Survey on Cloud Computing Integrated with Artificial Intelligence

Abstract

The combination of cloud computing with Artificial Intelligence (AI) has become a viable approach in recent years to handle the increasing processing needs of AI applications. This article offers a thorough analysis of distributed and scalable artificial intelligence frameworks that use cloud computing to improve the effectiveness and performance of Deep Learning (DL). First, we provide a summary of common cloud services and AI frameworks, emphasizing the advantages and disadvantages of each. The important topics of data management and storage in cloud-based AI systems are then covered in detail, including feature engineering, data pretreatment, privacy, and security. Next, we investigate the distributed and parallel training methods for AI models, with particular emphasis on cloud-based training topologies, communication mechanisms, and model splitting. On the next chapters, we examine auto-scaling, load balancing, resource allocation, and performance benchmarking as optimization methodologies for AI workloads on the cloud. We also cover serverless deployment alternatives, containerization, best practices for monitoring, and the deployment and servicing of AI models on the cloud. We give a comprehensive study of costs, optimization methodologies, and case studies exhibiting successful installations to guarantee the cost-effectiveness of cloud-based AI systems. In conclusion, we provide an overview of the study's main conclusions, go through the difficulties and restrictions associated with cloud-based artificial intelligence, and point out new directions for future research in this area. For academics and practitioners trying to use cloud computing to create scalable, effective, and affordable artificial intelligence systems, this article  will be a great resource.