Video coming soon
Building Governance into the ML Pipeline
Even organizations that understand governance-as-architecture struggle with implementation. How do you translate governance policies into code that runs inside ML platforms? How do you enforce deployment gates without creating bottlenecks?
“Policy documents don’t stop bad models from going to production. Pipeline gates do. I built a governance-as-code architecture that automates model approval, bias testing, and documentation — directly inside the deployment pipeline.”
Architecture Diagrams
Governance-as-code three-layer stack
ML pipeline with governance gates at each stage
Platform comparison: Databricks vs. SageMaker
Build Notes
- Three implementation layers: Policy Definition (YAML/OPA/Rego), Enforcement (CI/CD), Dashboard/Reporting
- OPA policies evaluate in milliseconds — no deployment latency for low-risk models
- Platform-specific implementations for Databricks and AWS SageMaker
- Auto-generated model cards from MLflow metadata
Lessons Learned
- Start with risk tiering — don’t apply the same governance weight to every model
- Auto-approve low-risk models that pass all automated tests; human review only for high-risk
- Model cards generated from MLflow metadata are more accurate than manually written ones
- The governance dashboard drives executive visibility and justifies the investment
Discussion
If you’re running ML pipelines in production, how many governance gates exist between training completion and production deployment? If the answer is zero, how do you know what’s running?