End-to-end machine learning operations (MLOps) with Azure Machine Learning

  • Learning Path
  • 6 Modules
Intermediate
Data Scientist
Azure Machine Learning
GitHub

Machine learning operations (MLOps) applies DevOps principles to machine learning projects. In this learning path, you'll learn how to implement key concepts like source control, automation, and CI/CD to build an end-to-end MLOps solution.

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

Use an Azure Machine Learning job for automation

Learn how to take your machine learning model from experimentation to production by using Azure Machine Learning jobs.

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 linting and unit testing in GitHub Actions

Learn how to automate code checks whenever you update code for machine learning workloads.

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).