Model Drift Embedding Refresh

Model Drift Embedding Refresh is a ml index workflow that updates vector representations after source data changes for changes in model performance over time. 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 Model Drift Embedding Refresh when the live population changed, so the team could keep retrieval results current before the model moved into evaluation.”