Guía de estudio — AI-300

Create and manage resources in a Machine Learning workspace

• Create and manage a workspace
• Create and manage datastores
• Create and manage compute targets
• Configure identity and access management for workspaces

Create and manage assets in a Machine Learning workspace

• Create and manage data assets
• Create and manage environments
• Create and manage components
• Share assets across workspaces by using registries

Implement IaC for Machine Learning

• Configure GitHub integration with Machine Learning to enable secure access
• Deploy Machine Learning workspaces and resources by using Bicep and Azure CLI
• Automate resource provisioning by using GitHub Actions workflows
• Restrict network access to Machine Learning workspaces
• Manage source control for machine learning projects by using Git