Active Learning

Active learning addresses a classic ML problem: annotated training data is expensive and slow to produce. Instead of labeling a dataset fully, active learning starts with a small labeled set, trains an initial model, and identifies the examples where the model is uncertain. Those are then prioritized for human annotation.

The effect is measurably significant. Studies show that active learning achieves comparable model quality with 30 to 50 percent fewer labeled examples than classic supervised learning. The relevance is especially high in domains with expensive expertise (medical, industrial, legal), where every annotation effort visibly matters.

Pimcore uses active learning principles in the Agent SDK. In AI-driven product classification, model suggestions with confidence below a threshold are prioritized for review, everything else is accepted automatically. That reduces manual effort to what actually needs human judgment.