Banking — Performance Monitoring for Deployed Models

Overview

The purpose of this DAG is to monitor the performance of machine learning models deployed in a banking environment. By integrating various data sources such as performance metrics and logs, the DAG facilitates a comprehensive analysis of model effectiveness. The ingestion pipeline begins with the collection of performance metrics from deployed models, followed by the aggregation of logs that capture operational data. The processing steps include analyzing the collected data to identify performan

The purpose of this DAG is to monitor the performance of machine learning models deployed in a banking environment. By integrating various data sources such as performance metrics and logs, the DAG facilitates a comprehensive analysis of model effectiveness. The ingestion pipeline begins with the collection of performance metrics from deployed models, followed by the aggregation of logs that capture operational data. The processing steps include analyzing the collected data to identify performance trends, generating detailed reports that summarize findings, and configuring alerts to detect any deviations or drifts in model performance. Quality controls are implemented to ensure the accuracy of the analysis, while monitoring key performance indicators (KPIs) such as model accuracy, response time, and drift detection rates. The outputs of this DAG consist of performance reports, alert notifications, and a dashboard for real-time monitoring. Additionally, a recovery process is established to maintain continuity in monitoring, ensuring that any failures are promptly addressed. The business value of this DAG lies in its ability to enhance decision-making through data-driven insights, reduce operational risks associated with model performance, and ultimately improve customer satisfaction by ensuring reliable banking services.

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

Use cases

• Enhances operational efficiency through proactive monitoring
• Reduces risks associated with model performance failures
• Improves compliance with regulatory requirements
• Facilitates data-driven decision-making in banking
• Increases customer trust through reliable service delivery