# Studying the Practices of Deploying Machine Learning Projects on Docker

arXiv: https://arxiv.org/abs/2206.00699 · HTML: https://mlsanity.com/paper/2206.00699/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 25 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Built on by (most cited first, 8 of 8)

- [An Empirical Study of Challenges in Converting Deep Learning Models](https://mlsanity.com/paper/2206.14322/) (2022)
- [An Empirical Study on the Usage of Automated Machine Learning Tools](https://mlsanity.com/paper/2208.13116/) (2022)
- [Machine Learning Systems are Bloated and Vulnerable](https://mlsanity.com/paper/2212.09437/) (2022)
- [Detection and Evaluation of bias-inducing Features in Machine learning](https://mlsanity.com/paper/2310.12805/) (2023)
- [An Efficient Model Maintenance Approach for MLOps](https://mlsanity.com/paper/2412.04657/) (2024)
- [Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review](https://mlsanity.com/paper/2504.11079/) (2025)
- [Bridging the Reproducibility Divide: Open Source Software's Role in Standardizing Healthcare AI](https://mlsanity.com/paper/2603.03367/) (2026)
- [ML in a Box: Analyzing Containerization Practices in Open Source ML Projects](https://mlsanity.com/paper/2607.10126/) (2026)

## Abstract (arXiv)

Docker is a containerization service that allows for convenient deployment of websites, databases, applications' APIs, and machine learning (ML) models with a few lines of code. Studies have recently explored the use of Docker for deploying general software projects with no specific focus on how Docker is used to deploy ML-based projects. In this study, we conducted an exploratory study to understand how Docker is being used to deploy ML-based projects. As the initial step, we examined the categories of ML-based projects that use Docker. We then examined why and how these projects use Docker, and the characteristics of the resulting Docker images. Our results indicate that six categories of ML-based projects use Docker for deployment, including ML Applications, MLOps/ AIOps, Toolkits, DL Frameworks, Models, and Documentation. We derived the taxonomy of 21 major categories representing the purposes of using Docker, including those specific to models such as model management tasks (e.g., testing, training). We then showed that ML engineers use Docker images mostly to help with the platform portability, such as transferring the software across the operating systems, runtimes such as GPU, and language constraints. However, we also found that more resources may be required to run the Docker images for building ML-based software projects due to the large number of files contained in the image layers with deeply nested directories. We hope to shed light on the emerging practices of deploying ML software projects using containers and highlight aspects that should be improved.