Engineering AI Systems: A Research Agenda

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Author(s): Jan Bosch (Chalmers University of Technology, Sweden), Helena Holmström Olsson (Malmö University, Sweden)and Ivica Crnkovic (Chalmers University of Technology, Sweden)
Copyright: 2021
Pages: 19
Source title: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems
Source Author(s)/Editor(s): Ashish Kumar Luhach (The PNG University of Technology, Papua New Guinea)and Atilla Elçi (Hasan Kalyoncu University, Turkey)
DOI: 10.4018/978-1-7998-5101-1.ch001

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Systems and Software Engineering

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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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