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Distributed machine learning pipelines in multi-cloud architectures: A new paradigm for data scientists
University of Hull, United Kingdom.
Research Article
International Journal of Science and Research Archive, 2022, 05(02), 357-372.
Article DOI: 10.30574/ijsra.2022.5.2.0049
Publication history:
Received on 17 January 2022; revised on 18 March 2022; accepted on 22 March 2022
Abstract:
In this article, the author captures a comprehensive guide of the distributed MLP in the multi-cloud environment as critical to data scientists. The paper will discuss the existing machine learning pipelines and the realities and potentials of multi-cloud computing; the paper will also explain the propagation, uses, and effective enabling of efficient and scalable machine learning pipelines. The article describes the proper way of customised distribution of pipelines across clouds by using theoretical work, as well as the authors’ observations and examples of the use of distributed machine learning in multi-cloud environments. From the results, it is clear that this new approach can improve data handling, training, and deployment, which can advance the data science domain.
Keywords:
Distributed Machine Learning; Multi-Cloud Architectures; Data Pipelines; Scalability; Data Science; Cloud Computing
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Copyright information:
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
