Tagging articles at scale is bread-and-butter ML, but the easy version fails three ways: it is multi-label, the taxonomy is hierarchical and keeps growing, and rare classes hide behind aggregate accuracy. The signal is the model choice plus how you handle all three.
Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.
Tagging articles at scale is bread-and-butter ML, but the easy version fails three ways: it is multi-label, the taxonomy is hierarchical and keeps growing, and rare classes hide behind aggregate accuracy. The signal is the model choice plus how you handle all three.
No lesson covers this question directly yet. These teach the surrounding topic from the beginning.
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