What This Repository Does

This is a collection of real-world tutorials and working examples for Kubeflow, an open-source platform that helps teams build and deploy machine learning systems. Think of it as a cookbook of recipes—instead of starting from scratch, you get copy-paste-ready projects that walk you through building everything from data prep to a trained model to a live prediction service.

The repository covers three main categories. End-to-end examples show you a complete ML workflow: they typically start with raw data, show how to train a model on a cluster of computers, and then serve that model so other applications can use it for predictions. Component-focused examples zoom in on one specific piece—like training with XGBoost on a particular cloud provider. Demos are designed for showing off the platform on stage, not for self-guided learning.

Who Would Use This and Why

If you're a data scientist or engineer setting up machine learning infrastructure, these examples save you months of work. Instead of figuring out how to connect Jupyter notebooks to cloud storage, then orchestrate training jobs, then deploy a model—you get step-by-step walkthroughs with actual code. For example, one tutorial shows how to build a system that summarizes GitHub issues using natural language processing, touching storage, training, and serving. Another demonstrates image recognition on handwritten digits with automatic monitoring. A financial forecasting example shows how to train multiple models in parallel and iterate on them.

The examples also act as a reference—experienced users can scan them to see how others solved similar problems or to understand what's possible within the Kubeflow ecosystem.

How It's Organized

The repository is hosted on GitHub with separate folders for each example. Most tutorials include Jupyter notebooks (interactive Python documents that explain and run code), configuration files for setting up cloud infrastructure, and documentation. Some examples use specific components like TensorFlow for deep learning, PyTorch for training, or Seldon Core for serving predictions—but the README explains which tools each example uses upfront. The community maintains these examples and actively invites contributions; there are links to Slack, a mailing list, and a code of conduct for people who want to get involved.