Engineering AI systems: A research agenda
Kapitel i bok, 2022

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, we 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 we 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.


Jan Bosch

Software Engineering 1

Helena Holmström Olsson

Malmö universitet

Ivica Crnkovic

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Accelerating Digital Transformation: 10 Years of Software Center

9783031108730 (ISBN)




Datavetenskap (datalogi)

Annan elektroteknik och elektronik



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