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.

When an ML system enters production, the data pipeline becomes the true source of risk because real-world inputs rarely behave like training data. Upstream services introduce schema drift, fields start arriving with unexpected formats, and previously clean features suddenly contain outliers, nulls, or delayed events. Even slight deviations an extra categorical value, a missing timestamp, a silent change in data collection logic can cause the model to degrade in ways that are hard to detect from metrics alone. What looked perfectly reliable in development becomes fragile once it relies on external systems that were never built with ML constraints in mind. The model isn’t broken; the world feeding it has changed.