[danielrpa]

In a previous job my team had to do a lot of ML work with a team full of gatekeepers. I was surprised how easy it was to understand the work even though we were ML newbies - we even implemented some simpler ML algorithms ourselves (long story) which wasn't too hard.

When talking to the PhD boss of the gatekeeper group, he told me that in practice a lot of practical ML work is done with a handful of algorithms and most of what he learned on his PhD is not useful in the industry. And a lot of complexity now sits behind tools (what TFA says).

He was suggesting that, in most applications, a ML expert could be useful as a consultant for a brief period early in the project, but 1) the amateurs wouldn't do too bad by picking algorithms themselves and using the tools for tuning after some study of the fundamentals and 2) in case an expert is consulted, the rest of the team can just run with their suggestions for 90%+ of the duration of the project.