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Serverless Machine Learning Framework for Efficient Training and Deployment of Models Across Multiple Cloud Platforms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 55 |
| Year of Publication: 2024 |
| Authors: Balaji Thadagam Kandavel |
10.5120/ijca2024924270
|
Balaji Thadagam Kandavel . Serverless Machine Learning Framework for Efficient Training and Deployment of Models Across Multiple Cloud Platforms. International Journal of Computer Applications. 186, 55 ( Dec 2024), 14-19. DOI=10.5120/ijca2024924270
Abstract
The rise of serverless computing has revolutionized the deployment and scaling of applications, including machine learning (ML). Traditional cloud-based ML systems often incur high costs, complexity in scaling, and infrastructure management. Serverless computing offers a simplified alternative, abstracting the underlying infrastructure to reduce operational overhead. This paper proposes a serverless machine learning framework that enables efficient training and deployment of ML models across multiple cloud platforms such as AWS Lambda, Google Cloud Functions, and Azure Functions. The framework optimizes the allocation of compute resources dynamically based on workload, significantly reducing both time and cost for training and inference processes. We implemented the framework using Kubernetes for container orchestration, and applied it to various machine learning tasks, including image classification and natural language processing. Results demonstrate up to 45% cost savings and a 50% reduction in deployment time compared to traditional cloud setups. We conclude that a serverless ML framework provides scalable, cost-effective, and reliable solutions for ML operations while simplifying infrastructure management across cloud platforms.
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