What does it mean to deploy a model as a REST API in AWS?

Deploying a model as a REST API in AWS means creating an interface that allows external applications to interact with the model using standard HTTP requests. This setup enables real-time predictions, where an application can send data to the model and receive immediate responses, typically in a JSON format. This is particularly useful for applications that require quick decision-making, such as web or mobile applications that utilize machine learning models for tasks like classification, image recognition, or natural language processing.

The REST API serves as a bridge between the model and other software components, making it easy to integrate machine learning capabilities into existing systems without the need for deep technical knowledge of the model itself. By leveraging commonly used protocols, businesses can streamline the deployment and accessibility of their machine learning models, enhancing agility and responsiveness in their applications.

The other options do not encapsulate the comprehensive functionality of deploying a model as a REST API. For instance, storing the model in S3 refers to a storage solution rather than providing an interface for predictions. Allowing batch prediction processes does not align with the real-time interaction characteristic of a REST API. Visualizing model performance focuses on analyzing the effectiveness of the model rather than providing an interface for predictions.