# Model Registry Governance

Establish model registry standards, governance controls, metadata schemas, approvals...

- Repository: https://github.com/BagelHole/DevOps-Security-Agent-Skills (1187 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-05-22
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add BagelHole/DevOps-Security-Agent-Skills --skill model-registry-governance -g -a claude-code -y`
Codex: `npx skills add BagelHole/DevOps-Security-Agent-Skills --skill model-registry-governance -g -a codex -y`
Cursor: `npx skills add BagelHole/DevOps-Security-Agent-Skills --skill model-registry-governance -g -a cursor -y`
Gemini CLI: `npx skills add BagelHole/DevOps-Security-Agent-Skills --skill model-registry-governance -g -a gemini-cli -y`

## Description

Establish model registry standards, governance controls, metadata schemas, approvals, and lifecycle policies for enterprise AI deployments.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

```bash
# 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
```

The rest: https://github.com/BagelHole/DevOps-Security-Agent-Skills/blob/HEAD/devops/ai/model-registry-governance/SKILL.md