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I'm more on the development side of things, but I'm working on similar products right now and I completely agree with your sentiment. What's more, there's usua
by qsort 6y ago
I'm more on the development side of things, but I'm working on similar products right now and I completely agree with your sentiment.
What's more, there's usually a middle ground between spitting data out of postgres and full-on ML models. For example, if you're making forecasts, maybe a simple statistical model will get you 90% of the way there and won't require you to reach out for RNNs or something. In the end it's a tradeoff, and personally I'm delighted when simple math that mostly works can be used in place of complex models with little marginal value.
- pbronez 6y agoThis has been my experience as well. My intuition is that complex models have several pitfalls that undermine their practical value: 1) complex models make more assumptions and may be less robust to the world behaving in unexpected ways. It takes more effort for practitioners to thoroughly review all these assumptions. 2) complex models have more parameters, and thus require more data to train. This limits the scenarios where they can be deployed 3) complex models are harder to interpret, which makes them less useful for convincing people to take action AND increase the likelihood that a practitioner will make an interpretation error Of course, complex models have plenty of advantages too. Under-specified models can be wrong just by failing to account for obvious, well-documented variations. Human judgement is required to select the appropriate model for a given situation.