High Tech — Recommendation Model Performance Monitoring System

Overview

The primary purpose of this DAG is to establish a robust monitoring framework for recommendation models within the high-tech industry. It begins by ingesting performance metrics from various data sources, including user interaction logs, model prediction outputs, and historical performance data. The ingestion pipeline is designed to ensure data integrity and timeliness, automatically collecting relevant metrics at predefined intervals. The processing steps involve calculating drift and bias metr

The primary purpose of this DAG is to establish a robust monitoring framework for recommendation models within the high-tech industry. It begins by ingesting performance metrics from various data sources, including user interaction logs, model prediction outputs, and historical performance data. The ingestion pipeline is designed to ensure data integrity and timeliness, automatically collecting relevant metrics at predefined intervals. The processing steps involve calculating drift and bias metrics, comparing them against established KPIs, and generating alerts when deviations are detected. Quality controls are implemented to validate the accuracy of the metrics, ensuring that any anomalies are promptly addressed. The outputs of this DAG include detailed performance reports, alert notifications, and a historical audit log for compliance and future analysis. Monitoring KPIs such as model accuracy, precision, and recall are continuously tracked, providing insights into model performance over time. This monitoring system delivers significant business value by minimizing operational disruptions, enhancing model reliability, and fostering continuous improvement in recommendation strategies.

Part of the Customer Personalization solution for the High Tech industry.

Use cases

• Improved reliability of recommendation systems
• Reduced risk of operational disruptions
• Enhanced ability to detect and address biases
• Facilitated compliance through audit trails
• Increased trust in AI-driven recommendations