Deploy and monitor a model in Azure Machine Learning

  • Module
  • 9 Units
Intermediate
Data Scientist
DevOps Engineer
Azure Machine Learning
GitHub

Choose this module if you want to register, deploy, monitor, and roll back models by using GitHub Actions and protected environments.

Learning objectives

After completing this module, you can:

  • Register, version, and archive MLflow models throughout their lifecycle.
  • Use GitHub environments to control access and require approval before production promotion.
  • Deploy and troubleshoot a managed online endpoint, safely promote a model, and roll back to a previous version.
  • Automate model deployment and testing with GitHub Actions.
  • Monitor a deployed model and recognize signals that call for investigation or retraining.

Prerequisites

  • Programming experience with Python or R
  • Experience developing and training machine learning models
  • Familiarity with basic Azure Machine Learning concepts, such as jobs, models, and endpoints
  • Basic familiarity with GitHub and continuous integration/continuous deployment (CI/CD) concepts
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