Synchronous vs Asynchronous Serving

Serving strategies can be broadly categorized as synchronous or asynchronous, depending on how predictions are requested and delivered.

1. Synchronous Serving:

The client sends a request and waits for the model to return a response immediately.

Suitable for real-time applications where low latency is critical.

Example: An online chatbot generating responses instantly.

Advantages:

Simple to implement.

Immediate feedback for end-users.

Challenges:

High traffic can overload the system if not scaled properly.

Each request blocks resources until the response is returned.

2. Asynchronous Serving:

The client sends a request, and the model processes it in the background, returning results later.

Suitable for batch processing or tasks where immediate results aren’t required.

Example: Generating daily sales forecasts or processing large datasets offline.

Advantages:

Can handle large workloads efficiently.

Reduces system blocking and allows queuing of requests.

 Challenges:

Slightly more complex to implement.

Users do not get immediate results.