Logging is a common practice in traditional software development. There have been multiple studies on the characteristics of logging in traditional software systems such as C/C++, Java, and Android applications. However, logging practices in Machine Learning-based (ML-based) applications are still not well understood. The size and complexity of data and models used in ML-based applications present unique challenges for logging. In this paper, we aim to bridge this knowledge gap and provide insight into the logging practices in ML-based applications, making the first attempt to characterize current logging practices within a large number of open-source ML-based applications. We conducted an empirical study on 502 open-source ML applications to understand their logging practices, combining quantitative and qualitative analyses and a survey involving 31 practitioners. Our quantitative analysis reveals that logging in ML applications is less common than in traditional software, with info and warn log levels being popular. Top ML-specific logging libraries include MLflow, Tensorboard, Neptune, and W&B. Qualitatively, logging is used for data and model management, especially in model training. Our survey reinforces the importance of logging in experiment tracking, complementing our qualitative findings. Our research carries significant implications. It reveals distinctive ML logging practices compared to traditional software. We have highlighted the prevalence of general-purpose logging libraries in ML code, indicating a potential gap in awareness regarding ML-specific logging tools. This insight benefits researchers and developers aiming to enhance ML project reproducibility and sets the stage for exploring ML-specific logging tools’ impact on machine learning system quality and trustworthiness.