Which service allows integrating a trained model into an application for predictions?

The process of making a trained model available for predictions is accomplished through endpoint deployment. This service enables developers to integrate the model into applications, allowing them to generate predictions in real-time based on new input data. Once the model is deployed to an endpoint, applications can make HTTP requests to this endpoint, passing the necessary data, and receiving predictions in response. This capability is crucial for operationalizing machine learning models and embedding them into business processes or user-facing applications.

In contrast, model training involves the process of teaching the model using historical data, while data preparation focuses on cleaning and transforming the raw data before it is used for training. Model evaluation is concerned with assessing the performance of the trained model to ensure it meets desired accuracy and operational metrics. These processes are essential for creating and refining a predictive model, but they do not directly facilitate integrating the model into applications for making predictions.