An enterprise AI governance team wants to implement a model versioning and approval process to ensure that only vetted AI models are deployed to production. Before a model is promoted, it must pass security scans, bias evaluations, and compliance checks. Which Azure service best supports implementing this approval gate in an MLOps pipeline?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you want Azure DevOps with pipeline gates requiring manual approvals and automated quality checks before deployment stages. It lets you run security scans, bias evaluations, and compliance checks, then require governance sign-off before promoting a model to production.
Full Explanation
Azure DevOps with pipeline gates requiring manual approvals and automated quality checks before deployment stages is the best fit because Azure DevOps, or GitHub Actions with environment protection rules, supports MLOps pipelines with deployment gates. These gates can include automated checks such as security scans, bias evaluation scripts, and compliance checks, plus manual approval requirements from security and governance reviewers before a model is promoted to a production environment. This governance workflow ensures models are vetted before deployment. Azure Machine Learning compute clusters with auto-scaling are wrong because they handle the computational resources for training and inference; they are not governance workflow tools. Microsoft Purview model compliance assessment is wrong because while Microsoft Purview can catalog and classify models, it does not provide a pipeline approval workflow for model promotion gates. Azure Policy deny effect for non-tagged ML models is wrong because Azure Policy can enforce tagging of deployed ML models but cannot implement a sequential approval workflow for model promotion.