I'm working on it professionally, but not with huge datasets in a FAANG kind of place, like the MOOCs would have you wish for. Once the ML promises hit the rea

I'm working on it professionally, but not with huge datasets in a FAANG kind of place, like the MOOCs would have you wish for. Once the ML promises hit the real world, where the demand for recognising cats is less acute, and datasets are much smaller (since they're so expensive to curate), it does get less sexy and glitzy. Specifically, I currently work on fraud detection at a government agency, using ML and graph databases. We have some people who do the ML stuff full time, where I'm the back-up and sounding board (as in I'm the senior/mentor) So, I wouldn't have been working there, doing that, if it weren't for my drive to learn on the side, I guess. ML is not my primary extracurricular interest anymore, but it feels good to know that I can code up a neural net, or discuss the tactics of building a model pipeline with (mostly) anyone.