What is the main purpose of Amazon SageMaker?

Amazon SageMaker is specifically designed to facilitate the entire machine learning workflow. Its main purpose is to enable users to build, train, and deploy machine learning models efficiently. This involves providing a suite of tools and services that streamline the process, such as pre-built algorithms, data labeling, and managed training environments. SageMaker also allows for the easy deployment of models into production environments, making it a comprehensive solution for machine learning practitioners.

The other options do not accurately reflect the primary function of Amazon SageMaker. For instance, while running serverless functions is associated with AWS Lambda, it is not the focus of SageMaker. Storing large datasets aligns more with services like Amazon S3, which is optimized for data storage rather than model training. Performing batch data processing is primarily handled by services such as AWS Glue or AWS Batch, which differ from the model-centric capabilities of SageMaker. Therefore, the identification of option B as the correct answer is grounded in SageMaker's dedicated functions related to the machine learning lifecycle.