Telecom — Real-Time Model Performance Monitoring Pipeline

Overview

The purpose of this DAG is to ensure the effective monitoring of model performance in the telecom industry, focusing on governance and compliance. The architecture consists of a robust data pipeline that ingests metrics and logs from various sources, including operational databases and real-time transaction feeds. The ingestion pipeline begins with collecting data from sources such as network performance logs, customer interaction records, and model inference outputs. Following ingestion, the pr

The purpose of this DAG is to ensure the effective monitoring of model performance in the telecom industry, focusing on governance and compliance. The architecture consists of a robust data pipeline that ingests metrics and logs from various sources, including operational databases and real-time transaction feeds. The ingestion pipeline begins with collecting data from sources such as network performance logs, customer interaction records, and model inference outputs. Following ingestion, the processing steps involve analyzing the data for performance metrics, detecting deviations or biases, and generating alerts based on predefined thresholds. Quality controls are implemented at each stage to ensure data integrity and accuracy. The outputs of this pipeline include performance reports, anomaly detection alerts, and compliance documentation. Key performance indicators (KPIs) such as model accuracy, response time, and alert frequency are monitored continuously to assess the effectiveness of the models in production. The business value of this DAG lies in its ability to enhance operational efficiency, reduce risks associated with model drift, and ensure compliance with regulatory standards, ultimately leading to improved customer satisfaction and trust.

Part of the Governance & Compliance solution for the Telecom industry.

Use cases

• Minimizes risks associated with model drift in telecom operations
• Enhances compliance with industry regulations and standards
• Improves decision-making through timely performance insights
• Increases operational efficiency by automating monitoring processes
• Strengthens customer trust by ensuring reliable model outputs