Data drift, also known as covariate shift, is a phenomenon in which the statistical properties of input data change over time, creating a disparity between the data used for training machine learning models and the data encountered during deployment. This drift can significantly impair model accuracy as the underlying assumptions become outdated, necessitating continuous monitoring and updating of models to maintain reliability. Factors contributing to data drift include evolving user behaviors, seasonal variations, changes in data sources or preprocessing methods, and data quality issues. Effective detection involves monitoring data quality, model performance, and using statistical tests to identify shifts in data distributions. Tools like Encord Active can aid in detecting and managing data drift by offering features such as data distribution analysis, quality metrics assessment, model evaluation, and active learning for adaptive model updates. These strategies ensure that models remain effective and accurate in dynamic environments.