;
-  string modelName = 3;
-  uint32 modelVersion = 4;
-  enum Event {
-      UNKNOWN_EVENT = 0;
-      LOAD_FAIL_MEMORY = 1;
-      LOADED = 2;
-      LOAD_FAILED = 3;
-      UNLOADED = 4;
-      UNLOAD_FAILED = 5;
-      REMOVED = 6; // unloaded and removed from local PVC
-      REMOVE_FAILED = 7;
-      RSYNC = 9; // Ask server for all models that need to be loaded
-      }
-  Event event = 5;
-  string message = 6;
-  uint64 availableMemoryBytes = 7;
-}
-
-message ModelEventResponse {
-
-}
-
-message ModelScalingTriggerMessage {
-  string serverName = 1;
-  uint32 replicaIdx = 2;
-  string modelName = 3;
-  uint32 modelVersion = 4;
-  enum Trigger {
-      SCALE_UP = 0;
-      SCALE_DOWN = 1;
-      }
-  Trigger trigger = 5;
-  uint32 amount = 6;  // number of replicas required
-  map<string,uint32> metrics = 7;  // optional metrics to expose to the scheduler
-}
-
-message ModelScalingTriggerResponse {
-
-}
-
-message AgentDrainRequest {
-  string serverName = 1;
-  uint32 replicaIdx = 2;
-}
-
-message AgentDrainResponse {
-  bool success = 1;
-}
-
-message AgentSubscribeRequest {
-  string serverName = 1;
-  bool shared = 2;
-  uint32 replicaIdx = 3;
-  ReplicaConfig replicaConfig = 4;
-  repeated ModelVersion loadedModels = 5;
-  uint64 availableMemoryBytes = 6;
-}
-
-message ReplicaConfig {
-  string inferenceSvc = 1; // inference DNS service name
-  int32 inferenceHttpPort = 2; // inference HTTP port
-  int32 inferenceGrpcPort = 3; // Inference grpc port
-  uint64 memoryBytes = 4; // The memory capacity of the server replica
-  repeated string capabilities = 5; // The list of capabilities of the server, e.g. sklearn, pytorch, xgboost, mlflow
-  uint32 overCommitPercentage = 6; // The percentage of over commit to allow, set to 0 (%) to disable over commit
-}
-
-message ModelOperationMessage {
-  enum Operation {
-    UNKNOWN_EVENT = 0;
-    LOAD_MODEL = 1;
-    UNLOAD_MODEL = 2;
-  }
-  Operation operation = 1;
-  ModelVersion modelVersion = 2;
-  bool autoscalingEnabled = 3;
-}
-
-message ModelVersion {
-  scheduler.Model model = 1;
-  uint32 version = 2;
-}
-
-// [END Messages]
-
-// [START Services]
-
-service AgentService {
-  rpc AgentEvent(ModelEventMessage) returns (ModelEventResponse) {};
-  rpc Subscribe(AgentSubscribeRequest) returns (stream ModelOperationMessage) {};
-  rpc ModelScalingTrigger(stream ModelScalingTriggerMessage) returns (ModelScalingTriggerResponse) {};
-  rpc AgentDrain(AgentDrainRequest) returns (AgentDrainResponse) {};
-}
-
-// [END Services]
-```
diff --git a/docs/apis/internal/chainer.md b/docs/apis/internal/chainer.md
index f731e9c26f..fe04a92315 100644
--- a/docs/apis/internal/chainer.md
+++ b/docs/apis/internal/chainer.md
@@ -4,82 +4,4 @@ This API is for communication between the Seldon Scheduler and the Seldon datafl
 
 ## Proto Definition
 
-```proto
-syntax = "proto3";
-
-package seldon.mlops.chainer;
-
-option go_package = "github.com/seldonio/seldon-core/apis/go/v2/mlops/chainer";
-option java_package = "io.seldon.mlops.chainer";
-
-message PipelineSubscriptionRequest {
-  string name = 1;
-}
-
-message PipelineUpdateMessage {
-  enum PipelineOperation {
-    Unknown = 0;
-    Create = 1;
-    Delete = 2;
-  }
-  PipelineOperation op = 1;
-  string pipeline = 2;
-  uint32 version = 3;
-  string uid = 4;
-  repeated PipelineStepUpdate updates = 5;
-}
-
-message PipelineStepUpdate {
-  enum PipelineJoinType {
-    Unknown = 0;
-    Inner = 1;
-    Outer = 2;
-    Any = 3;
-  }
-  // https://docs.google.com/document/d/1tX-uaOvngx1RpEyWEZ4EbEcU8D0OgYuRWVb2UAi85n4/edit
-  // Pipeline Resource example, e.g. transform.outputs.traffic
-  //    seldon.<namespace>.<model name>.<inputs|outputs>.<tensor name>
-  repeated PipelineTopic sources = 1;
-  repeated PipelineTopic triggers = 2;
-  PipelineTopic sink = 3;
-  PipelineJoinType inputJoinTy = 4;
-  PipelineJoinType triggersJoinTy = 5;
-  bool passEmptyResponses = 6; // Forward empty response to following steps, default false
-  optional uint32 joinWindowMs = 7; // Join window millisecs, some nozero default (TBD)
-  repeated PipelineTensorMapping tensorMap = 8; // optional list of tensor name mappings
-  Batch batch = 9; // Batch settings
-}
-
-message PipelineTensorMapping {
-  string pipelineName = 1;
-  string topicAndTensor = 2;
-  string tensorName = 3;
-}
-
-message PipelineTopic {
-  string pipelineName = 1;
-  string topicName = 2;
-  optional string tensor = 3;
-}
-
-message Batch {
-  optional uint32 size = 1;
-  optional uint32 windowMs = 2;
-  bool rolling = 3;
-}
-
-message PipelineUpdateStatusMessage {
-  // TODO - include `name` to identify transformer message comes from
-  PipelineUpdateMessage update = 1;
-  bool success = 2;
-  string reason = 3;
-}
-
-message PipelineUpdateStatusResponse {
-}
-
-service Chainer {
-  rpc SubscribePipelineUpdates(PipelineSubscriptionRequest) returns (stream PipelineUpdateMessage) {};
-  rpc PipelineUpdateEvent(PipelineUpdateStatusMessage) returns (PipelineUpdateStatusResponse) {};
-}
-```
+{% @github-files/github-code-block url="https://github.com/SeldonIO/seldon-core/blob/v2/apis/mlops/chainer/chainer.proto" %}

From e4a43d977be6bd0bf07d26ab2d88c53150627a49 Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Tue, 27 Aug 2024 15:18:58 +1000
Subject: [PATCH 31/32] removed hard-coded references and added GitHub Gist
 pointing to v2 branch

---
 docs/getting-started/configuration.md | 75 +++------------------------
 1 file changed, 6 insertions(+), 69 deletions(-)

diff --git a/docs/getting-started/configuration.md b/docs/getting-started/configuration.md
index 5844d95780..cdf5dc26cd 100644
--- a/docs/getting-started/configuration.md
+++ b/docs/getting-started/configuration.md
@@ -6,25 +6,7 @@ Seldon can be configured via various config files.
 
 We allow configuration of the Kafka integration. In general this configuration looks like:
 
-```json
-# scheduler/config/kafka-internal.json
-{
-    "topicPrefix": "seldon",
-    "bootstrap.servers":"kafka:9093",
-    "consumer":{
-	"session.timeout.ms":6000,
-	"auto.offset.reset":"earliest",
-	"topic.metadata.propagation.max.ms": 300000,
-	"message.max.bytes":1000000000
-    },
-    "producer":{
-	"linger.ms":0,
-	"message.max.bytes":1000000000
-    },
-    "streams":{
-    }
-}
-```
+{% @github-files/github-code-block url="https://github.com/SeldonIO/seldon-core/blob/v2/scheduler/config/kafka-internal.json" %}
 
 The top level keys are:
 
@@ -47,22 +29,7 @@ The `consumerGroupIdPrefix` will ensure that all consumer groups created have a
 For Kubernetes this is controlled via a ConfigMap called `seldon-kafka` whose default values are
 defined in the `SeldonConfig` custom resource.
 
-```yaml
-# k8s/yaml/components.yaml
-kafkaConfig:
-  bootstrap.servers: 'seldon-kafka-bootstrap.seldon-mesh:9092'
-  consumer:
-    auto.offset.reset: 'earliest'
-    message.max.bytes: '1000000000'
-    session.timeout.ms: '6000'
-    topic.metadata.propagation.max.ms: '300000'
-  consumerGroupIdPrefix: ''
-  debug: ''
-  producer:
-    linger.ms: '0'
-    message.max.bytes: '1000000000'
-  topicPrefix: 'seldon'
-```
+{% @github-files/github-code-block url="https://github.com/SeldonIO/seldon-core/blob/v2/k8s/yaml/components.yaml" %}
 
 When the `SeldonRuntime` is installed in a namespace a configMap will be created with these
 settings for Kafka configuration.
@@ -70,13 +37,8 @@ settings for Kafka configuration.
 To customize the settings you can add and modify the Kafka configuration via Helm, for example
 below is a custom Helm values file that add compression for producers:
 
-```yaml
-# k8s/samples/values-runtime-kafka-compression.yaml
-config:
-  kafkaConfig:
-    producer:
-      compression.type: gzip
-```
+{% @github-files/github-code-block url="https://github.com/SeldonIO/seldon-core/blob/v2/k8s/samples/values-runtime-kafka-compression.yaml" %}
+
 To use this with the SeldonRuntime Helm chart:
 
 ```sh
@@ -110,15 +72,7 @@ You can create alternate `SeldonConfig`s with different values or override value
 
 We allow configuration of tracing. This file looks like:
 
-```json
-# scheduler/config/tracing-internal.json
-{
-  "disable": false,
-  "otelExporterEndpoint": "otel-collector:4317",
-  "otelExporterProtocol": "grpc",
-  "ratio": "1"
-}
-```
+{% @github-files/github-code-block url="https://github.com/SeldonIO/seldon-core/blob/v2/scheduler/config/tracing-internal.json" %}
 
 The top level keys are:
 
@@ -136,24 +90,7 @@ The top level keys are:
 
 For Kubernetes this is controlled via a ConfigMap call `seldon-tracing` whose default value is shown below:
 
-```yaml
-# scheduler/k8s/config/tracing.yaml
-apiVersion: v1
-kind: ConfigMap
-metadata:
-  name: tracing
-data:
-  tracing.json: |-
-   {
-     "enable": true,
-     "otelExporterEndpoint": "seldon-collector:4317",
-     "otelExporterProtocol": "grpc",
-     "ratio": "1"
-   }
-  OTEL_JAVAAGENT_ENABLED: "true"
-  OTEL_EXPORTER_OTLP_ENDPOINT: "http://seldon-collector:4317"
-  OTEL_EXPORTER_OTLP_PROTOCOL: "grpc"
-```
+{% @github-files/github-code-block url="https://github.com/SeldonIO/seldon-core/blob/v2/scheduler/k8s/config/tracing.yaml" %}
 
 Note, this `ConfigMap` is created via our Helm charts and there is usually no need to modify it manually.
 

From 3903c25adc8b507908b82bd725e843d5691289bf Mon Sep 17 00:00:00 2001
From: Ramon Perez <ramonpzg@protonmail.com>
Date: Thu, 5 Sep 2024 13:35:31 +1000
Subject: [PATCH 32/32] fixed format and broken links

---
 docs/servers.md   | 8 +++++---
 docs/upgrading.md | 2 +-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/docs/servers.md b/docs/servers.md
index 38bee68fb4..6c9a4aee89 100644
--- a/docs/servers.md
+++ b/docs/servers.md
@@ -1,6 +1,8 @@
 # Servers
 
-By default Seldon installs two server farms using MLServer and Triton with 1 replica each. Models are scheduled onto servers based on the server's resources and whether the capabilities of the server matches the requirements specified in the Model request. For example:
+By default Seldon installs two server farms using MLServer and Triton with 1 replica each. Models
+are scheduled onto servers based on the server's resources and whether the capabilities of the server
+matches the requirements specified in the Model request. For example:
 
 ```yaml
 apiVersion: mlops.seldon.io/v1alpha1
@@ -92,9 +94,9 @@ spec:
 
 The `capabilities` field takes precedence over the `extraCapabilities` field.
 
-For some examples see [here](../examples/custom-servers.md).
+For some examples see [here](examples/custom-servers.md).
 
 
 ## Autoscaling of Servers
 
-Within docker we don't support this but for Kubernetes see [here](../kubernetes/autoscaling/README.md)
+Within docker we don't support this but for Kubernetes see [here](kubernetes/autoscaling/README.md)
diff --git a/docs/upgrading.md b/docs/upgrading.md
index f43d2d50d7..280d3033e1 100644
--- a/docs/upgrading.md
+++ b/docs/upgrading.md
@@ -9,7 +9,7 @@ Previously, the labelling has been inconsistent across different versions of Sel
 mixture of `app` and `app.kubernetes.io/name` used.
 
 If using the Prometheus operator ("Kube Prometheus"), please apply the v2.7.0 manifests for Core v2
-according to the [metrics documentation](../kubernetes/metrics.md).
+according to the [metrics documentation](kubernetes/metrics.md).
 
 Note that these manifests need to be adjusted to discover metrics endpoints based on the existing setup.