Operationalize machine learning models (MLOps)

A path built around one platform

This Microsoft Learn route shows how to take a model from first experiment to monitored deployment, with Azure Machine Learning as the working environment and GitHub Actions handling automation. It is self-paced, made up of 7 modules and about 5 hours 49 minutes of material.

Why the module order helps

The path begins with first contact with Azure Machine Learning, moves through experiments and MLflow tracking, then reaches hyperparameter tuning and pipelines before ending with automated training and monitoring. Each stage builds on the artifacts of the one before, so skipping ahead tends to leave gaps.

Who it is for

• Engineers who already write training code and want a repeatable release process
• Teams that use GitHub and want model training wired into existing automation
• Anyone who wants hands-on Azure experience rather than slides about MLOps

Expect to move between Azure and GitHub, since the final modules connect the two.