Retail — Retail Model Performance Monitoring Pipeline

Overview

The purpose of this DAG is to continuously monitor the performance of machine learning models deployed in the retail sector, ensuring they operate effectively and deliver accurate predictions. The architecture consists of a data ingestion pipeline that collects performance metrics from various sources, including sales data, customer feedback, and inventory levels. The first step involves gathering real-time data from these sources, followed by the processing stage where metrics such as accuracy,

The purpose of this DAG is to continuously monitor the performance of machine learning models deployed in the retail sector, ensuring they operate effectively and deliver accurate predictions. The architecture consists of a data ingestion pipeline that collects performance metrics from various sources, including sales data, customer feedback, and inventory levels. The first step involves gathering real-time data from these sources, followed by the processing stage where metrics such as accuracy, precision, and recall are calculated. Quality control mechanisms are implemented to detect any drift in model performance, which could indicate that the model is no longer aligned with current data trends. When a drift is detected, alerts are generated to notify relevant teams, enabling prompt corrective actions. The outputs of this DAG include detailed performance reports and visualizations that highlight key performance indicators (KPIs) such as model accuracy over time, drift occurrences, and overall model health. Monitoring these KPIs provides valuable insights into model performance and drives data-informed decision-making. The business value lies in maintaining high model accuracy, improving customer satisfaction, and optimizing inventory management, ultimately leading to enhanced profitability.

Part of the Data & Model Catalog solution for the Retail industry.

Use cases

• Improved model accuracy leading to better sales forecasts
• Faster response to performance issues with automated alerts
• Enhanced customer satisfaction through accurate predictions
• Optimized inventory management based on model insights
• Data-driven decisions supported by comprehensive reporting