Model Registry
Model Registry is a centralized system for storing, organizing, tracking, and managing machine learning models and their associated versions, metadata, and lifecycle information.
What is Model Registry?
A Model Registry maintains records of trained models, including their versions, performance metrics, configurations, and deployment status. It provides a structured way for teams to register models, compare versions, promote validated models to production, and track changes throughout the model lifecycle.
Why is Model Registry Important?
A model registry improves visibility and control over machine learning models as they move from development to production. It supports reproducibility, collaboration, governance, and version management while making it easier to identify which model is currently deployed and roll back to an earlier version when necessary.
Common use cases
Model registries are commonly used for model versioning, deployment management, experiment tracking, model governance, auditing, and managing machine learning models across development and production environments.