When I Drift, You Drift, We Drift

In the context of machine learning observability, "drift" refers to changes in the statistical properties of data or models over time. Model drift occurs when a model's predictions change without any modification to the underlying model itself. Concept drift is characterized by shifts in the statistical properties of the target variable, while data drift involves changes in the independent variables and their correlations. Upstream drift results from alterations in the data pipeline that can lead to missing values or changes in feature cardinality. Monitoring and diagnosing these various forms of drift are crucial for maintaining optimal model performance and mitigating future performance degradation. Arize is an ML observability platform designed to help teams manage model performance, monitor drift, and troubleshoot issues in production environments.