LEGACY MAINFRAME MODERNIZATION THROUGH PRODUCTION MACHINE LEARNING AND AUTOMATED MODEL GOVERNANCE

Abstract

Legacy mainframe systems continue to support mission-critical enterprise operations across banking, insurance, healthcare, and government sectors because of their reliability and transactional consistency. However, these systems often face challenges related to scalability, integration, operational agility, and limited support for modern artificial intelligence technologies. Production Machine Learning (ML) combined with automated model governance offers an effective modernization pathway that preserves valuable business logic while enabling intelligent decision-making and continuous optimization. This paper proposes a comprehensive modernization framework that integrates legacy mainframe applications with cloud-native MLOps pipelines, automated governance mechanisms, feature engineering, model monitoring, and continuous deployment practices. The proposed methodology emphasizes secure API integration, metadatadriven governance, explainable artificial intelligence, automated compliance validation, and continuous performance monitoring throughout the model lifecycle. Experimental evaluation demonstrates improvements in prediction accuracy, deployment efficiency, governance compliance, operational scalability, and infrastructure utilization. The proposed approach enables organizations to modernize legacy environments while minimizing operational risks and ensuring sustainable enterprise AI adoption. Keywords— Legacy Mainframe Modernization, Production Machine Learning, MLOps, Automated Model Governance, Explainable AI, Enterprise AI, Digital Transformation, Cloud Integration.