Observed Signal · Apr 25, 2026 · Technical Guide · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
CI/CD for Azure ML with DevOps and Terraform
A technical guide showing how to build CI/CD for machine learning on Azure using Azure ML Pipelines (SDK v2), Azure DevOps, and Terraform. The post describes an end-to-end architecture where code pushes trigger Azure DevOps pipelines that run CI tests, submit Azure ML pipeline jobs (preprocess → train → evaluate → conditional registration), and use a manual approval gate before deploying models to endpoints. The author includes Terraform examples to provision a service principal, storage for pipeline artifacts, an Azure DevOps project and service connection, plus a repository-based Azure DevOps YAML that implements CI, CD (az ml job create) and approval stages. The article stresses using Azure ML SDK v2 (SDK v1 reached end-of-support March 2025 and will stop in June 2026) and recommends federated identity (OIDC) to avoid rotating service-principal secrets.
Practical, actionable MLOps tutorial that helps teams automate model training and deployment on Azure; useful to engineering teams but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Explains CI/CD architecture combining Azure ML Pipelines (SDK v2), Azure DevOps pipelines, and Terraform provisioning.
- Provides Terraform code examples to create an Azure AD service principal, role assignment (Contributor on ML workspace), storage container for pipeline artifacts, and azuredevops project/service connection.
- Publishes an Azure DevOps pipeline YAML that runs CI (install, pytest, component validation), submits an Azure ML pipeline job via az ml job create, and enforces a manual approval gate before deployment.
- Recommends Azure ML SDK v2 only; notes SDK v1 reached end-of-support in March 2025 and will stop working in June 2026.
- Advises using federated identity (OIDC) in Azure DevOps as a secretless alternative and using the AzureML Job Wait task for long-running training jobs.
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