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.

翻译：当前，许多系统利用人工智能解决复杂问题。虽然这通常能提升系统效能，但开发可投产的基于AI的系统仍是一项艰巨任务。因此，需要扎实的AI工程实践来确保最终系统质量并改进开发流程。尽管已有不少面向AI系统开发的实践方案被提出，但关于这些实践应用细节的经验记录仍然匮乏。本文旨在通过案例研究填补这一空白——具体以开发自主股票交易系统为实例，该系统采用机器学习功能进行股票投资。我们从文献中筛选出10项AI工程实践，在开发过程中系统性地应用这些实践，旨在收集其适用性和有效性证据。通过结构化现场笔记，我们记录了实践经验。此外，我们还利用现场笔记记录了开发过程中遇到的挑战及应对解决方案。随后对收集的现场笔记进行分析，评估各项实践对开发过程的改进作用。最后将实证结果与现有文献进行对比。结果表明，大多数实践虽改进程度不一，但均提升了系统性能，同时我们成功克服了所有重大挑战。定性研究成果详细阐述了10项AI工程实践，以及此类项目相关的挑战与解决方案。因此，我们的实践经验丰富了该领域的新兴实证体系，尤其对初涉AI工程的实践团队具有重要参考价值。