The kubeflow/examples repository provides a collection of extended examples and tutorials for Kubeflow, demonstrating machine learning concepts, data science workflows, and various deployment scenarios. It serves as a learning resource for users new to Kubeflow and a reference for experienced practitioners.

How It Works

The repository organizes examples into end-to-end workflows, component-specific demonstrations, and public demos. These examples showcase practical applications of Kubeflow components for tasks like model training, deployment, and pipeline automation, often integrating with tools like Seldon Core, TensorFlow, and PyTorch.

Quick Start & Requirements

This repository is not actively maintained, and examples may be outdated or non-functional. Users seeking working examples are directed to the GitHub repositories of individual Kubeflow components. No direct installation or quick-start instructions are provided for this specific repository.

Highlighted Details

• Features end-to-end examples for Named Entity Recognition, GitHub issue summarization, PyTorch MNIST, and Object Detection.
• Includes component-focused examples like XGBoost for housing price prediction and pipeline examples for notebook-based workflows.
• Demonstrates advanced concepts such as distributed training (DDP, TF-jobs), model serving with Seldon Core, CI/CD integration, and pipeline automation using Argo.
• Covers various ML tasks including NLP, image recognition, and financial time series analysis, often leveraging Kubernetes and cloud platforms like GKE.

Maintenance & Community

The repository is explicitly marked as not maintained, with a warning that examples may be outdated or non-functional. Contributions are encouraged by joining Kubeflow community calls. Community engagement is facilitated via Slack, Twitter, and a mailing list, guided by a Code of Conduct.

Licensing & Compatibility

The provided README content does not specify a license type or any compatibility notes for commercial use.

Limitations & Caveats

The primary limitation is the repository's unmaintained status, meaning examples are likely outdated and may not function correctly with current Kubeflow versions. Users must consult individual Kubeflow component repositories for reliable, up-to-date examples and guidance.