1Design and implement an MLOps infrastructure

• 1.1Machine Learning workspaces and resources

  Understand creating and managing an Azure Machine Learning (AML) workspace, datastores, compute targets, and identity and access management for the workspace (managed identities / RBAC)—the foundation of MLOps infrastructure.

• 1.2Managing assets in Machine Learning

  Understand creating and managing AML assets (data assets, environments, components) and sharing assets across workspaces using registries, for reproducibility and reuse.

• 1.3IaC and automation for Machine Learning

  Understand secure GitHub–Machine Learning integration, deploying workspaces/resources with Bicep and Azure CLI, automating provisioning with GitHub Actions workflows, restricting network access to workspaces, and Git source control—as the automation foundation of MLOps.