active learning

Labels are the expensive bottleneck in ML, and these two techniques attack it from different angles. The signal is knowing active learning chooses what to label while semi-supervised uses unlabeled data directly. Here is the answer.

Small labeled sets are the normal starting condition, not an excuse. The strong answer is a ladder of techniques that squeeze signal from unlabeled data, pretrained models, and the labeling budget. Here it is.

Labeled data is the fuel for ML, and a labeling platform lives or dies on quality control. The signal is the workflow plus the quality math: consensus, gold honeypots, inter-annotator agreement, and active learning to spend the budget where it counts.