  Machine Learning (ML) projects incur novel challenges in their development
and productionisation over traditional software applications, though
established principles and best practices in ensuring the project's software
quality still apply. While using static analysis to catch code smells has been
shown to improve software quality attributes, it is only a small piece of the
software quality puzzle, especially in the case of ML projects given their
additional challenges and lower degree of Software Engineering (SE) experience
in the data scientists that develop them. We introduce the novel concept of
project smells which consider deficits in project management as a more holistic
perspective on software quality in ML projects. An open-source static analysis
tool mllint was also implemented to help detect and mitigate these. Our
research evaluates this novel concept of project smells in the industrial
context of ING, a global bank and large software- and data-intensive
organisation. We also investigate the perceived importance of these project
smells for proof-of-concept versus production-ready ML projects, as well as the
perceived obstructions and benefits to using static analysis tools such as
mllint. Our findings indicate a need for context-aware static analysis tools,
that fit the needs of the project at its current stage of development, while
requiring minimal configuration effort from the user.
