Architecting cloud-native platforms for predictive enterprise intelligence

Cloud-native platforms have rapidly emerged as the foundation for deploying scalable, modular, and intelligent enterprise systems. When combined with artificial intelligence, these platforms unlock Predictive Enterprise Intelligence (PEI), enabling organizations to anticipate trends, automate decisions, and drive data-driven transformation. This review paper explores the intersection of cloud-native technologies (e.g., Kubernetes, serverless, MLOps) with predictive modeling approaches. It presents block diagrams, architectural patterns, theoretical models, and experimental evaluations from recent literature. The review covers performance metrics such as latency, inference speed, model retraining, and regulatory compliance across domains like healthcare, finance, logistics, and public services. It also highlights emerging research directions, including autonomous MLOps, multi-cloud AI federation, explainable AI integration, and quantum-aware hybrid models. By synthesizing academic and industrial findings, the paper offers a structured foundation for practitioners and researchers aiming to design next-generation predictive platforms.