Scalable Cloud Architectures for AI-Driven Decision Systems

Abstract

The convergence of Artificial Intelligence and Cloud Computing has revolutionized organizational decisions through a sophisticated infrastructure capable of computationally supporting intensive operations. This article examines architectural frameworks to enable scalable AI-powered decision systems in the cloud environment, focuses on server-free computing paradigms, container orchestration with Kubernetes, multi-cloud-purinogenous strategies, and monotonic work for machine learning workflows. These technologies collectively facilitate increased operational efficiency, real-time analytics capabilities, and strong models serving architecture. Serverless Computing event-in-manufacturing model offers complicated AI pipelines to independently disintegrate into scalable components, while Kubernetes provides an integrated control plane with the required capabilities for AI workloads. Multi-cloud architecture distributes workloads to providers to get better flexibility, geographical distribution, and regulatory compliance. Integration patterns including feature stores, model registries, tracking, and pipeline orchestration enable disciplined MLOps practices that significantly faster the growth and deployment cycles of the model, eventually changing how organizations are implemented and scored in modern cloud ecological systems.