What is Model Serving?

Model serving is the process of making a trained machine learning model available to provide predictions on demand. While deployment is about taking the model to production, serving ensures that the model can respond to real-time requests from users or applications efficiently.

Think of it like this:

If deployment is opening a bakery, serving is the waiter who takes the orders, delivers the dishes, and ensures the customers are satisfied.

A model might be deployed on a server, but without proper serving, requests may not be handled efficiently or reliably.

Key Features of Model Serving:

Real-Time Access: Applications can send input data to the model and receive predictions instantly.

Batch Processing: Models can handle large sets of data at once, useful for offline analysis or reporting.

Reliability: The system must ensure consistent predictions under varying loads.

Scalability: The serving infrastructure must handle increasing numbers of requests without degradation.

Example Scenario:

• An e-commerce website uses a recommendation system. Each time a user visits, the model must serve recommendations in milliseconds.

• A fraud detection system evaluates transactions as they happen; serving must be real-time to prevent losses.