Machine learning is more like biology, physics or law than like CS or math.
In the software engineering world, we're accustomed to the reality that to do great work, all you need is a sharp mind and a cheap laptop. Better tools help, but aren't really a necessity. You could probably become a top-notch Linux kernel engineer with nothing but dedication and self-study, a $300 laptop and a shabby apartment with internet access.
You just can't really learn ML that way, just like you can't do it with banking, professional trading or medicine.
Kind of sounds like a truism. You make the claim, but you don't explain why. Also, the analogies at the end are... odd.
Maybe you are right! I'd like to hear the justifications more though.
Simply put, I could compile and build from the source of most of the exciting cutting-edge software for free as a hobbyist once I own the computer.
But I can't do the same for things that excite me in this DL domain, even running pretrained models requires hardware that most hobbyists can't afford.
Cloud solutions exist but unlike traditional dev where we could test almost everything about the application locally, only thing unsolved was deployment and distribution in some cases, which is a sensible spend on infrastructure.
Lot of effort and labor, not payed by commercial companies, went into your ability to compile and build from the source of most of the exciting cutting-edge software for free as a hobbyist. For example GCC:
" In late 1983, in an effort to bootstrap the GNU operating system, Richard Stallman asked Andrew S. Tanenbaum, the author of the Amsterdam Compiler Kit (also known as the Free University Compiler Kit), for permission to use that software for GNU. When Tanenbaum advised him that the compiler itself was not free, but rather only the university, Stallman decided to work on a different compiler. "
https://en.wikipedia.org/wiki/GCC
Also the whole reason for existence of GNU Project:
" According to Stallman, what prompted him to start the GNU Project was having previously had an experience with Xerox refusing to give him access to the source code of a laser printer used at the MIT Lab, now with the realization that this community of sharing software would end and noticing the advancing spread of proprietary software which had undone the culture of sharing software that had previously existed, Richard Stallman refused to adapt by continuing with proprietary software, and he instead decided that with this skills as an operating systems programmer, he would begin the development of a new free software operating system and to recreate a community of sharing software that was lost, called GNU. "
https://en.wikipedia.org/wiki/GNU
Weird to put banking and trading on the same complexity level as medicine or ML.
Agreed trading and banking are social systems, more complex that biological systems