Feature Embedding Refresh

Borrador de traduccion automatica (Spanish) for "Feature Embedding Refresh": Feature Embedding Refresh is a ml index workflow that updates vector representations after source data changes for input signals used by a machine learning model. It uses batch jobs, backfills, and index validation so teams can keep retrieval results current while keeping evidence, reliability, and public-safe operational boundaries clear.

“Ejemplo en borrador: The machine learning team used Feature Embedding Refresh when a feature distribution shifted, so the team could keep retrieval results current before the model moved into evaluation.”