Data Drift

Data drift refers to the change in the statistical properties of a dataset over time, which can adversely affect the performance of machine learning models. It occurs when the data used for training a model diverges from the data it encounters in production, leading to decreased accuracy and reliability. Common characteristics of data drift include shifts in feature distributions, changes in relationships between input features and target variables, and the emergence of new patterns. Monitoring for data drift is crucial in maintaining model performance, often involving techniques like statistical tests or machine learning models designed to detect these changes. Use cases for addressing data drift include model retraining, updating data pipelines, and implementing adaptive learning systems.