Model Registry Governance

Create a trustworthy system of record for model artifacts, prompts, adapters, and evaluation evidence.

When to Use This Skill

• Setting up a centralized model registry for your organization
• Defining metadata standards for model artifacts
• Building approval workflows for model promotion to production
• Implementing lifecycle policies for model retirement
• Preparing for compliance audits of AI systems

Prerequisites

• MLflow Tracking Server or Weights & Biases instance deployed
• Object storage for model artifacts (S3, GCS, or MinIO)
• CI/CD pipeline with access to the registry API
• OPA or similar policy engine for governance checks
• Git repository for policy definitions and promotion scripts

Core Principles

• Traceability: every production model maps to source code, data snapshot, and evaluation results.
• Reproducibility: builds are deterministic with pinned dependencies.
• Policy-driven promotion: no manual bypass for critical safety checks.
• Lifecycle hygiene: stale, vulnerable, or unowned models are retired automatically.

MLflow Registry Setup

# Install MLflow with required backends
pip install mlflow[extras] psycopg2-binary boto3

# Start MLflow tracking server with PostgreSQL backend and S3 artifact store
mlflow server \
  --backend-store-uri postgresql://mlflow:password@db:5432/mlflow \
  --default-artifact-root s3://mlflow-artifacts/models \
  --host 0.0.0.0 \
  --port 5000 \
  --serve-artifacts