# Machine Learning Operations: A Mapping Study

arXiv: https://arxiv.org/abs/2409.19416 · HTML: https://mlsanity.com/paper/2409.19416/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 65 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?](https://mlsanity.com/paper/2103.08942/) (2021)
- [A Data Quality-Driven View of MLOps](https://mlsanity.com/paper/2102.07750/) (2021)
- [Taxonomy of Machine Learning Safety: A Survey and Primer](https://mlsanity.com/paper/2106.04823/) (2021)
- [MLOps -- Definitions, Tools and Challenges](https://mlsanity.com/paper/2201.00162/) (2022)
- [Monitoring and explainability of models in production](https://mlsanity.com/paper/2007.06299/) (2020)
- [MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases](https://mlsanity.com/paper/2103.08937/) (2021)
- [On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps](https://mlsanity.com/paper/2202.03541/) (2022)
- [SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices](https://mlsanity.com/paper/2109.03947/) (2021)
- [Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines](https://mlsanity.com/paper/2306.02421/) (2023)
- ["We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning](https://mlsanity.com/paper/2403.16795/) (2024)
- [Maintainability Challenges in ML: A Systematic Literature Review](https://mlsanity.com/paper/2408.09196/) (2024)
- [Systematic Mapping Study on the Machine Learning Lifecycle](https://mlsanity.com/paper/2103.10248/) (2021)
- [A Preliminary Investigation of MLOps Practices in GitHub](https://mlsanity.com/paper/2209.11453/) (2022)
- [Challenges and Experiences of Iranian Developers with MLOps at Enterprise](https://mlsanity.com/paper/2402.12281/) (2024)

## Built on by (most cited first, 1 of 1)

- [TIMEGATE: Sustainable Time-Boxed Promotion Gates for Continual ML Adaptation Under Resource Constraints](https://mlsanity.com/paper/2605.29183/) (2026)

## Abstract (arXiv)

Machine learning and AI have been recently embraced by many companies. Machine Learning Operations, (MLOps), refers to the use of continuous software engineering processes, such as DevOps, in the deployment of machine learning models to production. Nevertheless, not all machine learning initiatives successfully transition to the production stage owing to the multitude of intricate factors involved. This article discusses the issues that exist in several components of the MLOps pipeline, namely the data manipulation pipeline, model building pipeline, and deployment pipeline. A systematic mapping study is performed to identify the challenges that arise in the MLOps system categorized by different focus areas. Using this data, realistic and applicable recommendations are offered for tools or solutions that can be used for their implementation. The main value of this work is it maps distinctive challenges in MLOps along with the recommended solutions outlined in our study. These guidelines are not specific to any particular tool and are applicable to both research and industrial settings.