Active Learning

Labeling data is one of the biggest bottlenecks in production ML. A single labeled example can cost anywhere from $0.01 (simple text classification) to $50+ (expert medical annotation). Active Learning is a strategy that helps you get the most value from limited labeling by letting the model choose which data points should be labeled next.

Instead of labeling data at random, the model actively identifies the most informative or uncertain examples. This leads to faster learning with fewer labeled samples, reducing both cost and effort.