ML Infrastructure & MLOps • Model Governance (Compliance, Auditability)Medium⏱️ ~3 min
What is Model Governance in ML Systems?
Model governance is the comprehensive framework of policies, processes, and technical controls that ensure Machine Learning (ML) systems remain compliant, accountable, and auditable throughout their entire lifecycle. It addresses two critical outcomes. First, compliance with external regulations like General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Health Insurance Portability and Accountability Act (HIPAA), and internal policies around data privacy, model risk management, and fairness. Second, auditability that provides complete traceability so any prediction can be reproduced, explained, and attributed to specific data, code versions, and human approvals.
In production, this translates to concrete technical requirements. Every artifact (model, dataset, feature definition) must be versioned and immutable. Every change requires documented approval and is recorded in audit logs. Every runtime decision can be traced back to a specific model build, dataset snapshot, and the exact feature values used at decision time. For example, a bank running fraud detection at 25,000 predictions per second with 50 millisecond p99 latency must still capture request ID, model version, dataset fingerprint, feature vector hash, and decision explanation for each prediction without violating latency Service Level Objectives (SLOs).
The stakes are significant. GDPR noncompliance can result in fines up to 4% of annual global turnover. The EU AI Act introduces risk classifications requiring specific documentation and reporting for high risk systems. Financial institutions must follow Model Risk Management guidance like Supervisory Requirements (SR) 11 to 7, maintaining seven year retention of decision records. Microsoft requires Responsible AI reviews and model cards for sensitive use cases before deployment. Google popularized model cards to standardize documentation of intended use, metrics, and limitations.
Effective governance balances control with velocity. Proven patterns include immutable artifact registries, approval workflows with separation of duties (the person who trains cannot directly deploy), append only audit logs, and continuous monitoring for accuracy drift, data distribution shifts measured by Population Stability Index (PSI), and bias across protected groups. The goal is a system where every prediction is explainable and traceable, every change is intentional and approved, and compliance checks are continuous rather than episodic audits.
💡 Key Takeaways
•Compliance with regulations like GDPR (4% revenue fines), CCPA, HIPAA, and EU AI Act requires data privacy controls, fairness evaluations, and documented model risk management throughout the ML lifecycle
•Auditability demands complete traceability where every prediction can be reproduced using the exact model version, dataset snapshot, and feature values from decision time, even at high throughput like 25,000 requests per second
•Technical implementation uses immutable artifact registries with cryptographic hashes, approval workflows with separation of duties (trainer cannot deploy), and append only audit logs that cannot be tampered with
•Production systems balance governance with performance by using asynchronous prediction journals that log metadata (request ID, model version, feature hashes) with p99 enqueue latency under 5 milliseconds to meet overall 50 millisecond SLOs
•Model cards standardized by Google and required by Microsoft document intended use, limitations, evaluation metrics, and subgroup performance before any deployment to production environments
•Continuous monitoring tracks data drift using Population Stability Index (PSI) with alerts when PSI exceeds 0.2 for three consecutive windows, and bias metrics across protected groups to detect harmful changes between formal reviews
📌 Examples
Bank fraud detection at 25,000 predictions per second logs each decision to append only storage, accumulating 1.7 TB per day (800 bytes per log), kept hot for 30 days and archived for 7 years to satisfy SR 11 to 7 audit requirements
Microsoft Responsible AI process requires formal reviews for sensitive use cases, model cards with documented limitations and fairness metrics, and security scanning before any high risk model moves to production
Amazon enforces separation of duties where the data scientist who trains a model cannot directly deploy it, requiring approval from a separate release engineer and a formal change ticket for audit trails