[ssivark]

> Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry.

The problem is that having a hammer makes one see everything as a nail. Sure, given a suitably clean set of images, anyone who's done a couple of tutorials will be able to apply a pre-trained neural on them to get something.

The hard part is getting an understanding of what tweaks to use when, and when to give up on a method. Otherwise, it is very easy to get carried away and waste time/resources.

For that, one needs to develop a good understanding of the landscape of ML algorithms, why each of them works and how they could break. That typically takes (intensive) experience or an understanding of the theory. Otherwise you'll be doing a brute-force search through a list of possible algorithms. As they say, "a few days in the lab might save a few hours in the library..."

Yes, things can get painful during hiring because the process is broken as it is, with additional complications due to not knowing how to vet for quality in a nascent field. But the "ML elite" are not morons and they don't mean to be obnoxious gatekeepers.