In simple terms, model drift in MLOps refers to the situation where a machine learning model’s performance starts to degrade over time because the real-world data it sees in production changes compared to the data it was originally trained on; this is important to monitor because even a well-trained model can become unreliable if user behavior, market trends, or data patterns shift, leading to inaccurate predictions and poor decisions. Model drift typically shows up in two main ways—data drift (changes in input data distribution) and concept drift (changes in the relationship between inputs and outputs)—and both can silently impact performance if not tracked. To detect it, teams usually monitor key metrics like prediction accuracy, data distribution changes, and feature statistics over time, often using dashboards and automated alerts; handling drift effectively involves retraining models with fresh data, validating them before redeployment, and sometimes using techniques like continuous training pipelines or shadow testing. Overall, the key idea is that models are not “set and forget,” and consistent monitoring and updating is essential to keep them reliable in real-world environments.