Data Drift Detection

Data drift detection is a machine learning monitoring technique that identifies when the statistical properties of input data change over time, potentially degrading model performance. It involves comparing the distribution of new, incoming data against a reference dataset (e.g., training data) to detect shifts in features, labels, or relationships. This is critical for maintaining the reliability of deployed models in dynamic environments.

Also known as: Concept Drift Detection, Model Drift Monitoring, Data Shift Detection, Distribution Shift, Covariate Shift

Why learn Data Drift Detection?

Developers should learn and use data drift detection when deploying machine learning models in production, especially for applications with evolving data streams like fraud detection, recommendation systems, or financial forecasting. It helps prevent model decay by alerting teams to retrain or update models when data distributions shift due to factors like seasonality, user behavior changes, or external events, ensuring ongoing accuracy and compliance.

Learning Resources

• tutorial: Monitoring Machine Learning Models in Production
• docs: Data Drift Detection with Evidently AI
• course: Coursera: Machine Learning Engineering for Production (MLOps)
• tutorial: A Gentle Introduction to Concept Drift in Machine Learning
• docs: Data Drift Detection in Python with scikit-multiflow