Description: Data drift refers to the change in data distribution over time, which can significantly impact the performance of machine learning models. This phenomenon occurs when the data used to train a model no longer adequately represents the current reality, leading to a decrease in the model’s accuracy and effectiveness. Drift can be caused by various factors, such as changes in user behavior, seasonal variations, or the introduction of new products and services. It is crucial for data scientists and machine learning engineers to monitor and adjust their models to mitigate the effects of data drift, ensuring they remain relevant and accurate in their performance. Early detection of this drift allows for adjustments in the models, such as retraining with more recent data or implementing adaptation techniques that help maintain the model’s robustness against changes in data distribution.
