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I agree we need to start from an mvp and so with a data model, but does this model really need to be AI or ML? My counter point to your counter point is yes, we
by kfk 6y ago
I agree we need to start from an mvp and so with a data model, but does this model really need to be AI or ML? My counter point to your counter point is yes, we need a model, but probably the initial one can be done with simple BI/SQL type aggregation methods. Most of AI/ML I see is optimizing an already existing model, not create something completely new.
- disgruntledphd2 6y agoI'd suggest using a lasso, tbh. It's pretty fast, relatively performant and one can interpret the coefficients. That being said, I'd do your simple SQL aggregations first, to ensure that one can actually learn something (if we have 0.0001% positives, a simple approach probably isn't going to work well).
- Ntrails 6y agoA few years back the team that do "Business analysis" came to our team asking for help setting up some machine learning. What for, we asked. Well, we'd like to predict hardware expenditures over the next n years. Ok. Well that sure seems like a thing the business might like an estimate of! Sure. Next question: Have you run a basic regression over the data you've got, maybe a simple model would be close enough to throw some error bars up and get going. Of course not.
- kfk 6y agoYou see "we'd like to predict hardware expenditures over the next n years" for me is a red flag. When I hear those types of requests I hear "we'd like to shift responsibility and ownership of our financial estimates to someone else".