Название: Establishing AI-Specific Cloud Computing InfrastructureАвтор: Avinash Kumar Sharma, Nitin, Sumit Kumar
Издательство: IGI Global
Год: 2025
Страниц: 654
Язык: английский
Формат: pdf (true), epub
Размер: 30.4 MB
As Artificial Intelligence (AI) continues to drive innovation across industries, the need for specialized cloud computing infrastructure to support AI workloads is critical. Traditional cloud platforms often struggle to meet the high computational demands and storage requirements of AI models, especially as they grow in complexity and scale. Establishing AI-specific cloud computing infrastructure involves designing systems optimized for the needs of AI, such as powerful processing capabilities, massive data storage, and real-time processing. With advancements in hardware like graphics processing units and tensor processing units, along with sophisticated data management solutions, businesses can better harness the full potential of AI technologies. This specialized infrastructure enhances the performance and scalability of AI applications while enabling faster innovation and more efficient deployment of AI-driven solutions across sectors.
Establishing AI-Specific Cloud Computing Infrastructure explores how AI has evolved as a transformative new technology, capable of delivering large incremental value to a wide range of sectors. It examines recent advances in innovation, specifically how computing power, data storage, and digitized data have led to AI-based applications for business and governance. This book covers topics such as digital technology, sustainable development, and artificial intelligence, and is a useful resource for computer engineers, business owners, academicians, data scientists, and researchers.
Cloud computing is changing how businesses operate across different industries by providing flexible and scalable solutions that improve efficiency and productivity. It encourages innovation among users. This paper discusses the various models of cloud delivery and deployment, focusing on their importance in the textile manufacturing sector. It also offers a brief overview of the cloud layered architecture and its key components. While cloud computing has many advantages, there are also challenges and issues that need to be addressed, such as security risks and integration difficulties. Additionally, this paper introduces related concepts like fog computing, edge computing, distributed computing, and grid computing, which enhance traditional cloud computing. Finally, the paper looks at the future applications of cloud technology and suggests areas for further research in the textile industry and other fields.
Automation and orchestration of workloads in the cloud environment are critical for managing the ever-increasing complexity and scale of artificial intelligence applications. This chapter analyses the tools and techniques used to automate and optimize AI workflows in cloud infrastructure. This focus is on the role that cloud orchestration platforms, such as Kubernetes, play in managing distributed workloads and the benefits of automation in scaling, resource management, and fault tolerance. This chapter will discuss the integration of the ML pipeline with cloud services towards streamlined model training, deployment and observation. The discussion puts greater emphasis on the fact that the automation of workload maximises productivity, minimizes downtime while making definite and almost guaranteed high availability in clouds. The case studies reflect the best practices adopted with the most common problems AI workload automation and orchestration face.
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