Defense & Aerospace — Machine Learning Pipeline for Pricing Optimization

Overview

The primary purpose of this DAG is to manage the training and evaluation of machine learning models specifically designed for pricing optimization within the Defense and Aerospace industry. It begins with the ingestion of historical pricing data, competitor pricing, and market demand signals. This data is then processed to create relevant features that inform the machine learning models. The processing steps include data cleaning, feature engineering, model training, and evaluation. Quality cont

The primary purpose of this DAG is to manage the training and evaluation of machine learning models specifically designed for pricing optimization within the Defense and Aerospace industry. It begins with the ingestion of historical pricing data, competitor pricing, and market demand signals. This data is then processed to create relevant features that inform the machine learning models. The processing steps include data cleaning, feature engineering, model training, and evaluation. Quality controls are implemented to monitor model performance and detect drift, ensuring that the models remain accurate over time. The outputs of this pipeline include scoring APIs that provide real-time pricing recommendations based on the latest data. Key performance indicators (KPIs) tracked include model accuracy, precision, recall, and drift metrics. In the event of performance degradation, an automated retraining process is initiated to maintain optimal model performance. This pipeline not only streamlines pricing strategies but also enhances profitability by enabling data-driven decisions in a highly competitive market.

Part of the Pricing Optimization solution for the Defense & Aerospace industry.

Use cases

• Improved pricing strategies leading to increased profitability
• Enhanced decision-making through data-driven insights
• Reduced time-to-market for pricing adjustments
• Increased competitiveness in the Defense and Aerospace sector
• Minimized risk through continuous model evaluation and retraining