Fine-Tuning Label Review

Fine-Tuning Label Review is a ml quality workflow that checks annotations for consistency and usefulness for adaptation of a model to a domain. It uses agreement metrics, reviewer queues, and adjudication so teams can improve supervised learning data while keeping evidence, reliability, and public-safe operational boundaries clear.

“The machine learning team used Fine-Tuning Label Review when the fine-tuning run used curated examples, so the team could improve supervised learning data before the model moved into evaluation.”