2016 MLlibMachineLearninginApacheSpa

• (Meng et al., 2016) ⇒ Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, and Ameet Talwalkar. (2016). “MLlib: Machine Learning in Apache Spark.” In: The Journal of Machine Learning Research, 17. ISBN:1938-7228 arXiv:1505.06807

Subject Headings: Distributed Machine Learning System; MLlib.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+MLlib%3A+Machine+Learning+in+Apache+Spark
• https://dspace.mit.edu/handle/1721.1/116816

Quotes

Author Keywords

• Scalable Machine Learning; Distributed Algorithms; Apache Spark.

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 MLlibMachineLearninginApacheSpa	Ameet Talwalkar Matei Zaharia Michael J. Franklin Davies Liu Xiangrui Meng Joseph Bradley Burak Yavuz Evan Sparks Shivaram Venkataraman Jeremy Freeman DB Tsai Manish Amde Sean Owen Doris Xin Reynold Xin Reza Zadeh			MLlib: Machine Learning in Apache Spark