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 maintain a clear overview of different model versions, promote or demote models as needed, and link experiments to their corresponding models. Effective management of model registries enhances collaboration, ensures reproducibility, and streamlines the deployment process, ultimately leading to more efficient and reliable machine learning workflows.