# Machine Learning at Microsoft with ML .NET

## TL;DR

This paper introduces ML .NET, a framework designed to seamlessly integrate machine learning models into large-scale software applications, addressing the engineering challenges of embedding ML in diverse development environments.

## Contribution

The paper presents the architecture of ML .NET, including its core DataView abstraction, and discusses its design considerations and performance compared to newer frameworks.

## Key findings

- ML .NET effectively captures full predictive pipelines.
- ML .NET demonstrates competitive performance.
- Design lessons from ML .NET inform future ML integration efforts.

## Abstract

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.