Which AWS service supports machine learning models for real-time inference?

Amazon SageMaker is specifically designed to support machine learning workflows, including the deployment of machine learning models for real-time inference. This service provides an integrated set of tools that allow developers and data scientists to build, train, and deploy machine learning models at scale.

One of the key features of Amazon SageMaker is its ability to create endpoints that can serve predictions in real-time. This means that once a training job is completed, the trained model can be deployed to an endpoint, allowing applications to send data and receive immediate predictions. This instantaneous processing is critical for use cases such as fraud detection, personalized recommendations, and other applications where timely responses are essential.

The other services listed do not focus specifically on machine learning model deployment. Amazon EC2 provides virtual servers in the cloud for computing but lacks the specific tools for machine learning model management and real-time inference. Amazon RDS is primarily a relational database service and does not have direct capabilities for deploying machine learning models. Amazon Athena, which is an interactive query service for analyzing data in Amazon S3 using standard SQL, is also not designed for real-time inference of machine learning models. Therefore, Amazon SageMaker stands out as the optimal choice for tasks related to machine learning, particularly in the context of real-time inference.