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Cited by 108Software Engineering Techniques and PracticesSoftware Engineering ResearchArtificial Intelligence Applications

Engineering AI Systems: A Research Agenda

Bosch, Jan, Olsson, Helena Holmström, Crnkovic, Ivica·2021·Advances in Systems Analysis, Software Engineering, and High Performance Computing
DOI 10.4018/978-1-7998-5101-1.ch001
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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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Crossref · 10.4018/978-1-7998-5101-1.ch001

Cites

26 of 26 references verified0 citation errors · 0 not checkable

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  • Achieving a low inference latency and high throughput is widely reported as a challenge in serving ML models.

    “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]

  • 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.

    “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]

  • Serving-related challenges are some of the ones most often reported in the literature, usually from a performance perspective.

    “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]

  • The integration of data-driven models with computation-driven software components is a potentially non-trivial task.

    “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]

  • [7] reported the extensive use of glue code as a challenge with which companies struggle.

    “[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]

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Authors
Bosch, J., Olsson, H. H., & Crnkovic, I.
Affiliations
  • Chalmers University of Technology (SE)
  • Malmö University (SE)
  • Chalmers University of Technology, Sweden
ORCID
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
DOI
10.4018/978-1-7998-5101-1.ch001
OpenAlex
W3149731118
Cited by
108
References
26
Citations by year
2026: 12 · 2025: 20 · 2024: 15 · 2023: 12 · 2022: 18 · 2021: 18 · 2020: 4 · 2019: 3 · 2018: 1 · 2017: 1 · 2015: 2 · 2013: 1 · 2012: 1
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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