model-registry-governance

What problem does it solve?

This Skill establishes robust standards and controls for managing AI model artifacts, ensuring traceability, reproducibility, and compliance throughout their lifecycle in enterprise deployments.

Core Features & Use Cases

• Standardized Metadata: Enforces a mandatory schema for tracking model lineage, datasets, licenses, and security ratings.
• Policy-Driven Promotion: Implements automated approval workflows and security checks before models can be promoted to production.
• Lifecycle Management: Defines clear states (draft, candidate, approved, deprecated, retired) and automates the retirement of stale or vulnerable models.
• Audit Readiness: Maintains immutable records of all governance actions, approvals, and policy executions.
• Use Case: Ensure that every AI model deployed in production has undergone rigorous security scanning, has clear ownership, and adheres to defined usage policies, preventing shadow AI and mitigating risks.

Quick Start

Establish model registry governance by defining metadata schemas and approval workflows.