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

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research