Media — Forecast Performance Monitoring for Media Streaming Models

Overview

The purpose of this DAG is to provide ongoing surveillance of the performance of deployed demand forecasting models within the media industry. By continuously monitoring these models, the DAG ensures that any performance issues are promptly identified and addressed. The data sources for this pipeline include historical viewership data, model performance metrics, and real-time streaming analytics. The ingestion pipeline collects these inputs and feeds them into a series of processing steps design

The purpose of this DAG is to provide ongoing surveillance of the performance of deployed demand forecasting models within the media industry. By continuously monitoring these models, the DAG ensures that any performance issues are promptly identified and addressed. The data sources for this pipeline include historical viewership data, model performance metrics, and real-time streaming analytics. The ingestion pipeline collects these inputs and feeds them into a series of processing steps designed to evaluate model accuracy and stability. The processing steps include calculating key performance indicators (KPIs) such as mean absolute error (MAE) and root mean square error (RMSE), comparing current performance against historical benchmarks, and generating alerts when performance drifts beyond acceptable thresholds. Quality controls are implemented to validate the integrity of the incoming data and the outputs generated. The outputs of this DAG include detailed performance reports, alert notifications for model drift, and a historical archive of performance metrics for further analysis. Monitoring KPIs such as alert frequency, drift detection rate, and model accuracy over time are crucial for assessing the effectiveness of the forecasting models. The business value of this DAG lies in its ability to enhance the reliability of demand forecasts, optimize content delivery, and ultimately drive revenue growth by ensuring that media companies can respond swiftly to changing viewer preferences.

Part of the Market & Trading Intelligence solution for the Media industry.

Use cases

• Improved forecasting accuracy leads to better content planning
• Proactive issue detection minimizes revenue loss
• Enhanced decision-making through detailed performance insights
• Increased operational efficiency with automated monitoring
• Strengthened competitive edge in the media market