Data drift refers to the phenomenon where the distribution of input data changes over time, often leading to decreased performance of machine learning models. Also known as covariate shift, data drift occurs when the patterns or characteristics of the data that a model was trained on no longer reflect the current or real-world data.
This can happen due to various factors, such as changes in user behavior, market trends, or external conditions. Monitoring and detecting data drift is crucial for ensuring that models remain accurate and reliable, requiring periodic retraining or adjustments to adapt to new data patterns.