Operationalize machine learning models (MLOps)
Microsoft Learn
Last updated March 13, 2026
A path on the full machine learning operations (MLOps) lifecycle with Azure Machine Learning and GitHub. It covers experimenting and training with automated machine learning, MLflow-tracked notebooks, and the Responsible AI dashboard, then automating training with pipelines and hyperparameter tuning. The operations half triggers jobs with GitHub Actions, applies trunk-based development to protect the main branch, manages environments, and automates model deployment to production with GitHub Actions and the Azure Machine Learning CLI — turning a trained model into a repeatable, automated production pipeline.
What you'll learn
- Experimenting and training with automated machine learning, MLflow notebooks, and the Responsible AI dashboard
- Automating training with pipelines and hyperparameter tuning
- Triggering machine learning jobs with GitHub Actions
- Trunk-based development and managing environments for machine learning workflows
- Automating model deployment with GitHub Actions and the Azure Machine Learning CLI
Frequently asked questions about Operationalize machine learning models (MLOps)
Who is Operationalize machine learning models (MLOps) for?
Practitioners who can train models in Python or R and want to automate, deploy, and operate them in production.
Is Operationalize machine learning models (MLOps) free?
Yes — Operationalize machine learning models (MLOps) is completely free to take.
What are the prerequisites for Operationalize machine learning models (MLOps)?
Programming experience with Python or R, plus experience developing and training machine learning models.
Do you need to code for Operationalize machine learning models (MLOps)?
Yes — Operationalize machine learning models (MLOps) involves hands-on coding.
Why we suggest this course
For a practitioner who can already train models and now needs to ship and maintain them reliably, this path covers the operations layer: pipelines, hyperparameter tuning, GitHub Actions automation, environment management, and automated deployment. One thing to know: it assumes programming experience in Python or R and prior experience training machine learning models, so it picks up where model-building leaves off rather than teaching modeling itself.