Also much of it, in fact most of it, does not work on neural networks or other “black box” unsupervised models.
The most common uses of ML for a business or researcher are correlation, categorization, and recommendation systems, or some form of forecasting/predictive modeling (I probably missed some). None of these require a neural net, or anything that resembles what we commonly call “AI”.
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yeah, and much of it is calculus based, i've done a lot of work with difficulty adjustment which uses historical samples to create close estimates of the current hashpower running on a network
i'm not a data scientist though, my area of specialisation is more about protocols and distributed consensus - and the latter (and including spam prevention) tend to involve statistical analytical tools
There’s also the whole range of analytics use cases that does not need any form of ML. The Python ecosystem has extremely robust tooling that makes this work easy. Rust tooling for dataframes etc is getting there, but it’s not reasonable to expect all analysts to learn Rust.
Does Golang have any libraries for dataframes and ad-hoc analysis, that works in something like a Jupyter notebook?
as i mentioned elsewhere, my thing is protocols and distributed consensus, and go is the best language for this kind of work, and it is what the language was built to do, it's just my assertion that it generally makes for better quality code IF PEOPLE FOLLOW THE IDIOM unlike way too many go coders in the bitcoin/lightning/nostr space
