[mpfundstein]

hand-make as in:

- looking very carefully into the very specific challenge of your client

- figuring out how (and if) ML can help

- figuring if its still economically feasible (costs of research vs perceived(!) benefit)

- deriving a solution.

- tinkering tinkering tinkering. usually more with the data than with the models :-)

All my A.I. projects are essentially outsourced R&D projects where we deliver the brain and computing power. So far, it never was as easy like installing YOLO or any other off the shelf product.

Edit: You also need very often custom software to create custom datasets. AI models are often only tested on academic datasets but I observed empirically that their performance transfers badly to real world datasets. So you need to create your own datasets etc. This is often a non-trivial problem. So I wrote a lot of dataset creation tools in my AI practice.