  Today, many systems use artificial intelligence (AI) to solve complex
problems. While this often increases system effectiveness, developing a
production-ready AI-based system is a difficult task. Thus, solid AI
engineering practices are required to ensure the quality of the resulting
system and to improve the development process. While several practices have
already been proposed for the development of AI-based systems, detailed
practical experiences of applying these practices are rare.
  In this paper, we aim to address this gap by collecting such experiences
during a case study, namely the development of an autonomous stock trading
system that uses machine learning functionality to invest in stocks. We
selected 10 AI engineering practices from the literature and systematically
applied them during development, with the goal to collect evidence about their
applicability and effectiveness. Using structured field notes, we documented
our experiences. Furthermore, we also used field notes to document challenges
that occurred during the development, and the solutions we applied to overcome
them. Afterwards, we analyzed the collected field notes, and evaluated how each
practice improved the development. Lastly, we compared our evidence with
existing literature.
  Most applied practices improved our system, albeit to varying extent, and we
were able to overcome all major challenges. The qualitative results provide
detailed accounts about 10 AI engineering practices, as well as challenges and
solutions associated with such a project. Our experiences therefore enrich the
emerging body of evidence in this field, which may be especially helpful for
practitioner teams new to AI engineering.
