tutorial docs section (#156)

SeldonIO · Apr 21, 2022 · d65eb00 · d65eb00
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diff --git a/docs/source/contents/inference-artifacts/index.md b/docs/source/contents/inference-artifacts/index.md
@@ -10,6 +10,11 @@ To run your model inside Seldon you must supply an inference artifact that can b
   - Tag
   - Server Docs
   - Example
+* - Alibi-Detect
+  - MLServer
+  - `alibi-detect`
+  - [docs](https://docs.seldon.io/projects/alibi-detect/en/stable/)
+  - TBC
 * - DALI
   - Triton
   - `dali`

diff --git a/docs/source/contents/tutorials/index.md b/docs/source/contents/tutorials/index.md
@@ -0,0 +1,10 @@
+# Tutorials
+
+ * [An introduction to building inference pipelines and using Seldon](./workflow.md)
+
+```{toctree}
+:maxdepth: 1
+:hidden:
+
+workflow.md
+```
diff --git a/docs/source/contents/tutorials/workflow.md b/docs/source/contents/tutorials/workflow.md
@@ -0,0 +1,23 @@
+# Seldon Workflow Introduction
+
+Seldon inference is built from atomic Model components. Models as [shown here](../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.
+  * 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.
+    1. You 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 dteectors 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)
+
+
diff --git a/docs/source/contents/tutorials/workflow.png b/docs/source/contents/tutorials/workflow.png
diff --git a/docs/source/index.md b/docs/source/index.md
@@ -7,6 +7,7 @@
 
 contents/about/index
 contents/getting-started/index
+contents/tutorials/index
 contents/inference-artifacts/index
 contents/kubernetes/index
 contents/examples/index

diff --git a/operator/config/serverconfigs/mlserver.yaml b/operator/config/serverconfigs/mlserver.yaml
@@ -31,7 +31,7 @@ spec:
       name: agent
       env:
       - name: SELDON_SERVER_CAPABILITIES
-        value: "lightgbm,mlflow,python,sklearn,spark-mlib,xgboost"
+        value: "alibi-detect,lightgbm,mlflow,python,sklearn,spark-mlib,xgboost"
       - name: SELDON_OVERCOMMIT_PERCENTAGE
         value: "10"
       - name: SELDON_SERVER_HTTP_PORT

diff --git a/scheduler/Makefile b/scheduler/Makefile
@@ -471,7 +471,7 @@ start-agent-local-mlserver:
 		--server-type mlserver \
 		--log-level debug \
 		--config-path ${PWD}/config \
-		--replica-config '{"inferenceSvc":"0.0.0.0","inferenceHttpPort":8080,"inferenceGrpcPort":8081,"memoryBytes":1000000,"capabilities":["lightgbm","python","sklearn","xgboost"],"overCommitPercentage":20}'
+		--replica-config '{"inferenceSvc":"0.0.0.0","inferenceHttpPort":8080,"inferenceGrpcPort":8081,"memoryBytes":1000000,"capabilities":["alibi-detect","lightgbm","python","sklearn","xgboost"],"overCommitPercentage":20}'
 
 
 .PHONY: start-agent-local-triton

diff --git a/scheduler/all-base.yaml b/scheduler/all-base.yaml
@@ -32,7 +32,7 @@ services:
       - SELDON_SCHEDULER_PORT=${SCHEDULER_AGENT_PORT}
       - MEMORY_REQUEST=${AGENT_MEMORY_REQUEST}
       - SELDON_SERVER_TYPE=mlserver
-      - SELDON_SERVER_CAPABILITIES=lightgbm,mlflow,python,sklearn,spark-mlib,xgboost
+      - SELDON_SERVER_CAPABILITIES=alibi-detect,lightgbm,mlflow,python,sklearn,spark-mlib,xgboost
 
   agent-triton:
     build: