A Model Registry in MLOps is a centralized repository used to store, organize, and manage machine learning models throughout their lifecycle. It helps teams track different model versions, manage approvals, and control the transition of models from development to production. This improves collaboration, reproducibility, and governance in machine learning workflows. A model registry also makes it easier to monitor, update, or roll back models when necessary. In your opinion, how important is a Model Registry for scaling ML operations, and what challenges can arise when managing models without one?