Which AWS service can be used to deploy deep learning models at scale?

Amazon SageMaker is the appropriate service for deploying deep learning models at scale. It offers a fully managed platform that facilitates the entire machine learning workflow, from data preparation and model training to deployment and monitoring. With SageMaker, you can easily build, train, and tune models using a variety of built-in algorithms and frameworks, including TensorFlow and PyTorch, which are commonly used in deep learning.

One of the key features of SageMaker is its ability to handle large volumes of data and scale training across multiple instances, which is essential for deep learning tasks that typically require significant computational resources. Additionally, it allows you to deploy models directly into production with just a few clicks, providing endpoints for real-time inference or batch processing.

While Amazon S3 serves as object storage and can hold datasets used for training models, it does not provide the functionality needed to deploy models. AWS Lambda can execute code in response to events and is suitable for serverless applications, but it's not specifically designed for scalable deployment of deep learning models, particularly those that require heavy computational resources. Amazon RDS is a relational database service, which is not related to model training or deployment in machine learning contexts. Thus, SageMaker stands out as the most appropriate choice for deploying deep learning models at