Modellmonitoring

Modellmonitoring, also known as model monitoring, is the process of continuously observing and evaluating machine learning models in production. Once a model is deployed, its performance can degrade over time due to various factors such as changes in data distribution, concept drift, or data quality issues. Modellmonitoring aims to detect these degradations and alert stakeholders, enabling timely intervention and model retraining.

Key aspects of modellmonitoring include tracking performance metrics like accuracy, precision, recall, or F1-score. It also involves monitoring data drift, which occurs when the statistical properties of the input data change. Concept drift refers to changes in the underlying relationship between input features and the target variable. Additionally, modellmonitoring often includes monitoring for system-level issues such as latency, error rates, and resource utilization.

The primary goal of modellmonitoring is to ensure that deployed models remain effective and reliable. By proactively identifying performance issues, organizations can avoid negative business impacts, maintain customer trust, and optimize their machine learning investments. Tools and platforms for modellmonitoring often provide dashboards, alerting mechanisms, and automated reporting to facilitate the process. The insights gained from monitoring can also inform future model development and feature engineering efforts.