MLlib: Machine learning in Apache Spark

Author(s)
Meng, Xiangrui; Bradley, Joseph; Yavuz, Burak; Sparks, Evan; Venkataraman, Shivaram; ... Show more
Thumbnail
Download15-237.pdf (284.1Kb)
PUBLISHER_POLICY
Terms of use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Metadata
Show full item record
Abstract
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLLIB, Spark's open-source distributed machine learning library. MLLIB provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLLIB supports several languages and provides a high-level API that leverages Spark's rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLLIB has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed.
Date issued
2016-04
URI
http://hdl.handle.net/1721.1/116816
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Journal of Machine Learning Research
Publisher
JMLR, Inc.
Citation
Meng, Xiangrui et al. "MLlib: Machine Learning in Apache Spark." Journal of Machine Learning Research, 17, 2016, pp. 1-7. © 2016 Xiangrui Meng et al.
Version: Final published version
ISSN
1938-7228
Collections