I wonder if the rules of continuous improvement apply to ML models. It seems that regressions are very easy to pick up. And that with a complex task like FSD it

I wonder if the rules of continuous improvement apply to ML models. It seems that regressions are very easy to pick up. And that with a complex task like FSD it is virtually impossible to use the established tools to safeguard the stack from regressions (i.e. test cases that assert some unbroken functionality).