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Docker is a containerization service that allows for convenient deployment ofwebsites, databases, applications' APIs, and machine learning (ML) models witha few lines of code. Studies have recently explored the use of Docker fordeploying general software projects with no specific focus on how Docker isused to deploy ML-based projects.  In this study, we conducted an exploratory study to understand how Docker isbeing used to deploy ML-based projects. As the initial step, we examined thecategories of ML-based projects that use Docker. We then examined why and howthese projects use Docker, and the characteristics of the resulting Dockerimages. Our results indicate that six categories of ML-based projects useDocker for deployment, including ML Applications, MLOps/ AIOps, Toolkits, DLFrameworks, Models, and Documentation. We derived the taxonomy of 21 majorcategories representing the purposes of using Docker, including those specificto models such as model management tasks (e.g., testing, training). We thenshowed that ML engineers use Docker images mostly to help with the platformportability, such as transferring the software across the operating systems,runtimes such as GPU, and language constraints. However, we also found thatmore resources may be required to run the Docker images for building ML-basedsoftware projects due to the large number of files contained in the imagelayers with deeply nested directories. We hope to shed light on the emergingpractices of deploying ML software projects using containers and highlightaspects that should be improved.

Studying the Practices of Deploying Machine Learning Projects on Docker