What is typically contained within the Model Registry?

The Model Registry serves as a centralized hub for managing machine learning models throughout their lifecycle. It typically includes information about different versions of models, their current states (such as staging or production), and metadata related to those models. One of the key aspects captured within the Model Registry is the transition history of models, which details how each model has moved from development to production. This includes timestamps, versioning information, and any associated notes, helping teams track the evolution and performance of models over time.

In contrast, other options would not typically be stored in the Model Registry. Raw datasets used for model training are generally stored in data storage solutions rather than in the registry itself. The source code for the feature store is also managed separately to enable collaboration on feature engineering. Lastly, parameters used in data extraction are often linked with preprocessing and data management systems rather than the Model Registry, which focuses specifically on the models themselves.