[dtech]

Yes! It's one tool in the software engineering toolbox! It's a great tool for some problems!

Due to the hype it becomes a goal in some organizations however. "We need to do machine learning because we have big data" or some such. Doesn't matter if the problem could've been solved in 5% of the time and cost with 20 lines of code, thou shalt use machine learning.

It doesn't help that data scientists (creating and training the ML model) and software developers (creating and maintaining the software) usually come from different backgrounds, requiring a "data engineer" as an additional intermediary.

It always a problem with hype, blockchain (or merkle trees) has the same problem but worse, because the problems it solves well are rarer and more narrow.

[dchichkov]

To me, it seem to be larger than one tool. I think of it as a color in a pallete, with which one can paint software. Octarine.

To put this statement into context, I'm speaking as someone who had been writing code in C, from the era of PC XT. Perhaps NIPS 2010 was a rite of passage to ML for me. There is a screen, full of industry grade C++ and PyTorch in front of me, right now...