Model Repository

A Model Repository is a centralized platform or system for storing, versioning, managing, and sharing machine learning models, datasets, and related artifacts. It enables teams to collaborate on model development, track experiments, and deploy models consistently across environments. Examples include MLflow Model Registry, Hugging Face Hub, and AWS SageMaker Model Registry.

Also known as: Model Registry, Model Hub, ML Model Store, Model Management Platform, Model Versioning System

Why learn Model Repository?

Developers should use a Model Repository when working on machine learning projects that require reproducibility, collaboration, and streamlined deployment. It is essential for managing model lifecycles in production systems, facilitating A/B testing, and ensuring compliance with version control and audit trails. This is particularly valuable in enterprise settings or large-scale AI applications.

Learning Resources

• docs: MLflow Model Registry Documentation
• docs: Hugging Face Hub Guide
• tutorial: AWS SageMaker Model Registry Tutorial
• course: Introduction to MLOps with Model Repositories
• video: Model Management Best Practices Video