MLlib: Machine Learning in Apache Spark

نویسندگان

  • Xiangrui Meng
  • Joseph K. Bradley
  • Burak Yavuz
  • Evan R. Sparks
  • Shivaram Venkataraman
  • Davies Liu
  • Jeremy Freeman
  • D. B. Tsai
  • Manish Amde
  • Sean Owen
  • Doris Xin
  • Reynold Xin
  • Michael J. Franklin
  • Reza Bosagh Zadeh
  • Matei Zaharia
  • Ameet Talwalkar
چکیده

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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عنوان ژورنال:
  • Journal of Machine Learning Research

دوره 17  شماره 

صفحات  -

تاریخ انتشار 2016