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• Concept Drift; Monitoring Model Quality in Streaming Machine Learning Applications

Concept Drift; Monitoring Model Quality in Streaming Machine Learning Applications

Abstract

Most machine learning algorithms are designed to work on stationary data. Yet, real-life streaming data is rarely stationary. Models lose prediction accuracy over time if they are not retrained. Without model quality monitoring, retraining decisions are suboptimal and costly. Here, we review the monitoring methods and evaluate them for applicability in modern fast data and streaming applications.