  Continuous Integration (CI) is a well-established practice in traditional
software development, but its nuances in the domain of Machine Learning (ML)
projects remain relatively unexplored. Given the distinctive nature of ML
development, understanding how CI practices are adopted in this context is
crucial for tailoring effective approaches. In this study, we conduct a
comprehensive analysis of 185 open-source projects on GitHub (93 ML and 92
non-ML projects). Our investigation comprises both quantitative and qualitative
dimensions, aiming to uncover differences in CI adoption between ML and non-ML
projects. Our findings indicate that ML projects often require longer build
durations, and medium-sized ML projects exhibit lower test coverage compared to
non-ML projects. Moreover, small and medium-sized ML projects show a higher
prevalence of increasing build duration trends compared to their non-ML
counterparts. Additionally, our qualitative analysis illuminates the
discussions around CI in both ML and non-ML projects, encompassing themes like
CI Build Execution and Status, CI Testing, and CI Infrastructure. These
insights shed light on the unique challenges faced by ML projects in adopting
CI practices effectively.
