What is used to detect changes in feature values over time in production?

Drift detection is the process specifically designed to identify changes in the statistical properties of features or data distributions over time in a production environment. Such changes can occur due to various factors, including shifts in the underlying data generation process or changes in user behavior, leading to a phenomenon known as "data drift."

By monitoring and identifying these changes, drift detection enables machine learning models to maintain their effectiveness and relevance. When drift is detected, it can trigger a retraining process or adjustments to the model to ensure that it continues to perform well in predicting outcomes based on the current data characteristics.

In contrast, anomaly detection, while also important in identifying unusual patterns in data, generally focuses on spotting rare events or behaviors that deviate significantly from the norm, rather than systematic changes in feature distributions. Outlier detection shares some similarities but often pertains to isolated data points that don't conform to the expected patterns. Feedback loops refer to the mechanism of using the output from a model to improve future predictions but are not specifically about tracking changes in feature values over time.