Which Azure ML component is used to store and version trained models?

The Azure Machine Learning model registry acts as a central repository within a workspace to manage the lifecycle of machine learning models. It allows you to store, version, and track your trained models along with their associated metadata, such as metrics and tags. This functionality is crucial for reproducibility, governance, and deploying the correct model version to production environments.

A. A Dataset is a reference to data in a datastore used for training; it is not the trained model itself.

C. A Pipeline is a workflow of ML steps; it can produce and register a model but is not the storage component.

D. A Compute cluster provides the processing resources for training models but does not store the resulting artifacts.

1. Microsoft. (2023). Manage models with Azure Machine Learning. Microsoft Learn. Retrieved from https://docs.microsoft.com/en-us/azure/machine-learning/concept-model. (Section: "What is a model?")

2. Microsoft. (2023). What is Azure Machine Learning?. Microsoft Learn. Retrieved from https://docs.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning. (Section: "Assets")