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Writing Clean, Production-Level ML Code with Laszlo Sragner

ML Platform Podcast

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What Are Your Best Resources for Data Scientists to Learn How to Write Clean Code?

The most important concept is relatively high level dependency inversion. You pretty much only need three design patterns to get started, which are adapter, factory, and strategy. If you can treat some kind of feature of your code base as an external resource, then you don't need to worry about what's behind this. Layers of obstruction problem decomposition, bounded context thinking that I am going to organize my work in a way that at the same time, I am only worrying about one part of the system. These are all coming down into this part.

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