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+++ b/content/en/docs/started/architecture.md @@ -4,122 +4,115 @@ description = "An overview of Kubeflow's architecture" weight = 10 +++ - - -This guide introduces Kubeflow as a platform for developing and deploying a -machine learning (ML) system. - -Kubeflow is a platform for data scientists who want to build and experiment with -ML pipelines. Kubeflow is also for ML engineers and operational teams who want -to deploy ML systems to various environments for development, testing, and -production-level serving. +This guide introduces Kubeflow ecosystem and explains how Kubeflow components fit in ML lifecycle. -## Conceptual overview +Read [the introduction guide](/docs/started/introduction) to learn more about Kubeflow, standalone +Kubeflow components and Kubeflow Platform. -Kubeflow is *the ML toolkit for Kubernetes*. +## Kubeflow Ecosystem -The following diagram shows Kubeflow as a platform for arranging the -components of your ML system on top of Kubernetes: +The following diagram gives an overview of the Kubeflow Ecosystem and how it relates to the wider +Kubernetes and AI/ML landscapes. An architectural overview of Kubeflow on Kubernetes - -Kubeflow builds on [Kubernetes](https://kubernetes.io/) as a system for -deploying, scaling, and managing complex systems. + class="mt-3 mb-3"> -Using the Kubeflow configuration interfaces (see [below](#interfaces)) you can -specify the ML tools required for your workflow. Then you can deploy the -workflow to various clouds, local, and on-premises platforms for experimentation and -for production use. +Kubeflow builds on [Kubernetes](https://kubernetes.io/) as a system for +deploying, scaling, and managing AI/ML infrastructure. -## Introducing the ML workflow +## Introducing the ML Lifecycle -When you develop and deploy an ML system, the ML workflow typically consists of -several stages. Developing an ML system is an iterative process. -You need to evaluate the output of various stages of the ML workflow, and apply -changes to the model and parameters when necessary to ensure the model keeps +When you develop and deploy an AI application, the ML lifecycle typically consists of +several stages. Developing an ML system is an iterative process. +You need to evaluate the output of various stages of the ML lifecycle, and apply +changes to the model and parameters when necessary to ensure the model keeps producing the results you need. -For the sake of simplicity, the following diagram -shows the workflow stages in sequence. The arrow at the end of the workflow -points back into the flow to indicate the iterative nature of the process: +The following diagram shows the ML lifecycle stages in sequence: -A typical machine learning workflow +ML Lifecycle Looking at the stages in more detail: -* In the experimental phase, you develop your model based on initial - assumptions, and test and update the model iteratively to produce the - results you're looking for: - - * Identify the problem you want the ML system to solve. - * Collect and analyze the data you need to train your ML model. - * Choose an ML framework and algorithm, and code the initial version of your - model. - * Experiment with the data and with training your model. - * Tune the model hyperparameters to ensure the most efficient processing and the - most accurate results possible. - -* In the production phase, you deploy a system that performs the following - processes: - - * Transform the data into the format that your training system needs. - To ensure that your model behaves consistently during training and - prediction, the transformation process must be the same in the experimental - and production phases. - * Train the ML model. - * Serve the model for online prediction or for running in batch mode. - * Monitor the model's performance, and feed the results into your processes - for tuning or retraining the model. - -## Kubeflow components in the ML workflow - -The next diagram adds Kubeflow to the workflow, showing which Kubeflow -components are useful at each stage: - -Where Kubeflow fits into a typical machine learning workflow +- In the _Data Preparation_ step you ingest raw data, perform feature engineering to extract ML + features for the offline feature store, and prepare training data for model development. + Usually, this step is associated with data processing tools such as Spark, Dask, Flink, or Ray. -To learn more, read the following guides to the Kubeflow components: +- In the _Model Development_ step you choose an ML framework, develop your model architecture and + explore the existing pre-trained models for fine-tuning like BERT or Llama. -* Kubeflow includes services for spawning and managing - [Jupyter notebooks](/docs/components/notebooks/). Use notebooks for interactive data - science and experimenting with ML workflows. +- In the _Model Optimization_ step you can optimize your model hyperparameters and optimize your + model with various AutoML algorithms such as neural architecture search and model compression. + During model optimization you can store ML metadata in the _Model Registry_. -* [Kubeflow Pipelines](/docs/components/pipelines/) is a platform for - building, deploying, and managing multi-step ML workflows based on Docker - containers. +- In the _Model Training_ step you train or fine-tune your model on the large-scale + compute environment. You should use a distributed training if single GPU can't handle your + model size. The results of the model training is the trained model artifact that you + can store in the _Model Registry_. -* Kubeflow offers several [components](/docs/components/) that you can use - to build your ML training, hyperparameter tuning, and serving workloads across - multiple platforms. +- In the _Model Serving_ step you serve your model artifact for online or batch inference. Your + model may perform predictive or generative AI tasks depending on the use-case. During the model + serving step you may use an online feature store to extract features. You monitor the model + performance, and feed the results into your previous steps in the ML lifecycle. -## Example of a specific ML workflow +### ML Lifecycle for Production and Development Phases -The following diagram shows a simple example of a specific ML workflow that you -can use to train and serve a model trained on the MNIST dataset: +The ML lifecycle for AI applications may be conceptually split between _development_ and +_production_ phases, this diagram explores which stages fit into each phase: -ML workflow for training and serving an MNIST model +ML Lifecycle with Development and Production + +### Kubeflow Components in the ML Lifecycle + +The next diagram shows how Kubeflow components are used for each stage in the ML lifecycle: + +Kubeflow Components in ML Lifecycle + +See the following links for more information about each Kubeflow component: -For details of the workflow and to run the system yourself, see the -[end-to-end tutorial for Kubeflow on GCP](https://github.com/kubeflow/examples/tree/master/mnist#mnist-on-kubeflow-on-gcp). +- [Kubeflow Spark Operator](https://github.com/kubeflow/spark-operator) can be used for data + preparation and feature engineering step. - -## Kubeflow interfaces +- [Kubeflow Notebooks](/docs/components/notebooks/) can be used for model development and interactive + data science to experiment with your ML workflows. + +- [Kubeflow Katib](/docs/components/katib/) can be used for model optimization and hyperparameter + tuning using various AutoML algorithms. + +- [Kubeflow Training Operator](/docs/components/training/) can be used for large-scale distributed + training or fine-tuning. + +- [Kubeflow Model Registry](/docs/components/model-registry/) can be used to store ML metadata, + model artifacts, and preparing models for production serving. + +- [KServe](https://kserve.github.io/website/master/) can be used for online and batch inference + in the model serving step. + +- [Feast](https://feast.dev/) can be used as a feature store and to manage offline and online + features. + +- [Kubeflow Pipelines](/docs/components/pipelines/) can be used to build, deploy, and manage each + step in the ML lifecycle. + +You can use most Kubeflow components as +[standalone tools](/docs/started/introduction/#what-are-standalone-kubeflow-components) and +integrate them into your existing AI/ML Platform, or you can deploy the full +[Kubeflow Platform](/docs/started/introduction/#what-is-kubeflow-platform) to get all Kubeflow +components for an end-to-end ML lifecycle. + +## Kubeflow Interfaces This section introduces the interfaces that you can use to interact with Kubeflow and to build and run your ML workflows on Kubeflow. -### Kubeflow user interface (UI) +### Kubeflow User Interface (UI) The Kubeflow UI looks like this: @@ -127,19 +120,28 @@ The Kubeflow UI looks like this: alt="The Kubeflow UI" class="mt-3 mb-3 border border-info rounded"> -The UI offers a central dashboard that you can use to access the components -of your Kubeflow deployment. Read -[how to access the central dashboard](/docs/components/central-dash/overview/). +The Kubeflow Platform includes [Kubeflow Central Dashboard](/docs/components/central-dash/overview/) +which acts as a hub for your ML platform and tools by exposing the UIs of components running in the +cluster. + +### Kubeflow APIs and SDKs + + -## Kubeflow APIs and SDKs +Various components of Kubeflow offer APIs and Python SDKs. -Various components of Kubeflow offer APIs and Python SDKs. See the following -sets of reference documentation: +See the following sets of reference documentation: -* [Pipelines reference docs](/docs/components/pipelines/reference/) for the Kubeflow +- [Pipelines reference docs](/docs/components/pipelines/reference/) for the Kubeflow Pipelines API and SDK, including the Kubeflow Pipelines domain-specific language (DSL). +- [Training Operator Python SDK](https://github.com/kubeflow/training-operator/blob/86e0df17db715543b366e885c9ae659aa1342c8e/sdk/python/kubeflow/training/api/training_client.py) + to manage Training Operator jobs using Python APIs. +- [Katib Python SDK](https://github.com/kubeflow/katib/blob/086093fed72610c227e3ae1b4044f27afa940852/sdk/python/v1beta1/kubeflow/katib/api/katib_client.py) + to manage Katib hyperparameter tuning Experiments using Python APIs. ## Next steps -* Follow [Installing Kubeflow](/docs/started/installing-kubeflow/) to set up your environment and install Kubeflow. +- Follow [Installing Kubeflow](/docs/started/installing-kubeflow/) to set up your environment and install Kubeflow. diff --git a/content/en/docs/started/images/ml-lifecycle-dev-prod.drawio.svg b/content/en/docs/started/images/ml-lifecycle-dev-prod.drawio.svg new file mode 100644 index 0000000000..a1564db481 --- /dev/null +++ b/content/en/docs/started/images/ml-lifecycle-dev-prod.drawio.svg @@ -0,0 +1,4 @@ + + + +
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 \ No newline at end of file diff --git a/content/en/docs/started/introduction.md b/content/en/docs/started/introduction.md index b404c161cc..a89b39214d 100644 --- a/content/en/docs/started/introduction.md +++ b/content/en/docs/started/introduction.md @@ -41,21 +41,19 @@ The Kubeflow Platform can be installed via [Packaged Distributions](/docs/started/installing-kubeflow/#packaged-distributions) or [Kubeflow Manifests](/docs/started/installing-kubeflow/#kubeflow-manifests). -## Getting started with Kubeflow +## Kubeflow Overview Diagram -The following diagram shows the main Kubeflow components to cover each step of ML lifecycle +The following diagram shows the main Kubeflow components to cover each stage of the ML lifecycle on top of Kubernetes. Kubeflow overview -Read the [architecture overview](/docs/started/architecture/) for an -introduction to the architecture of Kubeflow and to see how you can use Kubeflow -to manage your ML workflow. +Read the [architecture overview](/docs/started/architecture/) to learn about the Kubeflow ecosystem +and to see how Kubeflow components fit in ML lifecycle. -Follow [Installing Kubeflow](/docs/started/installing-kubeflow/) to set up -your environment and install Kubeflow. +## Kubeflow Video Introduction Watch the following video which provides an introduction to Kubeflow.