MLlib: Machine learning in Apache Spark
Author(s)Meng, 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; Talwakar, Ameet; Zaharia, Matei A; ... Show more Show less
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal of Machine Learning Research
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.
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