Banking — Automated Language Model Retraining Pipeline

Overview

The Automated Language Model Retraining Pipeline is designed to enhance the accuracy of language models used in generating summaries within the banking industry. The primary purpose of this DAG is to ensure that language models remain effective and relevant by automatically retraining them based on performance metrics. The data sources for this pipeline include historical model performance logs, user feedback on summary quality, and transaction data that informs model context. The ingestion pipe

The Automated Language Model Retraining Pipeline is designed to enhance the accuracy of language models used in generating summaries within the banking industry. The primary purpose of this DAG is to ensure that language models remain effective and relevant by automatically retraining them based on performance metrics. The data sources for this pipeline include historical model performance logs, user feedback on summary quality, and transaction data that informs model context. The ingestion pipeline collects this data in real-time, allowing for timely analysis and intervention. The processing steps begin with data validation, where the integrity of the input data is checked. Next, performance metrics are analyzed to detect any significant deviations from expected outcomes. If deviations are identified, the retraining process is triggered, utilizing the latest data to refine the models. Following retraining, the new models undergo validation to ensure they meet predefined accuracy thresholds. In the event of a failure during validation, the system automatically rolls back to the previous stable model version, minimizing disruption. The outputs of this DAG include updated language models, performance reports, and validation results, which are essential for maintaining high-quality summary generation. Monitoring KPIs such as model accuracy, retraining frequency, and user satisfaction scores are crucial for assessing the effectiveness of the pipeline. The business value lies in improved operational efficiency, enhanced customer satisfaction through better summaries, and reduced risk associated with outdated models.

Part of the Knowledge Portal & Ontologies solution for the Banking industry.

Use cases

• Increased accuracy in summary generation
• Enhanced customer experience through improved content
• Reduced operational risks with automatic rollbacks
• Efficient use of resources through automated processes
• Continuous model improvement aligned with user needs