Why High-Accuracy ML Models Often Break Down After Deployment

Machine learning models can achieve impressive accuracy during development yet fail significantly once deployed in real-world environments. A key reason is data drift, where the distribution of incoming data shifts after deployment — for example, when a fraud-detection model trained mostly on mobile transactions suddenly receives a surge of web-based ones. Flawed evaluation practices, such as randomly splitting time-sensitive data instead of using chronological splits, can also make a model appear more reliable than it truly is. Inconsistent preprocessing pipelines — where scaling, encoding, or missing-value handling differs between training and production — can silently corrupt model inputs without any change to the model itself. Additionally, concept drift occurs when the real-world definition of the target variable evolves over time, meaning the model continues solving its original problem even as the actual problem has changed.