Model Drift

Model drift refers to the phenomenon where a machine learning model's performance deteriorates over time due to changes in the underlying data distribution. This can occur because of shifts in user behavior, external factors, or changes in the environment where the model is deployed. The main characteristics of model drift include a decrease in accuracy, increased error rates, and the need for model retraining or updates to maintain performance. Common use cases include monitoring predictive models in finance, healthcare, and marketing, where data patterns can evolve rapidly. Organizations need to implement strategies to detect and mitigate model drift to ensure their AI systems remain effective and reliable.