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Machine Learning (ML) projects incur novel challenges in their developmentand productionisation over traditional software applications, thoughestablished principles and best practices in ensuring the project's softwarequality still apply. While using static analysis to catch code smells has beenshown to improve software quality attributes, it is only a small piece of thesoftware quality puzzle, especially in the case of ML projects given theiradditional challenges and lower degree of Software Engineering (SE) experiencein the data scientists that develop them. We introduce the novel concept ofproject smells which consider deficits in project management as a more holisticperspective on software quality in ML projects. An open-source static analysistool mllint was also implemented to help detect and mitigate these. Ourresearch evaluates this novel concept of project smells in the industrialcontext of ING, a global bank and large software- and data-intensiveorganisation. We also investigate the perceived importance of these projectsmells for proof-of-concept versus production-ready ML projects, as well as theperceived obstructions and benefits to using static analysis tools such asmllint. Our findings indicate a need for context-aware static analysis tools,that fit the needs of the project at its current stage of development, whilerequiring minimal configuration effort from the user.

"Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint