This shows up when a model that looked solid in testing suddenly starts behaving unpredictably in production, not because the logic is wrong, but because the data feeding it has changed. A field goes missing, a service updates a schema without notice, timestamps arrive late, or default values silently shift and the model keeps making predictions anyway. Nothing crashes, but confidence slowly erodes as outputs drift from what teams expect. Over time, you realize the real issue isn’t model performance, it’s that ML systems are only as stable as the weakest data pipeline they depend on.