Which AWS service is utilized to manage and distribute ML models?

Amazon SageMaker Model Registry is specifically designed for managing and distributing machine learning models. It provides functionalities that allow you to store, version, and organize models across their lifecycle. This service facilitates easy tracking of model versions, enabling better collaboration among teams and streamlining the deployment process of machine learning models into production environments.

With the SageMaker Model Registry, you can register a model after training, retrieve different versions as needed, and promote models to various stages of a workflow, such as development or production. This helps ensure a more structured and reliable approach to managing machine learning deployments.

In contrast, Amazon S3 is primarily an object storage service useful for storing data and artifacts but does not provide specialized functionalities for managing machine learning models. Amazon EC2 is a computing service that provides virtual machines but does not include any specific model management capabilities. Amazon Aurora is a relational database service designed for data storage and not for model management. Thus, the effectiveness of the SageMaker Model Registry in the context of machine learning makes it the ideal choice for managing and distributing ML models.