Definition
A model registry is a centralized system for managing the lifecycle of machine learning models. It acts as a single source of truth, tracking model versions, their associated metadata, and performance metrics throughout development and deployment. This systematic approach ensures reproducibility and facilitates collaboration among data scientists and engineers. In practice, it allows teams to version control models like code, enabling rollbacks and comparisons between different iterations. Using a model registry is crucial for maintaining model governance, auditing, and ensuring compliance in production environments. For instance, a financial institution might use a model registry to track different versions of a fraud detection model, ensuring only approved and tested versions are deployed. This differs from a simple model store, which typically only stores trained models without comprehensive versioning or lifecycle management. Concepts related to model registries appear in advanced AI and MLOps certifications.