Data Drift refers to the phenomenon where the statistical properties of the input data to a predictive model change over time, causing model performance to degrade. It is a common challenge in machine learning models, specifically those deployed in real-world and ever-changing environments. Since these models are usually trained on historical data, any significant change or divergence in the incoming data compared to the training data used might alter the accuracy of the model’s predictions.
Data drift can occur due to various reasons – it could be caused by changes in the way data is collected, real-world events that influence the data’s nature, or changes in the population’s behavior over time. Depending on the model’s application area and the nature of the data it processes, data drift can be predictable and seasonal, like e-commerce sales patterns, or unforeseen like a global health crisis impacting all types of data.
The concept of data drift underscores an important aspect of machine learning models – their need for continuous monitoring and adaptation post-deployment. Detecting and addressing data drift is crucial for maintaining the model’s prediction quality and relevance over time. Hence, managing data drift becomes an essential part of an organization’s robust model monitoring and management system, enabling it to keep its AI-driven decision-making processes accurate and reliable in the face of changing data landscapes.
« Back to Glossary Index
