MLI: An API for Distributed Machine Learning
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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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2013.
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Citing papers per year: 2022: 1; 2023: 2.
3 papers.
- 2022End-to-end Optimization of Machine Learning Prediction Queriescited by 16
- 2023Baechi: Fast Device Placement of Machine Learning Graphscited by 2
- 2023Ordering for Non-Replacement SGDcited by 0
- 2023Ordering for Non-Replacement SGD28 Jun 2023
- 2023Baechi: Fast Device Placement of Machine Learning Graphs20 Jan 2023
- 2022End-to-end Optimization of Machine Learning Prediction Queries31 May 2022