Abstract: Artificial intelligence enabled systems have been an inevitable part of every day life. However, efficient software engineering principles and processes need to be considered and extended when developing AI-enabled systems. The objective of this study is to identify and classify software engineering challenges that are faced by different companies when developing software-intensive systems that incorporate machine learning components. Using case study approach, we explore the development of machine learning systems from six different companies across various domains and identified main software engineering challenges. The challenges are mapped into a proposed taxonomy that depicts the evolution of use of ML/DL components in software-intensive system in industrial settings. Our study provides useful insights to software engineering community and research to guide discussion and future research of applied machine learning.
