Dataset Embedding Refresh

Dataset Embedding Refresh is a ml index workflow that updates vector representations after source data changes for labeled and unlabeled data used for learning. 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.

“The machine learning team used Dataset Embedding Refresh when the dataset received a new batch, so the team could keep retrieval results current before the model moved into evaluation.”