Real-Time Model Monitoring

Real-Time Model Monitoring is a process used to continuously track the performance, behavior, and reliability of machine learning models deployed in production. It ensures that models operate correctly by monitoring metrics such as prediction accuracy, data drift, latency, and system health, allowing organizations to detect issues early and maintain trustworthy AI systems.

Features of Real-Time Model Monitoring:

• Continuous tracking of model performance and prediction quality.
• Detection of data drift, concept drift, and anomalies in real time.
• Monitoring of system metrics such as latency, throughput, and errors.
• Alerts and notifications for performance degradation or failures.
• Integration with dashboards, logging systems, and monitoring tools.

To check the version of the Real-Time Model Monitoring tool (Evidently), run the following command:

 $ pip show evidently 

Disclaimer: Real-Time Model Monitoring tools may vary depending on the platform and implementation. Proper configuration of data pipelines, monitoring thresholds, and alerting mechanisms is essential to ensure accurate insights and timely responses. Users are responsible for maintaining data quality, model updates, and system security in production environments.