Inference Hyperparameter Sweep

Rascunho de traducao automatica (Portuguese) for "Inference Hyperparameter Sweep": Inference Hyperparameter Sweep is a ml optimization process that searches over model settings to improve a target metric for model prediction serving. It uses bounded search spaces, trial tracking, and early stopping so teams can find better configurations while keeping evidence, reliability, and public-safe operational boundaries clear.

“Exemplo em rascunho: The machine learning team used Inference Hyperparameter Sweep when the endpoint handled burst traffic, so the team could find better configurations before the model moved into evaluation.”