[armcat]

The final codebase, yes. But ML is not like traditional software engineering. There is a 99% failure rate, so you are forgetting 100s of hours that go into: (1) surveying literature to find that one thing that will give you a boost in performance, (2) hundreds of notebooks in trying various experiments, (3) hundreds of tweaks and hacks with everything from data pre-processing, to fine-tuning and alignment, to tearing up flash attention, (4) beta and user testing, (5) making all this run efficiently on the underlying infra hardware - by means of distillation, quantization, and various other means, (6) actually pipelining all this into something that can be served at hyperscale

[pk-protect-ai]

> you are forgetting 100s of hours

I would say thousands. Even for the hobby projects, - thousands of GPU hours and thousands of research hours a year.

[karmasimida]

And some luck is needed really.