Translations:Large Scale Machine Learning (Big Data)/68/en: Difference between revisions

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[https://spark.apache.org/docs/latest/ml-guide.html Spark ML] is a Machine Learning library built on top of [[Apache_Spark/en|Apache Spark]]. It enables users to scale out many Machine Learning methods to massive amounts of data, over multiple nodes, without worrying about distributing datasets or explicitly writing distributed/parallel code. The library also includes many useful tools for distributed Linear Algebra and Statistics. Please see our tutorial on [[Apache_Spark/en#Usage|submitting Spark jobs]] before trying out the examples on the official [https://spark.apache.org/docs/latest/ml-guide.html Spark ML documentation].
[https://spark.apache.org/docs/latest/ml-guide.html Spark ML] is a machine learning library built on top of [[Apache_Spark/en|Apache Spark]]. It enables users to scale out many machine learning methods to massive amounts of data, over multiple nodes, without worrying about distributing datasets or explicitly writing distributed/parallel code. The library also includes many useful tools for distributed linear algebra and statistics. Please see our tutorial on [[Apache_Spark/en#Usage|submitting Spark jobs]] before trying out the examples on the official [https://spark.apache.org/docs/latest/ml-guide.html Spark ML documentation].

Latest revision as of 20:44, 28 November 2023

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[https://spark.apache.org/docs/latest/ml-guide.html Spark ML] is a machine learning library built on top of [[Apache_Spark/en|Apache Spark]]. It enables users to scale out many machine learning methods to massive amounts of data, over multiple nodes, without worrying about distributing datasets or explicitly writing distributed/parallel code. The library also includes many useful tools for distributed linear algebra and statistics. Please see our tutorial on [[Apache_Spark/en#Usage|submitting Spark jobs]] before trying out the examples on the official [https://spark.apache.org/docs/latest/ml-guide.html Spark ML documentation].

Spark ML is a machine learning library built on top of Apache Spark. It enables users to scale out many machine learning methods to massive amounts of data, over multiple nodes, without worrying about distributing datasets or explicitly writing distributed/parallel code. The library also includes many useful tools for distributed linear algebra and statistics. Please see our tutorial on submitting Spark jobs before trying out the examples on the official Spark ML documentation.

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