Building ML projects at home is nothing like building a box to building a shed; it's more like building a shed to building a house.
Sure, building a house is more complicated, but you won't be building that house alone, and if someone hired you to build a house because you were good at building sheds, I'm sure you'll start your job as a junior house builder, not master architect.
You're running the team now. Your colleagues have only ever built boxes. Go get em', master architect!
This is my (hopefully humourous but actually taken from my experience) way of saying that companies will often do what is most immediately profitable rather than what's best in the long run (for humanity or themselves).
this is part of the "if you're not unhappy with the first version of your product then you've launched too late" philosophy and it does work in non critical sectors
Of course people learn with experience, and luckily redoing things in software development is cheap compared with when building houses. But that’s the only reason we get away with it.
My point is, we would build better and cheaper systems if we from the start acknowledge that we have to take into account completely different sets of considerations when we move up the scale. A shed isn’t just a big box, a house isn’t just a big shed, etc.
Here in software, we've turned that into a positive thing and made a philosophy out of it. How do you know the toilet shouldn't be in the kitchen? Maybe the users like it? You know what, the data actually shows that in houses where the toilet is next to the kitchen stove, people spend (on average) more time in the living room, thus raising the core metric of happiness.
(a) Bright young man who built a wooden box
(b) Bright young man who says he has read for years about building houses
I'd pick (a) over (b) and put him on a team where they build houses so he actually learns on the job.