High Tech — Model Performance Monitoring and Alerting Pipeline

Overview

The primary purpose of this DAG is to establish a robust monitoring framework for machine learning models in production within the high-tech industry. It collects various performance metrics and logs from deployed models, ensuring that they operate within expected parameters. The ingestion pipeline begins with gathering data from model performance logs, system health checks, and user interaction metrics. These inputs are then processed through a series of transformation steps, which include data

The primary purpose of this DAG is to establish a robust monitoring framework for machine learning models in production within the high-tech industry. It collects various performance metrics and logs from deployed models, ensuring that they operate within expected parameters. The ingestion pipeline begins with gathering data from model performance logs, system health checks, and user interaction metrics. These inputs are then processed through a series of transformation steps, which include data validation, anomaly detection, and performance drift analysis. Quality controls are implemented to ensure the accuracy of the collected data, allowing for real-time monitoring of model performance. The outputs of this DAG are comprehensive performance reports, alert notifications for any detected issues, and a real-time dashboard that visualizes key performance indicators (KPIs). Monitoring KPIs include model accuracy, response time, and error rates, which are crucial for maintaining compliance and governance standards. The business value of this DAG lies in its ability to provide actionable insights, enabling organizations to proactively address performance issues, enhance model reliability, and ensure compliance with industry regulations.

Part of the Fraud & Anomaly Analytics solution for the High Tech industry.

Use cases

• Ensures compliance with industry regulations and standards
• Enhances model reliability through continuous monitoring
• Reduces downtime by proactively addressing performance issues
• Improves decision-making with actionable insights
• Facilitates better resource allocation and management