# MLI: An API for Distributed Machine Learning

arXiv: https://arxiv.org/abs/1310.5426 · HTML: https://mlsanity.com/paper/1310.5426/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2013.

## Built on by (most cited first, 3 of 3)

- [End-to-end Optimization of Machine Learning Prediction Queries](https://mlsanity.com/paper/2206.00136/) (2022)
- [Baechi: Fast Device Placement of Machine Learning Graphs](https://mlsanity.com/paper/2301.08695/) (2023)
- [Ordering for Non-Replacement SGD](https://mlsanity.com/paper/2306.15848/) (2023)

## Abstract (arXiv)

MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability.