What service automatically optimizes machine learning models for inference on cloud instances and edge devices?

The option that best answers the question is Amazon SageMaker Neo. This service is specifically designed to optimize machine learning models for deployment, enabling them to run efficiently on different hardware platforms, whether in the cloud or on edge devices. SageMaker Neo takes a trained model and automatically optimizes it, enhancing performance while preserving accuracy.

This optimization process allows models to be smaller and faster, ensuring they can make inferences with reduced latency and resource consumption, which is crucial for applications requiring real-time predictions, especially on devices with limited compute power.

While Amazon SageMaker is a comprehensive machine learning platform that enables the entire machine learning workflow, from data labeling to training and deploying models, it does not specifically focus on the optimization of models for inference in the way SageMaker Neo does. Amazon Elastic Inference allows users to attach low-cost GPU-powered inference to Amazon SageMaker and other services, but it does not involve the automatic optimization of models. Amazon Comprehend is a natural language processing service that helps analyze text but is not related to optimizing machine learning models for inference.