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Azure MLOps: DevOps for Machine Learning

DevOps for machine learning models, often called MLOps, is a process for developing models for production.

A model’s lifecycle from training to deployment must be auditable if not reproducible.

Machine Learning Model Lifecycle

Machine learning model lifecycle - MLOps

Learn more about MLOps in Azure Machine Learning.

Integrations Enabling MLOPs

Azure Machine Learning is built with the model lifecycle in mind. You can audit the model lifecycle down to a specific commit and environment.

Some key features enabling MLOps include:

  • git integration
  • MLflow integration
  • Machine learning pipeline scheduling
  • Azure Event Grid integration for custom triggers
  • Easy to use with CI/CD tools like GitHub Actions or Azure DevOps

Also, Azure Machine Learning includes features for monitoring and auditing:

  • Job artifacts, such as code snapshots, logs, and other outputs
  • Lineage between jobs and assets, such as containers, data, and compute resources

Backlinks:

list from [[Azure MLOps]] AND -"Changelog"