Sensor drift is silent. Calibration degrades gradually. By the time you notice, weeks of data are suspect. Industrial ML doesn't fail loudly—it fails quietly, with predictions that slowly lose accuracy.

Build drift detection into the pipeline. Log calibration metadata. Make sensor health visible. The best models can't compensate for bad instrumentation.