GitHub Actions is the appropriate automation tool because the deployment process originates from pull requests and requires CI/CD automation, traceability, and an approval gate. Azure Machine Learning integrates directly with GitHub Actions for automated machine learning workflows, including infrastructure and asset deployment. Workflows can be triggered by repository events such as pull requests, merges, or pushes, providing an auditable record of who initiated the change, which commit was deployed, and whether the workflow succeeded.

Critically, GitHub environments can enforce required reviewers before a deployment job executes. Microsoft documents that when an environment requires approval, a job cannot access the environment ' s protected secrets until one of the required reviewers approves the deployment. This directly satisfies the requirement that deployment must require approval before running.

Azure Machine Learning pipelines orchestrate ML workflow steps such as preprocessing, training, evaluation, and registration, but they are not primarily the repository-level pull-request approval mechanism. MLflow provides experiment tracking and model lifecycle functionality, not CI orchestration. Azure Monitor provides telemetry and alerting rather than source-driven deployment automation.

Therefore, GitHub Actions provides the required combination of pull-request integration, automation, auditability, and protected deployment approval.

Study Guide Reference: Design and implement an MLOps infrastructure — CI/CD, GitHub Actions, pull-request automation, deployment approvals, protected environments, and Azure Machine Learning integration.

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