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Software Engineering for Machine Learning: A Case Study

Software Engineering for Machine Learning: A Case StudySaleema AmershiMicrosoft ResearchRedmond, WA BegelMicrosoft ResearchRedmond, WA BirdMicrosoft ResearchRedmond, WA DeLineMicrosoft ResearchRedmond, WA GallUniversity of ZurichZurich, KamarMicrosoft ResearchRedmond, WA NagappanMicrosoft ResearchRedmond, WA NushiMicrosoft ResearchRedmond, WA ZimmermannMicrosoft ResearchRedmond, WA Recent advances in Machine learning have stim-ulated widespread interest within the Information Technologysector on integrating AI capabilities into Software and goal has forced organizations to evolve their developmentprocesses. We report on a Study that we conducted on observingsoftware teams at Microsoft as they develop AI-based applica-tions. We consider a nine-stage workflow process informed byprior experiences developing AI applications ( , search andNLP) and data science tools ( application diagnostics and bugreporting).

training may loop back to feature engineering (e.g., in representation learning). always require deep enough knowledge of machine learning to build, evaluate, and tune models from scratch. Third, it can be more difficult to maintain strict module boundaries between machine learning components than for software engineering modules.

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