How to Detect Data Drift Before It Reduces Machine Learning Model Performance
A machine learning model can achieve strong results during training and testing, yet become less reliable after deployment. One reason is that real-world data rarely stays the same forever. Customer preferences change. New products enter the market. User behaviour shifts between devices. Data pipelines get updated. When the data reaching a model starts to differ from the data it learned from, the model may face patterns it was not designed to handle. This is where data drift monitoring becomes useful.