International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 2 March-April 2024 Submit your research before last 3 days of April to publish your research paper in the issue of March-April.

Cost-effective Cloud Architectures for Large-scale Machine Learning Workloads

Author(s) Lavanya Shanmugam, Kumaran Thirunavukkarasu, Kapil Kumar Sharma, Manish Tomar
Country United States
Abstract The optimization of cloud infrastructure for real-time AI processing presents a critical challenge and opportunity for organizations seeking to leverage machine learning (ML) at scale. This paper explores the strategies, case studies, and ethical considerations associated with achieving cost-effective cloud architectures for large-scale ML workloads. By examining real-world examples from leading cloud providers and international perspectives, we identify best practices and future directions for organizations navigating the complexities of cloud-based ML deployments.
Keywords Cloud computing, Machine learning, Optimization, Real-time processing, Cost-effectiveness, Case studies, Ethical considerations, Scalability, AI governance.
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-01
Cite This Cost-effective Cloud Architectures for Large-scale Machine Learning Workloads - Lavanya Shanmugam, Kumaran Thirunavukkarasu, Kapil Kumar Sharma, Manish Tomar - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.16093
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.16093
Short DOI https://doi.org/gtpw64

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