LEGACY MAINFRAME MODERNIZATION THROUGH MLOPS AND INTELLIGENT DATA ENGINEERING

Abstract

Legacy mainframe systems continue to support critical enterprise operations across banking, healthcare, insurance, government, and financial organizations due to their reliability, security, and high-volume transaction processing capabilities. However, traditional mainframe environments face significant challenges including outdated architectures, limited scalability, complex maintenance processes, restricted integration capabilities, and dependency on specialized legacy programming skills. Modernizing these systems requires intelligent approaches that preserve existing business logic while enabling cloud-native scalability, automation, and advanced analytics capabilities. This paper proposes a Legacy Mainframe Modernization Framework Through MLOps and Intelligent Data Engineering that integrates Machine Learning Operations (MLOps), intelligent data pipelines, cloud computing, automated code transformation, data engineering workflows, and predictive analytics mechanisms. The proposed framework enables systematic migration of legacy applications through automated assessment, data extraction, transformation, model-driven optimization, continuous integration, deployment automation, and intelligent monitoring. MLOps practices ensure reliable management of Machine Learning models and modernization workflows, while intelligent data engineering enables efficient processing, transformation, and integration of legacy enterprise data with modern platforms. Experimental analysis demonstrates that the proposed framework improves modernization efficiency, scalability, data accessibility, operational reliability, and system intelligence compared with traditional migration approaches. The proposed solution provides a scalable, automated, and intelligent foundation for transforming legacy mainframe ecosystems into modern enterprise platforms. Keywords: Legacy Mainframe Modernization, MLOps, Intelligent Data Engineering, Cloud Migration, Machine Learning, Data Transformation, Enterprise Systems, Application Modernization, DevOps Automation, Digital Transformation.