Scaling ML Model Inference through architectural separation

The model is only one part of the inference pipeline. At scale, the work happening around it, from routing requests to coordinating downstream dependencies, can have a much bigger impact on latency and efficiency. We addressed this by separating model execution from request orchestration into dedicated Hosting and Serving layers.Look at how most teams start serving a model. There is one service.…

This is an excerpt. The full article continues at CRED.