GitHub Actions for Machine Learning
This presentation discusses the use of GitHub Actions to automate certain steps of a toy ML project.
github mlops scikit-learn wandb code github-actions tutorial

In this deck I discuss the importance of incorporating CI/CD in ML engineering. We took a small demo that uses scikit-learn and GitHub Actions to automate certain parts of an ML Project and lets a bot comment on a PR with the latest experimental results.

This repository demonstrates how to integrate GitHub Actions to:

Upon a new commit

  • Automatically authenticate wandb (Weights and Biases) using a custom GitHub secret.
  • Automatically train a small Random Forest Regressor model on the wine quality dataset.
  • Automatically log the training and other important model metrics to wandb.
  • Cache Python dependencies so that old dependencies do not get installed each time a run is triggered.
  • Generate a metrics.csv file after a run is successfully completed.

Upon a new pull request

  • Fetch the latest wandb run URL and comment that on the PR.

Don't forget to tag @sayakpaul in your comment, otherwise they may not be notified.

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