Building machine learning (ML) models is an involved process requiring ML experts, engineers, and labelers. The demand of models for common-sense tasks far exceeds the available "teachers" that can build them. We approach this problem by allowing domain experts to apply what we call Machine Teaching (MT) principles. These include mining domain knowledge, concept decomposition, ideation, debugging, and semantic data exploration.
PICL is a toolkit that originated from the MT vision. It enables teachers with no ML expertise to build classifiers and extractors. The underlying SDK enables system designers and engineers to build customized experiences for their problem domain. In PICL, teachers can bring their own dataset, search or sample items to label using active learning strategies, label these items, create or edit features, monitor model performance, and review and debug errors, all in one place.