MLOps Tutorial Series
How to create an automatic model training & testing setup using GitHub Actions and Continuous Machine Learning (CML).
ci-cd ml-ops production github-actions cml video code tutorial

Learn how to use one of the most powerful ideas from the DevOps revolution, continuous integration, in your data science and machine learning projects. This hands-on tutorial shows you how to create an automatic model training & testing setup using GitHub Actions and Continuous Machine Learning (CML), two free and open-source tools in the Git ecosystem. Designed for total beginners!

  1. Intro to Continuous Integration for ML
  2. When data is too big for Git
  3. Track ML models with Git & GitHub Actions
  4. GitHub Actions with your own GPUs

Using CML with DVC

In many ML projects, data isn't stored in a Git repository and needs to be downloaded from external sources. DVC is a common way to bring data to your CML runner. DVC also lets you visualize how metrics differ between commits to make reports like this:

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

Authors community post
Quantitative researcher. Deep learning and bot enthusiast.
Share this project
Similar projects
Using GitHub Actions for MLOps & Data Science
A collection of resources on how to facilitate Machine Learning Ops with GitHub.
Act - GitHub Actions locally
Run your GitHub Actions locally.
Python Template for All Projects
A template that gives the batteries required to package code, CI checks, auto build and deploy docs, easy PyPi publishing support and docker files.
GitHub Actions & Machine Learning Workflows with Hamel Husain
In this talk, Hamel will provide a brief tutorial on GitHub Actions, and will show you how you can use this new tool to automate your ML workflows.
Top collections