I know very little about the DL/ML space, but as a full-stack engineer it feels like most companies have tried to replicate what FAANG companies do (heavy inves

I know very little about the DL/ML space, but as a full-stack engineer it feels like most companies have tried to replicate what FAANG companies do (heavy investment in data/ml) when the cost/benefit simply isn't there. Small companies need to frame the problem as: 1) Do we have a problem where the solution is discrete and already solved by an existing ML/DL model/architecture? 2) Can we have one of our existing engineers (or a short-term contractor) do transfer learning to slightly tweak that model to our specific problem/data? Once that "problem" actually turns into multiple "machine learning problems" or "oh, we just need todo this one novel thing", they will probably need to bail because it'll be too hard/expensive and the most likely outcome will be no meaningful progress. Said in another way: can we expect an engineer to get a fastai model up and running very quickly for our problem? If so, great - if not, then bail. ie: the solution for most companies will be having 1 part-time "citizen data scientist" [1] on your engineering team. [1]: https://www.datarobot.com/wiki/citizen-data-scientist/ https://www.datarobot.com/wiki/citizen-data-scientist/