Public Sector — Automated Machine Learning Model Update Pipeline

Overview

The purpose of this DAG is to streamline the process of updating machine learning models to adapt to new data, thereby maintaining their predictive performance in the public sector. The architecture includes a series of well-defined steps that begin with ingesting fresh data from various sources such as citizen feedback forms, service usage logs, and demographic databases. Once the data is ingested, it undergoes processing steps that involve data cleaning, feature extraction, and model retrainin

The purpose of this DAG is to streamline the process of updating machine learning models to adapt to new data, thereby maintaining their predictive performance in the public sector. The architecture includes a series of well-defined steps that begin with ingesting fresh data from various sources such as citizen feedback forms, service usage logs, and demographic databases. Once the data is ingested, it undergoes processing steps that involve data cleaning, feature extraction, and model retraining. Evaluation metrics are calculated to assess model accuracy and performance against predefined KPIs, such as precision rates and update duration. If the new model fails to meet performance standards, a rollback mechanism is triggered to revert to the previous version, ensuring continuity of service. The outputs of this DAG include updated model artifacts, performance reports, and notifications for stakeholders. Continuous monitoring of KPIs provides insights into model performance over time, enabling proactive adjustments. The business value of this DAG lies in its ability to enhance service delivery by ensuring that predictive models remain relevant and effective, ultimately leading to improved citizen satisfaction and operational efficiency.

Part of the Customer Personalization solution for the Public Sector industry.

Use cases

• Improved accuracy of predictive models for public services
• Increased efficiency in model management and updates
• Enhanced citizen satisfaction through personalized services
• Reduced risk of model failure with rollback capabilities
• Proactive adjustments based on continuous performance monitoring