# A Preliminary Investigation of MLOps Practices in GitHub

arXiv: https://arxiv.org/abs/2209.11453 · HTML: https://mlsanity.com/paper/2209.11453/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 23 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?](https://mlsanity.com/paper/2103.08942/) (2021)
- [Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems](https://mlsanity.com/paper/2103.14101/) (2021)

## Built on by (most cited first, 2 of 2)

- [Machine Learning Operations: A Mapping Study](https://mlsanity.com/paper/2409.19416/) (2024)
- [Good Tools are Half the Work: Tool Usage in Deep Learning Projects](https://mlsanity.com/paper/2310.19124/) (2023)

## Abstract (arXiv)

Background. The rapid and growing popularity of machine learning (ML) applications has led to an increasing interest in MLOps, that is, the practice of continuous integration and deployment (CI/CD) of ML-enabled systems. Aims. Since changes may affect not only the code but also the ML model parameters and the data themselves, the automation of traditional CI/CD needs to be extended to manage model retraining in production. Method. In this paper, we present an initial investigation of the MLOps practices implemented in a set of ML-enabled systems retrieved from GitHub, focusing on GitHub Actions and CML, two solutions to automate the development workflow. Results. Our preliminary results suggest that the adoption of MLOps workflows in open-source GitHub projects is currently rather limited. Conclusions. Issues are also identified, which can guide future research work.