microsoft/MLOps resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2116 stars · 606 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of end-to-end MLOps examples and solutions from Microsoft showing how to operationalize ML workflows with Azure Machine Learning, GitHub, and Azure services like Data Factory and DevOps. It serves as sample code and guidance for bringing ML models to production on Azure.

Use cases

• learn mlops on azure with examples
• set up a ci/cd pipeline for machine learning models
• operationalize ml model training and deployment workflows
• integrate azure machine learning with azure devops and github
• find end-to-end mlops architecture examples
• version and track ml lifecycle assets in production

When to choose

• you use Azure Machine Learning and want concrete end-to-end MLOps examples
• you want sample pipelines integrating Azure ML with Azure DevOps, GitHub, or Data Factory
• you are learning MLOps concepts with working Jupyter Notebook examples

When to avoid

• you need MLOps tooling for AWS, GCP, or on-premises environments
• you want the latest Microsoft MLOps guidance - the repo points to the newer Azure MLOps (v2) accelerator
• you need a production-ready library rather than example code

Facets

learning-resource · maturity maintenance

machine-learning ci-cd deployment workflow-automation machine-learning cloud-computing tutorials python cloud mlops azure-machine-learning azure-devops examples jupyter-notebooks sample-code devops docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="microsoft/MLOps")