In Azure, what is the purpose of a model registry?

A model registry in Azure serves the crucial function of managing and versioning machine learning models. This is essential as machine learning projects often involve multiple iterations of models, with modifications and improvements over time. A model registry provides a centralized repository where various versions of models can be stored, making it easy to track changes and ensure that the best-performing versions are easily accessible.

This system facilitates collaboration among data scientists and machine learning engineers, as they can work with consistent and versioned models. Furthermore, it simplifies the process of conducting experiments, as users can load previous versions of a model for reevaluation or further development.

In the context of machine learning lifecycle management, having a robust model registry enhances reproducibility and governance, allowing teams to maintain an organized approach to model deployment and auditing.

Other options mentioned like creating new datasets, storing raw data inputs, or deploying models across environments do not align with the specific purpose of a model registry. While those tasks are also vital aspects of the machine learning process, they fall under different categories of data management and deployment rather than the focused responsibility of managing and versioning models specifically.