What is the primary function of Amazon SageMaker?

The primary function of Amazon SageMaker is to develop and deploy machine learning models. Amazon SageMaker provides a comprehensive suite of tools that simplifies the process of building, training, and deploying machine learning models at scale. It offers features such as built-in algorithms, Jupyter notebooks for interactive development, and a managed infrastructure for training and inference, which helps data scientists and developers focus on the actual machine learning tasks rather than the underlying infrastructure and logistics. By integrating various stages of the machine learning workflow, SageMaker makes it easier to create models that can be used to analyze data, make predictions, and derive insights—all essential components of modern AI applications.

The other options do not align with the core functionality of SageMaker. While cloud storage, database management, and user authentication are important in the AWS ecosystem, they are handled by different services such as Amazon S3 for storage, Amazon RDS or DynamoDB for databases, and AWS IAM for user access control. SageMaker specifically targets the machine learning lifecycle, making it a distinct choice among AWS services.