@article {47752, title = {MLlib: Machine learning in Apache Spark.}, journal = {Journal of Machine Learning Research}, volume = {17}, year = {2016}, month = {2016 Apr 01}, pages = {1-7}, 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{\textquoteright}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{\textquoteright}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.\ 

}, url = {http://www.jmlr.org/papers/volume17/15-237/15-237.pdf}, author = {Meng, Xiangrui and Bradley, Joseph and Yavus, Burak and Sparks, Evan and Venkataraman, Shivaram and Liu, Davies and Freeman, Jeremy and Tsai, D B and Amde, Manish and Owen, Sean and Xin, Doris and Franklin, Michael J and Zadeh, Reza and Zaharia, Matei and Talwalkar, Ameet} }