What is it about?
Interdisciplinary collaboration challenges start to emerge when Machine learning (ML) components are being added to more and more critical software. This paper applies a Systems Engineering lens to investigate the use of V-Model in addressing the interdisciplinary collaboration challenges when building ML-enabled systems.
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Why is it important?
We have shown for the first time that despite requiring additional efforts, the characteristics of V-Model align effectively with several collaboration challenges encountered by practitioners when building ML-enabled systems.
Perspectives
I hope this article will encourge more researchers to investigate new process models that leverage the characteristics of V-Model such as the system decomposition, clear system boundary, and consistency of Validation & Verification (V&V) for building ML-enabled systems.
Jie Wu
University of British Columbia
Read the Original
This page is a summary of: An Exploratory Study of V-Model in Building ML-Enabled Software: A Systems Engineering Perspective, April 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3644815.3644951.
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