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Machine Learning is transitioning from an art and science into a technologyavailable to every developer. In the near future, every application on everyplatform will incorporate trained models to encode data-based decisions thatwould be impossible for developers to author. This presents a significantengineering challenge, since currently data science and modeling are largelydecoupled from standard software development processes. This separation makesincorporating machine learning capabilities inside applications unnecessarilycostly and difficult, and furthermore discourage developers from embracing MLin first place. In this paper we present ML .NET, a framework developed atMicrosoft over the last decade in response to the challenge of making it easyto ship machine learning models in large software applications. We present itsarchitecture, and illuminate the application demands that shaped it.Specifically, we introduce DataView, the core data abstraction of ML .NET whichallows it to capture full predictive pipelines efficiently and consistentlyacross training and inference lifecycles. We close the paper with asurprisingly favorable performance study of ML .NET compared to more recententrants, and a discussion of some lessons learned.

Machine Learning at Microsoft with ML .NET