Model Registry
A database or system for recording, tracking, and managing AI models throughout their lifecycle.
Definition
Model Registry refers to systems for cataloging, versioning, and tracking AI models throughout their lifecycle—from development through deployment and retirement. It serves both internal governance and regulatory compliance purposes.
EU AI Act Requirements: The Act mandates registration in an EU-level database:
- Article 71: Establishes EU database for high-risk AI systems
- Registration required: Providers must register high-risk systems before placing on market
- Information included: Provider details, system description, conformity status, intended purpose
- Public access: Much information publicly searchable
Internal Registry Functions:
- Version control: Track model iterations and changes
- Metadata management: Training data, parameters, performance metrics
- Lineage tracking: Document model provenance and dependencies
- Deployment tracking: Where and how models are deployed
- Access control: Manage who can use or modify models
- Audit trail: Record all model-related activities
Regulatory Benefits:
- Demonstrates governance and oversight
- Facilitates incident investigation
- Supports conformity assessment
- Enables post-market monitoring
Tools: MLflow Model Registry, AWS SageMaker Model Registry, Azure ML Model Registry, custom enterprise solutions.
Related concepts: EU Database for High-Risk AI Systems, Technical Documentation, Post-Market Monitoring, Audit Trail
Sources
- •SR 11-7
- •Model Risk Management Best Practices
Related Terms
EU Database for High-Risk AI Systems
The EU-managed registry where providers and certain public deployers must record key information about high‑risk AI systems (and some non‑high‑risk registrations) to ensure transparency and oversight....
Technical Documentation
Detailed records demonstrating AI system compliance with regulatory requirements....
Post-Market Monitoring
Ongoing surveillance of AI system performance after deployment in the market....