COMPEL Glossary / model-registry
Model Registry
A model registry is a centralized, versioned repository for storing, cataloging, and managing AI models throughout their lifecycle, maintaining metadata about each model's training data, hyperparameters, performance metrics, deployment status, owner, and governance approval status.
What this means in practice
The registry provides the single source of truth for which models exist, which versions are in production, and what their characteristics are. For organizations managing multiple AI models, a model registry prevents the chaos of untracked model versions, undocumented dependencies, and ungoverned deployments that commonly occur when AI development scales beyond a single team. In COMPEL, the model registry is part of the AI platform infrastructure assessed during Calibrate and designed during Module 3.3, connecting to the governance framework through approval workflows and compliance tracking.
Why it matters
Without a model registry, organizations accumulate untracked model versions, undocumented dependencies, and ungoverned deployments that create chaos as AI development scales. The registry provides the single source of truth for which models exist, which versions are in production, and what their characteristics are. Organizations that manage AI without a registry cannot answer basic governance questions about their deployed AI capabilities.
How COMPEL uses it
The model registry is part of the AI platform infrastructure assessed during Calibrate and designed during Module 3.3. During Model, registry requirements including metadata standards and governance integration are specified. The Produce stage implements the registry with version control and approval workflows. The Evaluate stage audits registry completeness, ensuring every production model is registered with current metadata and governance documentation.
Related Terms
Other glossary terms mentioned in this entry's definition and context.