Which AWS service is utilized for machine learning?

Amazon SageMaker is specifically designed for building, training, and deploying machine learning models at scale. It provides a comprehensive set of tools that simplify each part of the machine learning workflow, from data preparation to model training and deployment. Users can leverage built-in algorithms, integrate with popular machine learning frameworks, and easily manage compute resources, which makes it an ideal choice for machine learning tasks.

In contrast, Amazon Rekognition is primarily focused on image and video analysis, offering capabilities for detecting objects, faces, and scenes. While it employs machine learning techniques, its scope is narrower compared to SageMaker, which is more general-purpose for machine learning tasks.

Amazon Athena is a query service designed for analyzing data in Amazon S3 using standard SQL. It is not focused on machine learning but rather on data analysis, making it a different tool altogether.

Amazon RDS is a managed database service that simplifies the setup, operation, and scaling of relational databases in the cloud. While data stored in RDS can be used for machine learning, RDS itself does not provide machine learning capabilities, thus it does not fit the category as directly as SageMaker does.

Overall, SageMaker stands out as the best option for dedicated machine learning capabilities within the AWS ecosystem.