Automate model training with GitHub Actions

Beginner

Data Scientist

Azure Machine Learning

GitHub

Choose this module if you want to automate and validate model training with GitHub Actions and Azure Machine Learning pipelines.

Learning objectives

In this module, you learn how to:

Identify which machine learning assets to version in Git and which belong in purpose-built services such as data stores and model registries.
Describe trunk-based development practices — short-lived branches, pull requests, and branch protection rules — that keep the shared branch stable while allowing safe iteration.
Explain how GitHub Actions workflows validate code changes with linting and unit tests, and how branch protection can require those checks before merging.
Compare service principal client secrets with workload identity federation, and explain why OpenID Connect is the preferred authentication approach for GitHub Actions workflows that call Azure.
Describe how a GitHub Actions workflow submits Azure Machine Learning command and pipeline jobs, and explain how an intermediary converts Azure events into repository_dispatch events.

Experience developing and training machine learning models
Familiarity with basic Azure Machine Learning concepts, including workspaces and training jobs
Basic familiarity with GitHub repositories, branches, and pull requests