Active Learning Slashes Human Labeling Costs by Up to 80%
A step‑by‑step guide shows how to set up an active‑learning loop that stops asking for labels once the model’s confidence is high. Using uncertainty sampling and clear stopping criteria, teams can cut annotation effort by 50‑80% while keeping model performance stable.