The honest version: deep learning learns its own features but is hungry for data and compute, and on a customer's 50,000-row tabular dataset a gradient-boosted tree wins on accuracy, cost, and time-to-ship. The dividing line and the conditions that flip it.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
The honest version: deep learning learns its own features but is hungry for data and compute, and on a customer's 50,000-row tabular dataset a gradient-boosted tree wins on accuracy, cost, and time-to-ship. The dividing line and the conditions that flip it.
The screen is whether the candidate frames the difference around feature extraction (hand-crafted features for classical ML versus learned representations for deep nets) rather than reciting 'deep learning has more layers.' The strong move is to default to classical ML for tabular/small-data customer problems and reserve neural nets for unstructured data at scale. The reserved follow-up: 'the customer has 5,000 rows of CRM data and wants deep learning, what do you say': the answer is gradient-boosted trees, and a candidate who recommends a neural net here fails.
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