MLlib: Machine Learning in Apache Spark

Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan Sparks(University of California, Berkeley), Shivaram Venkataraman(University of California, Berkeley), Davies Liu, Jeremy Freeman(Janelia Research Campus), DB Tsai(Netflix (United States)), Manish Amde, Sean Owen, Doris Xin(Urbana University), Reynold Xin, Michael J. Franklin(University of California, Berkeley), Reza Bosagh Zadeh, Matei Zaharia, Ameet Talwalkar
arXiv (Cornell University)
May 26, 2015
Cited by 961Open Access
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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.


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