  Machine learning (ML) components are being added to more and more critical
and impactful software systems, but the software development process of
real-world production systems from prototyped ML models remains challenging
with additional complexity and interdisciplinary collaboration challenges. This
poses difficulties in using traditional software lifecycle models such as
waterfall, spiral, or agile models when building ML-enabled systems. In this
research, we apply a Systems Engineering lens to investigate the use of V-Model
in addressing the interdisciplinary collaboration challenges when building
ML-enabled systems. By interviewing practitioners from software companies, we
established a set of 8 propositions for using V-Model to manage
interdisciplinary collaborations when building products with ML components.
Based on the propositions, we found that despite requiring additional efforts,
the characteristics of V-Model align effectively with several collaboration
challenges encountered by practitioners when building ML-enabled systems. We
recommend future research to investigate new process models, frameworks and
tools 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.
