Scaling AI Applications on the Cloud toward Optimized Cloud-Native Architectures, Model Efficiency, and Workload Distribution

Abstract: The rapid growth of Artificial Intelligence (AI) has increasefd the demand for scalable, efficient, and cost-effective computational infrastructure. Traditional on-premise systems face limitations in scalability, resource allocation, and cost efficiency, making cloud computing a preferred solution. This paper examines cloud-native architectures, including containerization, Kubernetes orchestration, serverless computing, and microservices, as key enablers of AI scalability. Modern approaches for optimizing AI models involve using quantization and pruning and knowledge distillation approaches to make them more efficient without sacrificing their accuracy levels. The paper investigates workload distribution methods like federated learning together with distributed training plus adaptive AI scaling for improving resource efficiency and lowering response times. The implementation continues to face difficulties concerning expense control and latency reduction and scheduling resources efficiently while ensuring security standards. The research presents three possible solutions namely automated AI scaling, edge-cloud integration and provisioning with cost intelligent management systems to overcome current limitations. This examination features a study of present-day trends which consist of AI-native cloud orchestration along with AutoML-based optimization and quantum computing applications for the enhancement of AI scaling capabilities. This research provides comprehensive insights about cloud-based AI scalability which helps researchers as well as practitioners improve their deployment and optimization capabilities of high-performance AI systems.

AI Scalability, Cloud Computing, Cloud-Native Architectures, AI Model Optimization, Workload Distribution

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