What is the primary purpose of Amazon SageMaker?

The primary purpose of Amazon SageMaker is to build, train, and deploy machine learning models at scale. This service is designed to simplify the machine learning workflow, making it accessible for developers and data scientists to create machine learning applications efficiently. SageMaker provides a comprehensive set of tools and features that facilitate the entire machine learning lifecycle, from data preparation and model training to deployment and monitoring.

By offering pre-built algorithms, integration with Jupyter notebooks for experimentation, and the ability to easily scale training processes using distributed training, SageMaker allows users to focus on model development rather than the complexities of infrastructure management. It also supports deploying models directly to production, enabling quick and efficient use of the trained models for predictions at scale.

In contrast, the other options focus on different aspects of AWS services; analyzing large amounts of data pertains to services like Amazon Redshift or Amazon Athena, managing AWS infrastructure relates to services like AWS Management Console, and providing storage services points to AWS S3 or Amazon EBS. None of these services are specifically geared towards managing the end-to-end machine learning process in the same way that SageMaker is designed to do.