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Where DevOps and ML Meet - DevOps 156

Adventures in DevOps

CHAPTER

The Importance of Data Engineering

There are no really good ways to track that so you have to have some kind of like end test and mind. You can detect it, but how do you coerce data? That's why we have clinical trials. Like, yeah, no, no, I agree with you. There are no reallyGood tools right now to correct for that. So next you have to be able to verify things usually in a lab or in a clinical trial. And then if I'm working as the data scientist, I might think, hey, we know where the data came from... But over a long duration, things go wrong.

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