What are active learning and semi-supervised learning, and when do you use them?
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.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
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.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
