It's hard to find articles like this that give a glimpse into what is used by larger shops doing ML. I take this one with a grain of salt due to the source being a vendor, but still it is generous with the amount of detail and with its even mentioning some alternative solutions for cases that might fit those, so that is really appreciated.

The pros working in big shops who write these tend to overlook the tiny use cases such as apps that recognize a cat coming through a cat door (as opposed to a raccoon) which can get by with minuscule training.

There's a lot of discussion of "big data" but small data is amazingly powerful too. I wish there was more bridging of these two worlds — to have tools that deal with the needs of small data, without the assumption that training a model takes days or months, and on the other side, to have the big data world share more insights about how they manage their data for the big cases. There is a ton of info out there but what I find lacking is info about how labeling and tagging is managed on a large scale (I'm interested in both, big and small, as well as medium). Maybe I'm just missing something. This article gave some good clues — thanks!