Infrastructure as Code for Machine Learning
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Included in this chapter:
- Why Infrastructure as Code for Azure ML
- Authenticate GitHub Actions to Azure with OIDC
- Declare and deploy: Bicep, ARM JSON, and CLI v2
- Automate the lifecycle with GitHub Actions
- Restrict network access to the workspace
- Exam-pattern recognition
Choosing an IaC or provisioning tool for Azure ML
| Aspect | Bicep | ARM JSON | Terraform | az ml CLI v2 |
|---|---|---|---|---|
| Language / format | Azure-native DSL | ARM template JSON | HCL (third party) | Azure ML YAML specs |
| Deploy command | az deployment group create | az deployment group create | terraform apply | az ml <asset> create |
| What it provisions | Any Azure resource | Any Azure resource | Azure plus other clouds | Workspace-scoped AML assets |
| Best fit | Azure-only, readable templates | Lowest-level or generated output | One toolchain across clouds | Compute, environments, jobs, endpoints |
Decision tree
Cheat sheet
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Also tested in
References
- Create a Workspace by Using an Azure Resource Manager Template - Azure Machine Learning
- Deployment modes - Azure Resource Manager
- Create a trust relationship between an app and an external identity provider - Microsoft Entra Workload ID
- Authenticate to Azure from GitHub Actions by OpenID Connect
- GitHub Actions for CI/CD - Azure Machine Learning
- What is Bicep? - Azure Resource Manager
- Create workspaces with Azure CLI - Azure Machine Learning
- Git integration - Azure Machine Learning
- Managed virtual network isolation - Azure Machine Learning
- Configure a private endpoint - Azure Machine Learning