# Machine Learning at Microsoft with ML .NET

arXiv: https://arxiv.org/abs/1905.05715 · HTML: https://mlsanity.com/paper/1905.05715/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 11 of 11)

- [End-to-end Optimization of Machine Learning Prediction Queries](https://mlsanity.com/paper/2206.00136/) (2022)
- [On Efficient Approximate Queries over Machine Learning Models](https://mlsanity.com/paper/2206.02845/) (2022)
- [X Hacking: The Threat of Misguided AutoML](https://mlsanity.com/paper/2401.08513/) (2024)
- [Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines](https://mlsanity.com/paper/2405.11191/) (2024)
- [Identification of Dynamical Systems using Symbolic Regression](https://mlsanity.com/paper/2107.06131/) (2021)
- [Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications](https://mlsanity.com/paper/2107.13821/) (2021)
- [Phoebe: A Learning-based Checkpoint Optimizer](https://mlsanity.com/paper/2110.02313/) (2021)
- [MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat](https://mlsanity.com/paper/2209.07282/) (2022)
- [Keep Your Friends Close: Leveraging Affinity Groups to Accelerate AI Inference Workflows](https://mlsanity.com/paper/2312.11488/) (2023)
- [Natural Language Querying System Through Entity Enrichment](https://mlsanity.com/paper/2410.15753/) (2024)
- [MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds](https://mlsanity.com/paper/2508.03676/) (2025)

## Abstract (arXiv)

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.