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Engineering AI Systems: A Research Agenda
Chalmers University of Technology, Sweden.ORCID iD: 0000-0003-2854-722X
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7700-1816
Chalmers University of Technology, Sweden.
2021 (English)In: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems / [ed] Ashish Kumar Luhach; Atilla Elçi, IGI Global, 2021, p. 1-19Chapter in book (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IGI Global, 2021. p. 1-19
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:mau:diva-56850DOI: 10.4018/978-1-7998-5101-1.ch001Scopus ID: 2-s2.0-85137510970ISBN: 9781799851011 (print)ISBN: 9781799851028 (electronic)OAI: oai:DiVA.org:mau-56850DiVA, id: diva2:1721040
Available from: 2022-12-20 Created: 2022-12-20 Last updated: 2024-02-05Bibliographically approved

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Olsson, Helena Holmström

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