Telecom — Telecom Data Pipeline and Model Performance Monitoring

Overview

The primary purpose of this DAG is to implement a robust monitoring system for data pipelines and machine learning models within the telecom industry. By collecting and analyzing key performance metrics, the DAG facilitates the early detection of anomalies that could impact service delivery. The data sources include telecom network logs, customer usage data, and model performance metrics. The ingestion pipeline begins with the extraction of data from these sources, followed by preprocessing to e

The primary purpose of this DAG is to implement a robust monitoring system for data pipelines and machine learning models within the telecom industry. By collecting and analyzing key performance metrics, the DAG facilitates the early detection of anomalies that could impact service delivery. The data sources include telecom network logs, customer usage data, and model performance metrics. The ingestion pipeline begins with the extraction of data from these sources, followed by preprocessing to ensure data quality and consistency. The processing steps include anomaly detection algorithms that analyze the collected metrics for significant deviations, generating alerts when necessary. Regular performance reports are produced to provide insights into the overall health of the data pipelines and models, focusing on critical KPIs such as response time and availability rates. These KPIs are essential for maintaining service level agreements (SLAs) and ensuring customer satisfaction. The outputs of this DAG include detailed performance reports, alerts for significant anomalies, and dashboards for real-time monitoring. By implementing this monitoring framework, telecom companies can enhance operational efficiency, reduce downtime, and improve customer experience, ultimately driving business value and competitive advantage.

Part of the Scientific ML & Discovery solution for the Telecom industry.

Use cases

• Enhanced operational efficiency through proactive monitoring
• Reduced downtime and improved service availability
• Informed decision-making based on real-time insights
• Increased customer satisfaction with reliable service delivery
• Competitive advantage through optimized resource allocation