# On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps

arXiv: https://arxiv.org/abs/2202.03541 · HTML: https://mlsanity.com/paper/2202.03541/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [DeltaGrad: Rapid retraining of machine learning models](https://mlsanity.com/paper/2006.14755/) (2020)
- [Better Code, Better Sharing:On the Need of Analyzing Jupyter Notebooks](https://mlsanity.com/paper/1906.05234/) (2019)
- [Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment](https://mlsanity.com/paper/1903.00278/) (2019)

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

- [Operationalizing Machine Learning: An Interview Study](https://mlsanity.com/paper/2209.09125/) (2022)
- [Angler: Helping Machine Translation Practitioners Prioritize Model Improvements](https://mlsanity.com/paper/2304.05967/) (2023)
- [Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach](https://mlsanity.com/paper/2410.21346/) (2024)
- [Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain](https://mlsanity.com/paper/2406.14758/) (2024)
- [Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges](https://mlsanity.com/paper/2506.02032/) (2025)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [Machine Learning Operations: A Mapping Study](https://mlsanity.com/paper/2409.19416/) (2024)
- [Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis](https://mlsanity.com/paper/2504.20126/) (2025)

## Abstract (arXiv)

Model deployment in machine learning has emerged as an intriguing field of research in recent years. It is comparable to the procedure defined for conventional software development. Continuous Integration and Continuous Delivery (CI/CD) have been shown to smooth down software advancement and speed up businesses when used in conjunction with development and operations (DevOps). Using CI/CD pipelines in an application that includes Machine Learning Operations (MLOps) components, on the other hand, has difficult difficulties, and pioneers in the area solve them by using unique tools, which is typically provided by cloud providers. This research provides a more in-depth look at the machine learning lifecycle and the key distinctions between DevOps and MLOps. In the MLOps approach, we discuss tools and approaches for executing the CI/CD pipeline of machine learning frameworks. Following that, we take a deep look into push and pull-based deployments in Github Operations (GitOps). Open exploration issues are also identified and added, which may guide future study.