DocumentCode :
3166724
Title :
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 :
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