Engineering AI Systems: A Research Agenda

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 paper, 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 paper 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.Comment: 8 pages, 4 figure

Customer relationship management and interface redesign : a study on the website design on the ebay websites on cultural perspectives

This study is focused on the cross-cultural issues in the post-adoption phases of customer relationship management (CRM) for an international electronic marketplace, which operates in more than 30 countries. In particular, the authors focus on how the electronic marketplace modifies its interface redesign for addressing the different tastes of users from different cultural backgrounds. The authors hope this study can address to how cultural and language differences affect the interface redesign of CRM, which is part of the enterprise system, in the multinational and global context through a qualitative study