I agree that computational fields are more well-suited to spearhead such approaches, but I don't think machine learning is a good example. ML researchers are constantly pushing at the frontiers of what our current technology can do; consider that a big factor in neural networks coming back into fashion was the ability to throw GPUs at them. The choice of hardware can make a huge difference in outcomes, and some researchers are even using their own hardware (the work being done on half-precision floats comes to mind); any slight overhead will get amplified due to the massive amount of work to be computed; and so on.
Maybe a field that's less dependent on resources would be a better fit. An example I'm familiar with is work on programming languages: typechecking a new logic on some tricky examples is something that should work on basically any machine; bechmarking a compiler optimisation may be trickier to reproduce in a portable way, but as long as it's spitting out comparison charts it doesn't really matter if the speedups differ across different hardware architectures.
When the use of computers is purely an administrative thing, e.g. filling out spreadsheets, drawing figures and rendering LaTeX (e.g. for some medical study), there's no compelling reason to avoid scripting the whole thing and keeping it in git.
Maybe a field that's less dependent on resources would be a better fit. An example I'm familiar with is work on programming languages: typechecking a new logic on some tricky examples is something that should work on basically any machine; bechmarking a compiler optimisation may be trickier to reproduce in a portable way, but as long as it's spitting out comparison charts it doesn't really matter if the speedups differ across different hardware architectures.
When the use of computers is purely an administrative thing, e.g. filling out spreadsheets, drawing figures and rendering LaTeX (e.g. for some medical study), there's no compelling reason to avoid scripting the whole thing and keeping it in git.