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