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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“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]
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Jain, B., Gupta, K., Mamodiya, U., & Awasthi, S. P. (2024) · Advances in business strategy and competitive advantage book series · doi:10.4018/979-8-3693-8337-7.ch021
Architectural Assurance of AI-Based Driving Functions in Software-Defined Vehicles
Weber, S., Scheerer, M., & Weber, T. (2024) · International Conference on System Reliability and Safety (ICSRS) · doi:10.1109/icsrs63046.2024.10927546
KI-Engineering in industriellen Datenräumen
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An Approach for Integrated Development of an MLOps Architecture
Heck, P., Snoeren, J., Veracx, M., & Peeters, M. W. H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02138-0_1
Teaching Software Engineering for Artificial Intelligence: An Experience Report
Palomba, F., Voria, G., Parziale, A., Pentangelo, V., Porta, A. D., Martino, V. D., Recupito, G., & Giordano, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04207-1_15
An explanation of software architecture explanations
Rukmono, S. A., Zamfirov, F., Ochoa, L., Pex, F., & Chaudron, M. R. V. (2026) · Empirical Software Engineering · doi:10.1007/s10664-025-10782-3
Managing Entanglement in AI-Enabled Safety-Critical Systems (AISCS)
Nyakundi, N. B. N. (2026) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc67433.2026.11393817
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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