Samples and tutorials

This page summarizes samples and tutorials that demonstrate how to use the assets that are available on AI Hub.

Kubeflow pipelines

The following samples and tutorials illustrate how to use Kubeflow pipelines.

  • GitHub issue summarization is an advanced codelab focused on creating Kubeflow pipelines. This codelab demonstrates how to:

    • Set up a Kubeflow cluster using Google Kubernetes Engine
    • Build and run ML workflows using Kubeflow Pipelines
    • Run pipelines from within a Jupyter notebook
  • Kubeflow Pipelines end-to-end on Google Kubernetes Engine is a tutorial that demonstrates how to:

    • Set up a Kubeflow cluster using Google Kubernetes Engine
    • Compile a sample pipeline by using the Kubeflow Pipelines SDK
    • Run the pipeline
  • Getting started with Kubeflow Pipelines is a blog post that provides an introduction to building machine learning (ML) workflows with Kubeflow Pipelines. This blog post uses sample pipelines to demonstrate how to create, analyze, monitor, and make predictions with Kubeflow Pipelines.

The following videos provide overviews and demonstrations of Kubeflow Pipelines:

Introduction to Kubeflow pipelines

This section provides background information to help you complete the samples and tutorials. The section is particularly useful if you're getting started with Kubeflow pipelines

Kubeflow pipelines are portable and scalable end-to-end machine learning (ML) workflows built using the Kubeflow Pipelines SDK. To learn how to create pipelines, see the Kubeflow pipelines author's guide.

Kubeflow Pipelines is a component of Kubeflow that provides a platform for building and deploying ML workflows. Kubeflow is an open source toolkit for running ML workloads on Kubernetes. Learn more about getting started with Kubeflow.

To set up Kubeflow Pipelines on Google Kubernetes Engine (GKE), choose one of the following options:

TensorFlow modules

The following samples and tutorials illustrate how to use TensorFlow modules.

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