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1.	Bosch, Jan, 1967, et al. (author) Engineering AI systems: A research agenda 2022 In: Accelerating Digital Transformation: 10 Years of Software Center. - Cham : Springer. - 9783031108730 - 9783031108723 - 9783031108754 ; , s. 407-425 Book chapter (other academic/artistic)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, 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.

Bosch, Jan, 1967, et al. (author)
Engineering AI systems: A research agenda
2022
In: Accelerating Digital Transformation: 10 Years of Software Center. - Cham : Springer. - 9783031108730 - 9783031108723 - 9783031108754 ; , s. 407-425
Book chapter (other academic/artistic)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, 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.

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