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).

towards building AI applications and platforms. In this paper, we offer the following contributions. 1)A description of how several Microsoft software en-gineering teams work cast into a nine-stage workflow for integrating machine learning into application and platform development. 2)A set of best practices for building applications and

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  Engineering, Machine, Building, Software, Microsoft, Learning, Software engineering for machine learning

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