The Model Registry is a centralized model store that allows you to manage the full lifecycle of MLflow Models. It provides model lineage, versioning, stage transitions, and annotations. You can use the Model Registry to discover and share models, collaborate on moving them from experimentation to production, integrate with approval and governance workflows, and monitor model deployments and performance1. The other options are incorrect because:

Option A: Models is a component of MLflow that allows you to manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms. Models is not a centralized model store, but rather a convention for packaging machine learning models that offers self-contained code, environments, and models2.

Option C: Model Serving is a feature of Databricks that allows you to host MLflow Models as REST endpoints. Model Serving is not a centralized model store, but rather a model deployment option that enables low-latency inference for real-time applications3.

Option D: Feature Store is a feature of Databricks that allows you to store, discover, and share features for machine learning. Feature Store is not a centralized model store, but rather a centralized feature store that enables feature reuse, consistency, and governance across ML projects4.

Option E: Experiments is a component of MLflow that allows you to track experiments to record and compare parameters and results. Experiments is not a centralized model store, but rather a logging service that stores runs, metrics,parameters, tags, and artifacts for each experiment. References: MLflow Model Registry, MLflow Models, MLflow Model Serving, Databricks Feature Store, [MLflow Tracking]