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May 29, 2026 · 1 min read

How would you design an API for a machine learning model that needs to serve real-time predictions while ensuring scalability and low latency?

The API should follow the REST or gRPC protocol, support asynchronous requests, and use a load balancer to distribute incoming traffic. Caching predictions for frequently requested data can also improve…

debmedia

SOFTWARE_ARCHITECT // AI_ENGINEER

📅 May 29, 2026 ⏱ 1 min read

HW

How would you design an API for a machine learning model that needs to serve real-time predictions while ensuring scalability and low latency?

COVER // HOW WOULD YOU DESIGN AN API FOR A MACHINE LEARNING MODEL THAT NEEDS TO SERVE REAL-TIME PREDICTIONS WHILE ENSURING SCALABILITY AND LOW LATENCY?

The API should follow the REST or gRPC protocol, support asynchronous requests, and use a load balancer to distribute incoming traffic. Caching predictions for frequently requested data can also improve response times and reduce load on the model.

api gRPC machine learning performance scalability

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