The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Engineering AI Systems: A Research Agenda
Abstract
Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry. However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this chapter, the authors provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that they have studied. The main contribution of the chapter is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.
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In the citing paper
“Achieving a low inference latency and high throughput is widely reported as a challenge in serving ML models [6,7,12,28,34].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“Edge deployments are often reported as being more challenging than cloud deployments because of hardware resource heterogeneity, the typically distributed nature of edge devices, and possibly varying network connection quality [7,10,20,22,39,53].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“Serving-related challenges are some of the ones most often reported in the literature [5-7, 9, 11, 12, 16, 20, 28, 34, 44, 53, 54], usually from a performance perspective.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“The authors also highlight the integration of data-driven models with computationdriven software components as a potentially non-trivial task.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“[7] reported the extensive use of glue code as a challenge with which companies struggle.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“Monitoring ML models is challenging [6,28,41], but is required in order to detect performance degradations and build confidence in the models [7,10].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“[7] and [42] reported a lack of generic solutions for deploying ML models to embedded and edge devices.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
Citations
Cites
Building Theories from Case Study Research
Eisenhardt, K. M. (1989) · The Academy of Management Review · doi:10.2307/258557 · Cited by 17,455
(1996) · Cited by 11,584
Interpretive case studies in IS research: nature and method
Walsham, G. (1995) · European Journal of Information Systems · doi:10.1057/ejis.1995.9 · Cited by 3,834
Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars.
Maguire, M., & Delahunt, B. (2017) · All Ireland Journal of Higher Education · doi:10.62707/aishej.v9i3.335 · Cited by 2,099
Software Engineering for Machine Learning: A Case Study
Amershi, S., Begel, A., Bird, C., DeLine, R. A., Gall, H. C., Kamar, E. S., Nagappan, N., Nushi, B., & Zimmermann, T. (2019) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip.2019.00042 · Cited by 1,401
Designing Qualitative Research
Flick, U. (2018) · SAGE Publications Ltd eBooks · doi:10.4135/9781529622737 · Cited by 955
(2015) · neural information processing systems · Cited by 912
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
Hatcher, W. G., & Yu, W. (2018) · IEEE Access · doi:10.1109/access.2018.2830661 · Cited by 654
A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks
Klaine, P. V., Imran, M. A., Onireti, O., & Souza, R. D. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2727878 · Cited by 494
Software Engineering Challenges of Deep Learning
Arpteg, A., Brinne, B., Crnkovic-Friis, L., & Bosch, J. (2018) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa.2018.00018 · Cited by 238
Self-citationLarge-scale machine learning at twitter
Lin, J., & Kolcz, A. (2012) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2213836.2213958 · Cited by 231
Lwakatare, L. E., Raj, A., Bosch, J., Olsson, H. H., & Crnkovic, I. (2019) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-030-19034-7_14 · Cited by 214
Self-citationMachine learning applied to software testing: A systematic mapping study
Durelli, V. H. S., Durelli, R. S., Borges, S. S., Endo, A. T., Eler, M. M., Dias, D. R. C., & Guimaraes, M. P. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2019.2892517 · Cited by 207
Deep learning in the automotive industry: Applications and tools
Luckow, A., Cook, M., Ashcraft, N., Weill, E., Djerekarov, E., & Vorster, B. (2016) · 2016 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2016.7841045 · Cited by 164
Data Management Challenges for Deep Learning
Munappy, A. R., Bosch, J., Olsson, H. H., Arpteg, A., & Brinne, B. (2019) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa.2019.00030 · Cited by 140
Self-citationCascade Ranking for Operational E-commerce Search
Liu, S., Xiao, F., Ou, W., & Si, L. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098011 · Cited by 111
150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com
Bernardi, L., Mavridis, T., & Estevez, P. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330744 · Cited by 95
Novel Applications of Machine Learning in Software Testing
Briand, L. (2008) · doi:10.1109/qsic.2008.29 · Cited by 78
From Ad-Hoc Data Analytics to DataOps
Munappy, A. R., Mattos, D. I., Bosch, J., Olsson, H. H., & Dakkak, A. (2020) · International Conference on Software and System Process · doi:10.1145/3379177.3388909 · Cited by 73
Self-citationA Survey of Software Quality for Machine Learning Applications
Masuda, S., Ono, K., Yasue, T., & Hosokawa, N. (2018) · IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) · doi:10.1109/icstw.2018.00061 · Cited by 71
Practical considerations in deploying AI for defect prediction
Tosun, A., Turhan, B., & Bener, A. (2009) · doi:10.1145/1540438.1540453 · Cited by 63
It takes three to tango: Requirement, outcome/data, and AI driven development
Bosch, J., Olsson, H. H., & Crnković, I. (2018) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 38
Self-citationJohn, M. M., Olsson, H. H., & Bosch, J. (2020) · International Conference on Software and System Process · doi:10.1145/3379177.3388892 · Cited by 38
Self-citationAI on the Edge: Architectural Alternatives
John, M. M., Olsson, H. H., & Bosch, J. (2020) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa51224.2020.00015 · Cited by 34
Self-citationA critical look at the relationship between AI and software engineering
Tsai, W., Heisler, K. G., Volovik, D., & Zualkernan, I. A. (2003) · IEEE Workshop on Languages for Automation · doi:10.1109/lfa.1988.24945 · Cited by 31
On the Challenges of Translating NLP Research into Commercial Products
Dahlmeier, D. (2017) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p17-2015 · Cited by 28
Research works printed but not linked: 1
- R. A.Munappy, 2020, Modeling Data Pipelines., Proceedings of the Euromicro Conference on Software Engineering and Advanced Applications (SEAA)
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Metadata
- Authors
- Bosch, J., Olsson, H. H., & Crnkovic, I.
- Affiliations
- Chalmers University of Technology (SE)
- Malmö University (SE)
- Chalmers University of Technology, Sweden
- Venue
- Advances in Systems Analysis, Software Engineering, and High Performance Computing
- Publisher
- IGI Global
- Published
- 2021
- Pages
- 1-19
- ISSN
- 2327-3453 (ISSN-L)
- 2327-3461
- OpenAlex
- W3149731118
- Cited by
- 108
- References
- 26
- Citations by year
- 2026: 12 · 2025: 20 · 2024: 15 · 2023: 12 · 2022: 18 · 2021: 18
- FWCI
- 34.72
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Advances in systems analysis, software engineering, and high performance computing book series (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- AI engineering, machine learning systems, data quality, model performance, compliance, engineering problems, research agenda
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