active learning

Labels are the costly bottleneck in ML, and these two techniques tackle it from different angles. What matters is knowing active learning picks what to label while semi-supervised draws on unlabeled data directly. Here is the answer.

A small labeled set is the normal place to start, not an excuse. The strong answer is a ladder of techniques that pull signal from unlabeled data, pretrained models, and the labeling budget. Here it is.

Labeled data is the fuel for ML, and a labeling platform succeeds or fails 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.