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MLlib: Machine Learning in Apache Spark

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arxiv 1505.06807 v1 pith:R2IDNGAP submitted 2015-05-26 cs.LG cs.DCcs.MSstat.ML

Xiangrui Meng , Joseph Bradley , Burak Yavuz , Evan Sparks , Shivaram Venkataraman , Davies Liu , Jeremy Freeman , DB Tsai
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Manish Amde Sean Owen Doris Xin Reynold Xin Michael J. Franklin Reza Zadeh Matei Zaharia Ameet Talwalkar
This is my paper · ORCID
classification cs.LGcs.DCcs.MSstat.ML
keywords learningmllibsparkmachineopen-sourceapachegrowthincludes
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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.

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  1. Intelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows

    cs.AI 2024-12 conditional novelty 4.0 of 10

    A Spark-based visual ML workflow framework with LangGraph agents is proposed, but its claimed benefits are supported only by a thin prototype experiment without code release.

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