In MLOps, data drift refers to the situation where the statistical properties of input data change over time compared to the data the model was originally trained on, and this is important because even if the model itself hasn’t changed, its performance can silently degrade when real-world data no longer matches the training distribution; for example, user behavior, market conditions, or sensor inputs may evolve, causing the model to make less accurate predictions in production. This directly impacts reliability because the model starts operating on patterns it was not trained for, leading to increased error rates, biased outputs, or unstable predictions. To detect data drift, teams commonly monitor feature distributions over time using statistical tests (like KL divergence or population stability index), compare live data against training baselines, and track performance metrics where ground truth is available; in more advanced setups, automated monitoring tools flag significant deviations in real time. To address it, organizations typically retrain models with fresh data, use continuous training pipelines, apply data validation checks before inference, and sometimes redesign features to be more stable across changing conditions. Overall, monitoring data drift is essential in production ML systems because it ensures the model remains aligned with real-world data and continues to perform reliably over time rather than degrading silently.