Telecom — Machine Learning Model Performance Monitoring Pipeline

Overview

The purpose of the 'Machine Learning Model Performance Monitoring Pipeline' is to ensure the reliability and effectiveness of machine learning models deployed in the telecom sector. Triggered by updates to the models, this DAG ingests various data sources including prediction logs and actual transaction data from telecom operations. The ingestion pipeline begins with the collection of these logs, followed by a series of processing steps designed to compute key performance metrics such as accurac

The purpose of the 'Machine Learning Model Performance Monitoring Pipeline' is to ensure the reliability and effectiveness of machine learning models deployed in the telecom sector. Triggered by updates to the models, this DAG ingests various data sources including prediction logs and actual transaction data from telecom operations. The ingestion pipeline begins with the collection of these logs, followed by a series of processing steps designed to compute key performance metrics such as accuracy, precision, and recall. Additionally, the system performs drift detection to identify any significant changes in model performance over time, which may indicate the need for model retraining or adjustment. Alerts are generated and sent to relevant stakeholders when performance issues are detected, ensuring prompt action can be taken. The outputs of this process are stored in a centralized monitoring system, which is accessible through a user-friendly dashboard that visualizes performance metrics and alerts. Key performance indicators (KPIs) monitored include model accuracy, drift scores, and alert frequency, providing insights into the operational health of the machine learning models. The business value of this DAG lies in its ability to proactively identify and address performance issues, thereby reducing the risk of fraud and enhancing customer trust in telecom services.

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

Use cases

• Increased reliability of machine learning models in production
• Proactive fraud detection and prevention capabilities
• Enhanced decision-making through real-time insights
• Improved customer trust and satisfaction
• Streamlined operations through automated performance monitoring