Machine Learning Operations
Abstract
Machine learning operations (MLOps) is DevOps for machine learning processes. MLOps enables data scientists to collaborate and increase the pace of delivery and quality of model development through monitoring, validation, and governance of machine learning models. This is equivalent to how DevOps helps software engineers develop, test, and deploy software quicker and with fewer defects. MLOps supports the data science life cycle just as DevOps supports the application development life cycle. As such, MLOps is based on the principles of DevOps.
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MLOps is a subset of SE for ML and a superset/extension of DevOps, focusing on adopting DevOps practices when developing and operating ML systems.
“DevOps for Machine Learning, named MLOps, is a subset of SE for ML and a superset/extension of DevOps, focusing on adopting DevOps practices when developing and operating ML systems [50].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [50]
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Sorted by citations
MLOps - Definitions, Tools and Challenges
Symeonidis, G., Nerantzis, E., Kazakis, A., & Papakostas, G. A. (2022) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc54503.2022.9720902 · Cited by 143 · see citing works
“At first, Akshita Goyal [1] provides a general overview of MLOps, thereupon Yizhen Zhao [2] reviews the academic literature around ML in production in order to define the importance of MLOps.”
MethodSteidl, M., Felderer, M., & Ramler, R. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111615 · Cited by 126 · see citing works
A road map for planning-deploying machine vision artifacts in the context of industry 4.0
Silva, R. L., Canciglieri, O., & Rudek, M. (2021) · Journal of Industrial and Production Engineering · doi:10.1080/21681015.2021.1965665 · Cited by 40 · see citing works
Operationalizing machine learning models: a systematic literature review
Kolltveit, A. B., & Li, J. (2022) · Proceedings of the 1st Workshop on Software Engineering for Responsible AI · doi:10.1145/3526073.3527584 · Cited by 34 · see citing works
“DevOps for Machine Learning, named MLOps, is a subset of SE for ML and a superset/extension of DevOps, focusing on adopting DevOps practices when developing and operating ML systems [50].”
A Systematic Review of Machine Learning DevOps
Mboweni, T., Masombuka, T., & Dongmo, C. (2022) · 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet55527.2022.9872968 · Cited by 34 · see citing works
“…not covered or poorly covered include: MLOps governance, guidelines and manifestos to guide MLOps projects, and case studies to showcase how [31]; Soh and Singh (2020) [12]; Varon (2021) [32] Yes - ML-DevOps is understood to be a paradigm that consists of its own best practices, a development…”
BackgroundAlzheimer’s Disease Assessments Optimized for Diagnostic Accuracy and Administration Time
McCombe, N., Ding, X., Prasad, G., Gillespie, P., Finn, D. P., Todd, S., McClean, P. L., Wong‐Lin, K., & Initiative, A. D. N. (2022) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2022.3164806 · Cited by 22 · see citing works
“In the spirit of machine learning model operationalisation management (MLOps) [47], [48], we hope our GUI tool can help bridge the gap between machine learning research and other communities and stakeholders, especially clinicians, health economists and policy-makers.”
Tran, D. X., Dominati, E., Lowry, J. H., Mackay, A. D., Vibart, R. E., Pearson, D. M., Devantier, B. P., & Noakes, E. (2023) · Soil Use and Management · doi:10.1111/sum.12966 · Cited by 16 · see citing works
“For the selection of soil samples to train the machine learning models, we cleaned and de-noized the data which may significantly affect the model's performance (Soh & Singh, 2020).”
MethodMartin, V., Rouas, J.-L., Philip, P., Fourneret, P., Micoulaud‐Franchi, J., & Gauld, C. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.926286 · Cited by 13 · see citing works
“Regarding ML models, when they are put into production, the only parameter that could be set regarding their degree of freedom is the choice to fine train the model with the new samples, and if so, the importance to give to them (54, 56).”
BackgroundMethodAdvancing MLOps from Ad hoc to Kaizen
John, M. M., Gillblad, D., Olsson, H. H., et al. (2023) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa60479.2023.00023 · Cited by 6 · see citing works
Impact Factors and Best Practices to Improve Effort Estimation Strategies and Practices in DevOps
Meedeniya, D., & Thennakoon, H. (2021) · International Conference on Information Communication and Management (ICICM) · doi:10.1145/3484399.3484401 · Cited by 7 · see citing works
“MLOps, which stands for Machine Learning Operation, brings the machine learning models that are developed and tested in remote settings to a production system of a DevOps environment [38].”
BackgroundIterative Minimum Viable Product Approach to Implementing AI, RPA, and BI Solutions
Srivastava, R. M. (2021) · Westcliff International Journal of Applied Research · doi:10.47670/wuwijar202151rs · Cited by 3 · see citing works
Cost Effective Generic Machine Learning Operation: A Case Study
Jain, S., & Kumar, P. (2023) · International Conference on Data Science and Network Security (ICDSNS) · doi:10.1109/icdsns58469.2023.10245408 · Cited by 3 · see citing works
Data science: Trends, perspectives, and prospects
(2022) · Data Science and Informetrics · doi:10.59494/dsi.2022.3.1 · Cited by 2 · see citing works
Design Methods and Processes for ML/DL models
John, M. M. (2021) · doi:10.24834/isbn.9789178771998 · Cited by 4 · see citing works
Machine Learning Operations: A Mapping Study
Chakraborty, A., Das, S., & Gary, K. A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-86644-9_1 · Cited by 5 · see citing works
Datenqualität für Datenservices
Colombo, M. (2023) · Springer eBooks · doi:10.1007/978-3-662-67556-4_9
Vásquez, F., Lagos, J., Gutiérrez-Gutiérrez, F., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13167-6_23
- Authors
- Soh, J., & Singh, P.
- ORCID
- Singh, Priyanshi
- Venue
- Data Science Solutions on Azure
- Publisher
- Apress
- Published
- 2020
- Pages
- 259-279
- OpenAlex
- W3116142344
- Cited by
- 20
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 5 · 2022: 6 · 2021: 5 · 2019: 1
- FWCI
- 10.68
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Apress eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- MLOps, DevOps, model monitoring, model validation, model governance, machine learning deployment
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