A machine learning (ML) model registry serves as a centralized repository that facilitates the management and documentation of ML models throughout their lifecycle, enhancing collaboration between data scientists and operations teams. It provides features like model versioning, metadata storage, and integration with other tools such as experiment management systems and CI/CD pipelines, allowing for efficient deployment, testing, and validation of models. The registry ensures models are discoverable, trackable, and protected, supporting governance and security requirements, crucial for industries with regulatory compliance needs. It bridges experimental and production activities, enabling faster model deployment and simplifying lifecycle management. Organizations can choose to build, maintain, or subscribe to fully managed model registry solutions, weighing factors such as incentive, resources, cost, and support. Popular solutions like MLflow, Verta.ai, and Neptune.ai offer varying features and benefits, catering to different organizational needs and MLOps maturity levels.