A Machine Learning–Enabled Approach to Adaptive Cloud Database Modernization

The rapid evolution of cloud computing technologies has created significant challenges for organizations attempting to modernize legacy database systems. This research presents an innovative framework for adaptive cloud database modernization using artificial intelligence-enhanced decision models. Traditional migration approaches often fail due to inadequate assessment of workload characteristics, cost implications, and performance requirements. Our proposed AI-enhanced decision model incorporates machine learning algorithms to analyze historical database usage patterns, predict migration outcomes, and recommend optimal modernization strategies. The framework was validated through implementation in three enterprise environments, demonstrating an average 34% reduction in migration time and 28% cost savings compared to conventional approaches. Key findings indicate that AI-driven decision models can accurately predict post-migration performance with 89% accuracy and identify potential compatibility issues before they impact production systems. The research contributes a practical methodology for organizations undertaking cloud database modernization initiatives, offering data-driven insights that reduce risks and optimize resource allocation. This work bridges the gap between theoretical cloud migration frameworks and practical implementation challenges faced by database administrators and cloud architects.