# A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading System

arXiv: https://arxiv.org/abs/2303.13216 · HTML: https://mlsanity.com/paper/2303.13216/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 62 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [Adoption and Effects of Software Engineering Best Practices in Machine Learning](https://mlsanity.com/paper/2007.14130/) (2020)
- [How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study](https://mlsanity.com/paper/2101.06098/) (2021)
- [Engineering Safety in Machine Learning](https://mlsanity.com/paper/1601.04126/) (2016)
- [Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2103.09783/) (2021)
- [Adapting Software Architectures to Machine Learning Challenges](https://mlsanity.com/paper/2105.12422/) (2021)
- [Characterizing machine learning process: A maturity framework](https://mlsanity.com/paper/1811.04871/) (2018)
- [Autonomous Systems -- An Architectural Characterization](https://mlsanity.com/paper/1811.10277/) (2018)
- [A Machine Learning Model for Stock Market Prediction](https://mlsanity.com/paper/1402.7351/) (2014)
- [A Survey of Software Engineering Practices in Turkey (extended version)](https://mlsanity.com/paper/1412.4648/) (2014)

## Abstract (arXiv)

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.