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dc.contributor.authorMeng, Xiangrui
dc.contributor.authorBradley, Joseph
dc.contributor.authorYavuz, Burak
dc.contributor.authorSparks, Evan
dc.contributor.authorVenkataraman, Shivaram
dc.contributor.authorLiu, Davies
dc.contributor.authorFreeman, Jeremy
dc.contributor.authorTsai, DB
dc.contributor.authorAmde, Manish
dc.contributor.authorOwen, Sean
dc.contributor.authorXin, Doris
dc.contributor.authorFranklin, Michael J.
dc.contributor.authorZadeh, Reza
dc.contributor.authorTalwakar, Ameet
dc.contributor.authorZaharia, Matei A
dc.date.accessioned2018-07-06T14:08:08Z
dc.date.available2018-07-06T14:08:08Z
dc.date.issued2016-04
dc.date.submitted2015-05
dc.identifier.issn1938-7228
dc.identifier.urihttp://hdl.handle.net/1721.1/116816
dc.description.abstractApache 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.en_US
dc.publisherJMLR, Inc.en_US
dc.relation.isversionofhttp://www.jmlr.org/papers/volume17/15-237/15-237.pdfen_US
dc.rightsArticle 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.en_US
dc.sourceJournal of Machine Learning Researchen_US
dc.titleMLlib: Machine learning in Apache Sparken_US
dc.typeArticleen_US
dc.identifier.citationMeng, Xiangrui et al. "MLlib: Machine Learning in Apache Spark." Journal of Machine Learning Research, 17, 2016, pp. 1-7. © 2016 Xiangrui Meng et al.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorZaharia, Matei A
dc.relation.journalJournal of Machine Learning Researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-04-25T15:22:51Z
dspace.orderedauthorsMeng, Xiangrui; Bradley, Joseph; Yavuz, Burak; Sparks, Evan; Venkataraman, Shivaram; Liu, Davies; Freeman, Jeremy; Tsai, DB; Amde, Manish; Owen, Sean; Xin, Doris; Franklin, Michael J.; Zadeh, Reza; Zaharia, Matei; Talwalkar, Ameeten_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-7547-7204
mit.licensePUBLISHER_POLICYen_US


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