This talk on deploying data science models is technically complex and makes me feel like even basic data science is 5-8 years from maturity.
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Google-scale orgs can hire specialists for each part of that process and it's nbd. Most orgs just dump that onto 1 or 2 folks.
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This is an unsustainable approach to obtaining at best marginal gains in customer experience, customer targeting and market knowledge.
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Speaker even just said we're 10 years behind modern software dev practices. I agree.
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and my community (bioinformatics) is resorting to use docker as a crutch here...
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What makes it a crutch, in your view? Most problems w/ bioinf software are about environment management; that's core of Docker.
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well, I think most problems with bioinf software are correctness and reproducibility and software engineering :). That manifests... 1/2
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as environment management problems in the short term, but is symptomatic of deeper problems. Papering it over w/docker != fix.
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I love docker. We are using docker. But still need reliable software!
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Well, true. Docker won't, and can't, do much to paper over low-quality code
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