Which AWS service is designed for deploying machine learning models in a serverless manner?

The service that is specifically designed for deploying machine learning models in a serverless manner is Amazon SageMaker. This service provides a broad range of capabilities for building, training, and deploying machine learning models without the need to manage underlying infrastructure, allowing users to focus on developing their models.

SageMaker allows users to create endpoints for real-time inference, scaling automatically based on the number of requests, which embodies the serverless paradigm. It's built to handle the entire machine learning workflow, simplifying the complexities associated with moving from model development to deployment.

While Lambda is a serverless compute service, its primary purpose is to run code in response to events rather than being tailored specifically for machine learning models. It can indeed be integrated with machine learning solutions, but it does not provide the broad machine learning capabilities that SageMaker does.

DynamoDB is a NoSQL database service designed for applications requiring low-latency responses, and EC2 is a compute service that provides resizable compute capacity in the cloud but requires manual scaling and management, making it less suitable for serverless ML deployment. Therefore, the capabilities and focus of SageMaker make it the ideal answer for the question regarding serverless deployment of machine learning models.