Which AWS service is primarily used for creating machine learning models?

Amazon SageMaker is the service designed specifically for the creation, training, and deployment of machine learning models. It provides an integrated environment that allows data scientists and developers to build, train, and deploy machine learning applications at scale.

With SageMaker, you have access to a range of built-in algorithms and frameworks, such as TensorFlow, PyTorch, and Scikit-learn, enabling users to efficiently create models without needing to manage the underlying infrastructure. It also offers tools for data labeling, automated model tuning, and hosting, streamlining the end-to-end machine learning lifecycle.

In contrast, AWS Glue is focused on data integration and ETL (extract, transform, load) processes, aimed at preparing data for analytics. Amazon Aurora is a relational database service that provides high performance and availability but does not specialize in machine learning. Amazon S3 is primarily a storage service for data rather than model creation. Thus, SageMaker stands out as the dedicated solution for machine learning model development within the AWS ecosystem.