Scalable machine learning model deployment using serverless cloud architectures
1 Andhra University, INDIA.
2 Department of Information Technology.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2022, 05(01), 087-101.
Article DOI: 10.30574/wjaets.2022.5.1.0025
Publication history:
Received on 20 December 2021; revised on 24 January 2022; accepted on 28 January 2022
Abstract:
Implementations of the developed ML models are related to important questions of scale, resource, and management. This work investigates the use of serverless cloud models to address these issues and enhance and optimize the deployment, scalability, and maintenance of ML models. Reviewing main serverless platforms and their compatibility with different ML development and usage stages, the research compares the effectiveness, cost, and adaptability to the common application deployment practices. The study in this paper employs case and performance analysis to show and explain how serverless solutions can cut infrastructure costs and reduce the need for scaling and maintenance, among others. This paper outlines guidelines for implementing serverless technologies in ML applications and areas of concern that organizations might expect. Consequently, this research adds to the existing literature on deploying ML-based applications in the cloud while providing useful findings for developers and organizations interested in efficient, cost-effective solutions.
Keywords:
Serverless Computing; Machine Learning; Cloud Deployment; Scalability Solutions; Cost Efficiency; Performance Optimization; AI Integration; Predictive Maintenance; NLP Chatbots; CI/CD Pipelines
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0