Converging Intelligence: A Comprehensive Review of AI and Machine Learning Integration Across Cloud-Native Architectures

Abstract

The incorporation of AI and ML into cloud-native designs has become a hallmark of contemporary computer systems. The scalability, flexibility, and robustness of cloud-native systems are essential for supporting complex AI and ML processes, which are becoming more important as organisations depend on data-driven insight. Focussing on cloud-native settings, this analysis delves into the design, deployment, and management of AI and ML technologies. It pays special attention to microservices, containerisation, orchestration frameworks, and serverless computing models. Using examples like MLOps pipelines, automated scaling, and continuous integration and delivery methods, the research delves into architectural patterns that facilitate efficient model training, deployment, and lifecycle management. Problems with data governance, security, latency, interoperability, and optimising costs in remote cloud environments are also covered in the article. New developments like edge-cloud intelligence, platform-agnostic ML services, and hybrid and multi-cloud AI installations are highlighted in this study that synthesises current scholarly research and industry practices. According to the results, cloud-native designs are crucial for increasing the use of AI quickly without sacrificing operational efficiency or system resilience. Converging intelligence in cloud-native ecosystems presents both potential and constraints, and this review seeks to provide academics and practitioners a thorough knowledge of both.