Machine Learning Operations: A Mapping Study
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 65 references that are in the map. First posted on arXiv 28 Sep 2024.
Builds on
16 of 65 references.
References · 16
- 2020Challenges in Deploying Machine Learning: a Survey of Case Studiescited by 151
- 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 126
- 2021Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?cited by 37
- 2021A Data Quality-Driven View of MLOpscited by 31
- 2022MLOps -- Definitions, Tools and Challengescited by 25
- 2020Monitoring and explainability of models in productioncited by 23
- 2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 22
- 2021MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Casescited by 10
- 2022On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOpscited by 8
- 2021SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devicescited by 7
- 2023Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelinescited by 5
- 2024"We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learningcited by 5
4 more
- 2024Maintainability Challenges in ML: A Systematic Literature Reviewcited by 5
- 2021Systematic Mapping Study on the Machine Learning Lifecyclecited by 2
- 2022A Preliminary Investigation of MLOps Practices in GitHubcited by 2
- 2024Challenges and Experiences of Iranian Developers with MLOps at Enterprisecited by 1
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Citing papers per year: 2026: 1.
1 paper.