seniorAzure ML
How do you handle model governance and compliance in Azure ML?
Updated May 15, 2026
Short answer
Model governance in Azure ML involves versioning, lineage tracking, approval workflows, audit logging, explainability, and compliance controls.
Deep explanation
Enterprise AI systems must comply with regulatory, ethical, and operational requirements. Azure ML supports governance through centralized lifecycle management and auditing.
Governance capabilities include:
- Model versioning
- Dataset lineage tracking
- Approval workflows
- Experiment reproducibility
- Role-based access control
- Explainability reporting
- Audit logs
- Responsible AI tooling
- Deployment history tracking
Compliance is especially critical in regulated industries such as:
- Healthcare
- Banking
- Insurance
- Government…
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