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

# docker-compose.yaml for MLflow
services:
  mlflow:
    image: ghcr.io/mlflow/mlflow:2.12.0
    command: >
      mlflow server
      --backend-store-uri postgresql://mlflow:${DB_PASSWORD}@db:5432/mlflow
      --default-artifact-root s3://mlflow-artifacts/models
      --host 0.0.0.0
      --port 5000
      --serve-artifacts
    ports:
      - "5000:5000"
    environment:
      AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID}
      AWS_SECRET_ACCESS_KEY: ${AWS_SECRET_ACCESS_KEY}
    depends_on:
      - db

  db:
    image: postgres:16-alpine
    environment:
      POSTGRES_DB: mlflow
      POSTGRES_USER: mlflow
      POSTGRES_PASSWORD: ${DB_PASSWORD}
    volumes:
      - pgdata:/var/lib/postgresql/data

volumes:
  pgdata:

Required Metadata Schema

# model_metadata_schema.py
from pydantic import BaseModel, Field
from typing import List, Optional
from datetime import datetime
from enum import Enum

class LifecycleState(str, Enum):
    DRAFT = "draft"
    CANDIDATE = "candidate"
    APPROVED = "approved"
    DEPRECATED = "deprecated"
    RETIRED = "retired"

class RiskRating(str, Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"

class ModelMetadata(BaseModel):
    """Required metadata for every registered model."""
    # Identity
    name: str = Field(description="Model name matching registry key")
    version: str = Field(description="Semantic version")
    checksum: str = Field(description="SHA-256 of model artifact")
    storage_uri: str = Field(description="Artifact store path")

    # Lineage
    base_model: str = Field(description="Parent model identifier")
    fine_tune_method: Optional[str] = Field(default=None)
    training_dataset: Optional[str] = Field(default=None)
    training_date: Optional[datetime] = Field(default=None)
    source_commit: str = Field(description="Git SHA of training code")

    # Evaluation
    eval_datasets: List[str] = Field(description="Evaluation dataset IDs")
    eval_report_uri: str = Field(description="Path to evaluation results")
    quality_score: float = Field(ge=0, le=1)
    safety_score: float = Field(ge=0, le=1)

    # Governance
    license: str = Field(description="SPDX license identifier")
    allowed_use_cases: List[str]
    prohibited_use_cases: List[str]
    risk_rating: RiskRating
    security_controls: List[str]

    # Ownership
    owner: str = Field(description="Primary owner email")
    backup_owner: str = Field(description="Backup owner email")
    escalation_contact: str
    team: str

    # Lifecycle
    state: LifecycleState = LifecycleState.DRAFT
    created_at: datetime = Field(default_factory=datetime.utcnow)
    approved_at: Optional[datetime] = None
    approved_by: Optional[str] = None
    expires_at: Optional[datetime] = None

Model Registration Script

# register_model.py
import mlflow
from mlflow.tracking import MlflowClient
import json
import hashlib

def register_model(
    model_path: str,

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