How can AWS help with model versioning?

The correct answer is based on the capabilities of Amazon SageMaker, particularly its model registry feature. This feature is specifically designed to streamline the process of model versioning. With the model registry, users can manage multiple versions of machine learning models, allowing data scientists and engineers to organize, track, and deploy models effectively.

When models are registered, each version can be characterized with metadata, such as descriptions and labels, making it easier for teams to evaluate model changes, reproduce results, and ensure that the best-performing models are utilized in production. Additionally, the model registry facilitates collaboration across teams by providing a centralized location for model storage and versioning information.

While Amazon S3 is an excellent service for data storage, it does not inherently provide version control specifically for machine learning models. It lacks features that fully support the complex needs associated with managing different iterations of models.

Using AWS Lambda functions allows for serverless computing and automation, but it does not directly address model versioning.

Amazon CloudWatch is mainly focused on monitoring and logging AWS resources and applications, including metrics and alarms. While it can provide insights into model performance after deployment, it does not have built-in capabilities for managing model versions directly.

Therefore, B is the correct answer as it directly relates to the