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Introduction to Metaflow Tutorial Season 2

This season introduces the basics of machine learning workflows. You will see examples of how to train, explore, and track machine learning models. You will see how to build flows that use Scikit-learn and XGBoost, which cover many use cases in machine learning. You will build scalable workflows that you can adapt to many machine learning contexts.

To Run The Code

Ensure you have followed the setup steps. Then,

cd <YOUR PATH>/tutorials/intro-to-mf/season-2

What You Will Learn

At the end of this season you will be able to:

  • Build models in a way you can operationalize efficiently.
  • Train models in parallel.
  • Store and analyze data produced by model training workflows.

For help or feedback, please join Metaflow Slack. To suggest an article, you may open an issue on GitHub.

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