Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions
Abstract
Background: Developing and maintaining large scale machine learning (ML) based software systems in an industrial setting is challenging. There are no well-established development guidelines, but the literature contains reports on how companies develop and maintain deployed ML-based software systems. Objective: This study aims to survey the literature related to development and maintenance of large scale ML-based systems in industrial settings in order to provide a synthesis of the challenges that practitioners face. In addition, we identify solutions used to address some of these challenges. Method: A systematic literature review was conducted and we identified 72 papers related to development and maintenance of large scale ML-based software systems in industrial settings. The selected articles were qualitatively analyzed by extracting challenges and solutions. The challenges and solutions were thematically synthesized into four quality attributes: adaptability, scalability, safety and privacy. The analysis was done in relation to ML workflow, i.e. data acquisition, training, evaluation, and deployment. Results: We identified a total of 23 challenges and 8 solutions related to development and maintenance of large scale ML-based software systems in industrial settings including six different domains. Challenges were most often reported in relation to adaptability and scalability. Safety and privacy challenges had the least reported solutions. Conclusion: The development and ma…
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[35] noted that areas such as privacy and safety, evaluation, and deployment are missing solutions.
“The authors also identified solutions to some of the challenges but noted that areas such as privacy and safety, evaluation, and deployment are missing solutions.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [35]
The number of papers analyzed in this study (141) is higher than in existing reviews addressing SE aspects.
“The included number of papers in Kumeno (2019), Lwakatare et al. (2020), Nascimento et al. (2020), and this study are 115, 72, 55, and 141, respectively.”
From A software engineering perspective on engineering machine learning systems: State of the art and challenges (2021) · cites this paper as Lwakatare et al. (2020)
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- Authors
- Lwakatare, L. E., Raj, A., Crnkovic, I., Bosch, J., & Olsson, H. H.
- Affiliations
- Chalmers University of Technology (SE)
- Malmö University (SE)
- Corresponding author
- Lwakatare, Lucy Ellen
- Venue
- Information and Software Technology
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 127
- Pages
- 106368
- ISSN
- 0950-5849 (ISSN-L)
- OpenAlex
- W3039522238
- Cited by
- 241
- References
- 72
- Citations by year
- 2026: 37 · 2025: 78 · 2024: 48 · 2023: 32 · 2022: 27 · 2021: 14 · 2020: 5
- FWCI
- 18.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information and Software Technology (publishedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- VINNOVA
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Keywords
- industrial software development, software maintenance, adaptability, scalability, safety, privacy, data acquisition, model deployment, development guidelines
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