Operationalize machine learning models (MLOps)

  • Learning Path
  • 7 Modules

At a glance

Learn how to operationalize machine learning models using the complete MLOps lifecycle. This learning path covers experimenting and training models with Azure Machine Learning, automating model training with pipelines and hyperparameter tuning, triggering jobs with GitHub Actions, implementing trunk-based development, managing environments, and deploying models to production.

Prerequisites

  • Programming experience with Python or R
  • Experience developing and training machine learning models
  • Familiarity with basic Azure Machine Learning concepts

Modules in this learning path

Experiment with Azure Machine Learning

Learn how to find the best machine learning model with automated machine learning (AutoML), MLflow-tracked notebooks, and the Responsible AI dashboard.

Perform hyperparameter tuning with Azure Machine Learning

Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.

Run pipelines in Azure Machine Learning

Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.

Trigger Azure Machine Learning jobs with GitHub Actions

Learn how to automate your machine learning workflows by using GitHub Actions.

Trigger GitHub Actions with feature-based development

Learn how to protect your main branch and how to trigger tasks in the machine learning workflow based on changes to the code.

Work with environments in GitHub Actions

Learn how to train, test, and deploy a machine learning model by using environments as part of your machine learning operations (MLOps) strategy.

Deploy a model with GitHub Actions

Learn how to automate and test model deployment with GitHub Actions and the Azure Machine Learning CLI (v2).

Start