Serving - How a Model Starts “Talking to the World”

You’ve trained a model? Great. But now comes the stage where it needs to start responding to real people. This is where Serving comes in - the way to turn a trained model into a live service.

What is Serving?

When we talk about “Serving,” we mean the stage where the model:

• Is loaded into memory (like a running application).
• Listens for requests (e.g., “Give me a prediction”).
• Returns a response (quickly and accurately).

It’s essentially the service that allows any other system to use the model - through a simple API.

Two Types of Serving

Real-Time Serving

When you need an answer now. For example: A user asks a chatbot → the model responds immediately. Focus: Response speed (Latency).

Batch Serving

When processing large amounts of data at once. For example: Updating predictions for all users once a day. Focus: Efficiency and high Throughput.

Why is This Important?

Because a great model without Serving - is just a nice file. Serving is what turns it into part of a real system, serving people, applications, and organizations.

How Does It Work in Practice?

• API Layer - Receives requests from the outside (e.g., via HTTP or gRPC).
• Model Engine - Performs the actual computation (CPU or GPU).
• Scheduler / Load Balancer - Distributes requests to prevent system overload.
• Cache - Stores repeated results to avoid recalculating.

Conclusion

Serving is the stage where AI becomes a product. Without it, the model stays in the lab. With it - it talks, responds, and delivers real value.