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Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

Bentley Oakes, Michalis Famelis and Houari Sahraoui

Article (2023)

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Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain expert faces in addressing their problem with a computational workflow, and the underlying executable implementation. These challenges arise out of our conceptual framework which presents the "route" of transformations that a domain expert may choose to take while developing their solution.

To ground our conceptual framework in the state-of-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for the transformations described in our framework. Example studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation.

The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges and transformations are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.

Department: Department of Computer Engineering and Software Engineering
PolyPublie URL: https://publications.polymtl.ca/57010/
Journal Title: ACM Transactions on Software Engineering and Methodology
Publisher: Association for Computing Machinery
DOI: 10.1145/3638243
Official URL: https://doi.org/10.1145/3638243
Date Deposited: 25 Jan 2024 15:04
Last Modified: 26 Jan 2024 16:59
Cite in APA 7: Oakes, B., Famelis, M., & Sahraoui, H. (2023). Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice. ACM Transactions on Software Engineering and Methodology, 48 pages. https://doi.org/10.1145/3638243


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