Defense & Aerospace — Machine Learning Model Performance Monitoring Pipeline

Overview

The primary purpose of this DAG is to continuously monitor the performance of machine learning models used in supply and demand forecasting within the Defense and Aerospace industry. It ingests data from various sources, including model prediction outputs, historical performance metrics, and operational data. The ingestion pipeline collects these inputs and feeds them into a series of processing steps that analyze precision and recall metrics. The processing logic includes drift detection algori

The primary purpose of this DAG is to continuously monitor the performance of machine learning models used in supply and demand forecasting within the Defense and Aerospace industry. It ingests data from various sources, including model prediction outputs, historical performance metrics, and operational data. The ingestion pipeline collects these inputs and feeds them into a series of processing steps that analyze precision and recall metrics. The processing logic includes drift detection algorithms that identify when model performance deviates from established thresholds. If drift is detected, alerts are generated, and retraining processes are scheduled to recalibrate the models. The outputs of this DAG include detailed performance reports, alerts for model drift, and retraining schedules, which are delivered to an analyst console for enhanced visibility. Key performance indicators (KPIs) monitored include model accuracy, latency, and the frequency of drift events. This real-time monitoring capability provides significant business value by ensuring that forecasting models remain accurate and reliable, ultimately leading to improved decision-making and resource allocation in the Defense and Aerospace sector.

Part of the Supply/Demand Forecast solution for the Defense & Aerospace industry.

Use cases

• Enhanced decision-making through accurate forecasting
• Reduced operational risk by identifying model drift early
• Improved resource allocation based on reliable predictions
• Increased confidence in automated systems and models
• Streamlined processes for model maintenance and retraining