MLI: An API for Distributed Machine Learning

Author

Sparks, Evan R. ; Talwalkar, Ameet ; Smith, Valton ; Kottalam, Jey ; Xinghao Pan ; Gonzalez, Jose ; Franklin, M.J. ; Jordan, Michael I. ; Kraska, T.

fYear

2013

fDate

7-10 Dec. 2013

Firstpage

1187

Lastpage

1192

Abstract

MLI is an Application Programming Interface designed to address the challenges of building Machine Learning 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.

Keywords

application program interfaces; distributed algorithms; learning (artificial intelligence); API; MLI; application programming interface; data-centric computing; distributed algorithm; distributed machine learning; high-performance algorithm; Computational modeling; Logistics; MATLAB; Mathematical model; Sparks; Vectors; distributed computing; machine learning; programming interface;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining (ICDM), 2013 IEEE 13th International Conference on

Conference_Location

Dallas, TX

ISSN

1550-4786

Type

conf

DOI

10.1109/ICDM.2013.158

Filename

6729619

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3166724