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Managed ML lifecycle on Azure by Microsoft

Azure Machine Learning

The natural home for ML workloads inside the Azure perimeter, complementary to AI Foundry.

01 What is it?

Azure Machine Learning is Microsoft's managed platform for the full ML lifecycle: data labelling, training, experimentation, deployment, monitoring and governance. It complements Azure AI Foundry by covering classical ML, custom model training and MLOps at scale.

02 Why implement it?

  • Managed end-to-end ML lifecycle on Azure
  • Native integration with Microsoft Entra, Key Vault, Private Endpoints
  • MLOps with managed online and batch endpoints
  • Responsible AI dashboards and model registry
  • Strong compliance (HIPAA, SOC 2, FedRAMP, EU Data Boundary)

03 How I help

I design Azure ML architectures aligned to your security boundary: workspace isolation, private endpoints, fine-grained role-based access control, model registry governance, and integration with Defender for Cloud and your existing CSPM tooling.

04 Expected deliverables

  • Azure ML landing-zone design
  • Identity and entitlement model
  • Endpoint deployment and approval workflow
  • Responsible AI and audit pipeline
  • Cost and governance review
Ready to implement? Initial scoping call, typically 30 minutes, no commitment.
contact@jeremycanale.com