Model Registry

A Model Registry is a centralized platform for managing the lifecycle of machine learning models, including versioning, tracking metadata, and deployment. It serves as a single source of truth for model artifacts, enabling teams to collaborate, reproduce experiments, and ensure governance. Common features include model lineage, stage transitions (e.g., from staging to production), and integration with ML pipelines.

Also known as: ML Model Registry, Model Management Platform, Model Repository, Model Catalog, ML Registry

Why learn Model Registry?

Developers should use a Model Registry when working on machine learning projects that require scalable model management, especially in production environments with multiple models and frequent updates. It is essential for maintaining reproducibility, auditing model changes, and streamlining deployment workflows, such as in MLOps pipelines or regulated industries like finance or healthcare.

Learning Resources

• docs: MLflow Model Registry Documentation
• video: Introduction to Model Management with MLflow
• tutorial: Model Registry Best Practices on AWS
• course: Coursera: MLOps Fundamentals