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Yes, exactly. And most of the time, you can get by with a simple model like a random forest, so you just need to have a few analysts/engineers doing feature eng
by fnbr 9y ago
Yes, exactly. And most of the time, you can get by with a simple model like a random forest, so you just need to have a few analysts/engineers doing feature engineering, and feeding it into your model.
Almost everyone using more complicated architectures (e.g. neural networks) are adding unnecessary complexity. [1]
[1]: Unless you're doing computer vision.
- e_ameisen 9y agoAgreed, we've also written about why the best use of your time is to start with a baseline (even in Computer Vision!) https://blog.insightdatascience.com/always-start-with-a-stupid-model-no-exceptions-3a22314b9aaa https://blog.insightdatascience.com/always-start-with-a-stup...
- throwaway84742 9y agoAnd before a baseline, start with formulating a problem in a way that’s applicable to a real world product. Not just “image classification” but “image classification to improve QA in underwater basket weaving, a 10 billion dollar industry, from which we could generate $100m/yr in revenue by selling a product that does A, B, and C to an estimated 100k customers each of which would have $1000 LTV”. I’m in an AI startup right now where the founders are hell bent on doing nothing in particular, and I’ll be bailing in a month or so.
- didgeoridoo 9y agoI'd love to hear about your experiences — email in my profile. I've been doing some writing and speaking on why AI seduces founders into pursuing lots of cool ideas instead of marketable products. I'd love another data point. Could do it anonymously if you'd like, or hold off until after you bail.
- deleted 9y ago[deleted]