Design and Implementation of Scalable Distributed Machine Learning in Multi- Cloud Infrastructures
Abstract
The increasing computational demands of modern artificial intelligence applications have intensified the need for scalable machine learning systems capable of operating efficiently across distributed environments. While single-cloud deployments provide elasticity and computational resources, they are often constrained by vendor dependency, regional limitations, and cost variability. Multi-cloud infrastructures offer enhanced resilience, geographic diversity, and cost optimization; however, they introduce complexities in coordination, synchronization, and distributed training performance. This paper presents the design and implementation of a scalable distributed machine learning architecture specifically engineered for multi-cloud infrastructures. The proposed framework integrates containerized orchestration, adaptive workload scheduling, hybrid parallel training mechanisms, and cross-cloud data management strategies. Experimental evaluation demonstrates significant improvements in training efficiency, scalability, fault tolerance, and operational cost compared to traditional single-cloud deployments. The results confirm that a well-designed multi-cloud distributed ML architecture can provide robust, high-performance, and economically optimized AI infrastructure for enterprise-scale applications
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 8 No. 5 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
17304-17211 |
Published |
September 19, 2025 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr.Vimal Raja Gopinathan (2025). Design and Implementation of Scalable Distributed Machine Learning in Multi- Cloud Infrastructures. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 5 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17304-17211. https://doi.org/10.15662/IJAESIT.2025.0805003 |
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