Which AWS service is specifically designed to build and deploy recommendation systems?

Amazon Personalize is specifically designed for building and deploying recommendation systems. It enables developers to create individualized recommendations based on user activity and preferences. The service uses machine learning algorithms that analyze user interactions, items, and other contextual data to generate personalized experiences in real-time.

Amazon Personalize abstracts the complexity of machine learning, allowing users with minimal ML expertise to implement sophisticated recommendation solutions easily. This service supports various use cases, including personalized product recommendations for e-commerce, content recommendations for media, and more.

In comparison, while Amazon SageMaker is a comprehensive service for building, training, and deploying machine learning models, it is not tailored specifically for recommendation systems. Amazon Comprehend focuses on natural language processing and understanding text, and Amazon Rekognition is designed for image and video analysis. Each of these services serves different purposes and does not cater directly to the needs of recommendation system development like Amazon Personalize does.