Yes and no. No question, it makes builds, requirements, and environments more complicated. But it greatly simplifies the process for API end users (the data scientist or analytics engineer). These ppl tend to have deep expertise in how to use/interpret the ML models, or in data general data transformations/modeling, but not in low level computer science. I am in the this camp - but I am learning more about the lower level CS. Outside of the Python ecosystem, the only thing that comes close, wrt data science tooling, is Scala. And then you have to deal with Java bullshit 😂 If Golang, or some other “simple” lower level language (Mojo??) can step up to the plate in data tooling, then I’m sure the industry will follow.

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i'm all about the low level CS stuff, cryptography, protocols, concurrency, i just don't really care frankly, i don't see the use in it, all the stuff i've seen AI do is just garbage because it takes a human brain to recognise quality inputs and thus most of what is in the models is rubbish the hype around ML with regard to programming is way overblown, GPTs generate terrible, buggy code about the only use i see for them might be in automating making commit messages that don't attempt to impute intent and for improving template generation outside of that, it's really boring, you can instantly tell when the text is AI generated and when it's an AI generated image, there is no way it is ever going to get better than the lowest common denominator and everyone is determined to learn that the hard way