Retail — Ecommerce Model Performance Monitoring Pipeline

Overview

The purpose of this DAG is to continuously monitor the performance of predictive models used in retail ecommerce, enabling timely interventions to maintain model accuracy and reliability. The architecture consists of a data ingestion pipeline that collects metrics from various sources, including model performance logs, drift detection metrics, and bias analysis reports. The processing steps involve aggregating these metrics, analyzing them for anomalies, and generating alerts when performance th

The purpose of this DAG is to continuously monitor the performance of predictive models used in retail ecommerce, enabling timely interventions to maintain model accuracy and reliability. The architecture consists of a data ingestion pipeline that collects metrics from various sources, including model performance logs, drift detection metrics, and bias analysis reports. The processing steps involve aggregating these metrics, analyzing them for anomalies, and generating alerts when performance thresholds are breached. Quality control mechanisms are in place to ensure the integrity of the data being monitored, including automated checks for data completeness and accuracy. The outputs of this DAG include a comprehensive dashboard that visualizes key performance indicators (KPIs) such as response time and alert rates, facilitating quick decision-making. Furthermore, the DAG is designed to automatically restart in case of failure, ensuring continuous monitoring without manual intervention. Monitoring KPIs are crucial for assessing the effectiveness of predictive models, providing insights into their operational health. The business value of this DAG lies in its ability to enhance model reliability, reduce downtime, and ultimately improve customer satisfaction through accurate predictions.

Part of the Fraud & Anomaly Analytics solution for the Retail industry.

Use cases

• Improves predictive accuracy leading to better inventory management
• Reduces operational risks through timely issue detection
• Enhances customer satisfaction with reliable service delivery
• Facilitates data-driven decision-making across retail operations
• Optimizes resource allocation by identifying underperforming models