Article
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Low-Code Machine Learning Platforms: A Fastlane to Digitalization
Version 1
: Received: 16 May 2023 / Approved: 17 May 2023 / Online: 17 May 2023 (10:46:06 CEST)
A peer-reviewed article of this Preprint also exists.
Raghavendran, K.R.; Elragal, A. Low-Code Machine Learning Platforms: A Fastlane to Digitalization. Informatics 2023, 10, 50. Raghavendran, K.R.; Elragal, A. Low-Code Machine Learning Platforms: A Fastlane to Digitalization. Informatics 2023, 10, 50.
Abstract
In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We have developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We have also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms could address. Results showed that automatic machine learning platforms could provide a fast track for organizations seeking digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms could provide a viable option to many business cases and henceforth provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.
Keywords
low-code; no-code; machine learning; auto ML; ML platform; data scientist scarcity; projects overruns
Subject
Computer Science and Mathematics, Information Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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