Model Registry

Automatically tracks every iteration of a model artifact, from experimental candidate to production champion. Each version is uniquely identified and linked to its immutable model lineage, including the exact training dataset, code commit, and hyperparameters. This ensures full reproducibility and enables instant rollback to any previous state. Unlike ad-hoc file storage, a registry enforces a strict model versioning protocol, preventing confusion between development and production artifacts.

Stores structured, searchable metadata alongside the model binary. This includes the model provenance—a verifiable record of origin and transformations—as well as evaluation metrics, the author, and the intended use. The registry acts as the source of truth for an AI BOM (AI Bill of Materials), cataloging all dependencies. This deep annotation allows auditors to instantly verify a model's training data attribution and compliance status without reverse-engineering the artifact.

Enforces a formal promotion workflow through distinct lifecycle stages such as Staging, Production, and Archived. A model cannot be deployed to production without explicit approval and a status change within the registry. This creates a strict governance gate, ensuring that only validated models with complete model cards and approved algorithmic impact assessments are served to downstream applications. It directly implements the human-on-the-loop control required by the EU AI Act for high-risk systems.

Provides a standardized API and webhook system to trigger automated CI/CD pipelines upon model registration or status change. When a new model version is tagged as 'Production', the registry can programmatically notify a serving infrastructure like Kubernetes or a model server to begin a canary deployment. This eliminates manual handoffs between data scientists and MLOps engineers, reducing latency from experiment to production and ensuring the deployed artifact matches the registered, audited binary exactly.