Software Engineering for Machine Learning: A Case Study

TLDR

Recent advances in machine learning have spurred widespread interest in embedding AI capabilities into software, prompting organizations to evolve their development processes. This study observes Microsoft software teams as they develop AI‑based applications to understand how they integrate AI into engineering practices. The authors analyze a nine‑stage workflow informed by prior AI and data‑science experiences, including application diagnostics and bug reporting, to capture how teams structure their development. Microsoft teams have merged this workflow into Agile‑like processes, revealing key challenges—complex data management, specialized model skills, and entangled components—and offering best practices that can guide other organizations.

Abstract

Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components - models may be "entangled" in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.