Episode 71 of The Data Science Podcast explores active learning — a technique where models strategically query the most informative data points for human labeling, drastically reducing annotation costs. Lucas and Luna walk through a real-world example from medical imaging: training a diagnostic model to detect lung nodules with 80 percent less labeled data than traditional approaches. They explain query strategies like uncertainty sampling and diversity sampling, discuss when active learning beats random sampling, and touch on integration with weak supervision. The episode also covers a cautionary tale from an e-commerce content moderation project where biased query selection caused drift. By the end, listeners understand the core workflow — train a small initial model, let it pick the next batch of examples for humans to label, retrain, repeat — and know when this loop saves time versus when it doesn't.

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