Managing model registries is a crucial aspect of Machine Learning Operations (MLOps) that facilitates the organization and tracking of machine learning models throughout their lifecycle. A model registry serves as a centralized repository where models can be registered, versioned, and stored alongside their metadata, such as training data and performance metrics. This enables teams to efficiently manage different model versions, promote or decline models, and link experiments to their corresponding models. By implementing a robust model registry, organizations can streamline their deployment processes, enhance collaboration, and ensure compliance with best practices in model management.