Model Drift

The phenomenon where the statistical properties or distribution of data change over time. This shift in data distribution can result in reduced accuracy, degraded performance of machine learning models, and unexpected outcomes. Model drift poses significant challenges in maintaining the effectiveness and reliability of machine learning systems, as models trained on historical data may become less suitable for making accurate predictions in evolving environments. Monitoring and addressing model drift are essential for ensuring the continued effectiveness and relevance of machine learning models in real-world applications.