What is a Model Registry? - Definition & Meaning

Definition

A model registry is a central repository for ML models with versioning, metadata, and lifecycle management. It enables reproducible deployments, rollbacks, and compliance.

Technical explanation

A model registry stores models, artifacts, and metadata (dataset, hyperparameters, metrics). Versioning ensures v1.2 and v1.3 coexist. Stages (Staging, Production) govern promote workflows. MLflow Model Registry, Kubeflow, SageMaker Model Registry, and Azure ML Model Registry are common. CI/CD integration: training jobs push models; deployment pipelines pull approved versions. Lineage tracking links models to data and code for audit.

How AVARC Solutions applies this

AVARC Solutions uses MLflow or cloud-native registries (Vertex AI, SageMaker) for all production ML. We document metadata, automate promote-to-production, and build rollback procedures. For governance-focused clients we set up approval workflows.

Practical examples

• A team able to roll back to the previous model version with one click in the registry when regression occurs.
• A compliance department seeing which model was trained on which data and when it was promoted in the registry.
• An A/B test running two model versions in parallel; the registry serves both artifacts.

Related terms