Current Volume 8
Harnessing Advanced Cloud Computing for the Scalable Deployment of Generative AI: Enhancing Computational Efficiency and Real-Time Applications
Abstract
Natural language processing, computer vision, and healthcare, finance, and entertainment industries rank Generative Artificial Intelligence (AI) as a cornerstone of their businesses. Unfortunately, with the complexity of the AI models growing and their scale, computing resources are needed to train them and keep them running high. By promoting scalability, affordability, and efficiency of deployable models, advanced architectures such as serverless, multi-cloud, and edge computing represent a rung on the ladder to overcoming the above challenges cloud computing provides. In this paper, we show how the deployment of generative AI can be expedited by leveraging advanced cloud computing architectures for computational efficiency, real-time applications, and scalability. In this work, we look at some cloud computing paradigms and discuss their pros and cons for deploying AI systems. Real-world use cases and experiment results of generative AI show how cloud technology can overcome the haywire needs of generative AI and serve to accelerate the innovation cycle and operate more efficiently. We also present a framework for executing scalable AI solutions on cloud infrastructures, tackling key issues such as latency, data privacy, and cost optimization.
Keywords
Generative AI, Cloud computing, Scalability, Real-time application, Computational efficiency
Citations
IRE Journals:
Thejaswi Adimulam
"Harnessing Advanced Cloud Computing for the Scalable Deployment of Generative AI: Enhancing Computational Efficiency and Real-Time Applications" Iconic Research And Engineering Journals Volume 7 Issue 1 2023 Page 628-638
IEEE:
Thejaswi Adimulam
"Harnessing Advanced Cloud Computing for the Scalable Deployment of Generative AI: Enhancing Computational Efficiency and Real-Time Applications" Iconic Research And Engineering Journals, 7(1)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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