One of the most exciting aspects of deep learning’s emergence in computer vision a few years ago was that it didn’t appear to require any feature engineering, unlike previous techniques like histograms-of-gradients or Haar cascades. As neural networks ate up other fields like NLP and speech, the hope was that feature engineering would become unnecessary for those domains too. At first I fully bought into this idea, and saw any remaining manually-engineered feature pipelines as legacy code that would soon be subsumed by more advanced models.
Over the last few years of working with product teams to deploy models in production I’ve realized I was wrong. I’m not the first person to raise this idea, but I have some thoughts I haven’t seen widely discussed on exactly why feature engineering isn’t going away anytime soon. One of them is that even the original vision case actually does rely on a *lot* of feature engineering, we just haven’t been paying attention.
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