I spent 10 years transitioning academics' work to more production-ready environments. It involved a lot of rewriting code.
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There are tools that make it *possible* to productionize this work. But "is possible" isn't the benchmark for "is a best practice."
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I'm really hoping that the next few years of datasci can include an evolution towards better engineering support.
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"Go read docs to figure out which library this routine is in" is fine for academic stuff but does not fly for engineering work.
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Imagine if core file IO functions were spread across 5 different libraries. That's the status of datasci right now.
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There is just no good reason for PCA to be in scikit-learn and SVD to be in numpy. They're the same damn thing modulo a symmetrization op.
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I've wondered the same thing. Any hints of an effort to rationalize the ecosystem?
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But once used in a production environment they become the production tool delivered. Good or bad. POC always becomes product.
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