Taking Machine Learning Models to Production

Zalik: I think that the a two interesting points that are actually actionable, and you can do them to morrow. One is trying to define what would be a result that satisfies you to define as negative or positive? And how does the rest of the how do the rest tasks that are maybe not as risky or not as data science a related, how do they play with the different result options? Zalik: If you build that into steps, ah, whether it's using one of the data pipe lining tools or just writing it on a piece of paper, that many times provides you with a good framework to break things apart. You know, if your code is written

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