Engineering AI Systems: A Research Agenda

Understand

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.