Modellüberwachung

Modellüberwachung refers to the systematic process of tracking and evaluating machine learning models after deployment. This practice ensures models maintain their predictive accuracy and performance over time in production environments. The process involves monitoring various metrics such as prediction accuracy, error rates, and response times to detect any anomalies or degradation in performance.

A key aspect of Modellüberwachung is detecting data drift and concept drift, where the statistical properties of input data or the relationship between inputs and outputs change over time. When such drifts are identified, teams can decide whether to retrain the model or adjust its parameters to maintain effectiveness.

Effective model monitoring also involves establishing automated alerts for significant performance deviations and conducting regular audits. These practices help organizations maintain the reliability of their AI systems and prevent potential business impacts from model failures.

By implementing Modellüberwachung as part of their MLOps practices, organizations can ensure their machine learning solutions continue to deliver value while minimizing risks associated with model degradation or unexpected behavior.