Machine Learning at Microsoft with ML .NET
This addresses the problem for developers at Microsoft and potentially others by reducing the cost and difficulty of incorporating ML into applications, though it appears incremental as a framework built over a decade.
The paper tackles the challenge of integrating machine learning into standard software development by presenting ML .NET, a framework designed to make it easy to ship ML models in large applications, and reports a surprisingly favorable performance study compared to more recent entrants.
Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned.