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Mrs. Anusha Deshpande is an Assistant Professor in the Department of Electronics and Communication Engineering at Gurunanak Institute of Technology, Hyderabad, Telangana, India. Her research interests include Artificial Intelligence, IoT, embedded systems, cloud computing, and intelligent communication technologies. She actively participates in research related to emerging digital technologies and smart systems.
Cloud computing has become a pivotal technology for the large-scale implementation of Artificial Intelligence (AI)-based prediction applications. The integration of cloud infrastructure, Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), Big Data Analytics, and edge computing allows organisations to handle extensive amounts of real-time data, facilitating astute prediction and decision-making in sectors such as healthcare, manufacturing, finance, agriculture, transportation, smart cities, education, and industrial automation. In contrast to conventional on-premises computer systems, cloud-based AI infrastructures offer nearly boundless computational capabilities, flexible storage, decentralised processing, automated model implementation, and enhanced availability. These features provide scalable predictive analytics, smart automation, and instantaneous decision-making assistance, all while decreasing infrastructure expenses and enhancing operational efficacy. This chapter elucidates the principles, structure, cloud service paradigms, AI frameworks, predictive applications, obstacles, and prospective research trajectories of cloud-centric AI frameworks for scalable predictive applications.