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The new Mahout DSL has two aims. One, to make it easy to program distributed machine learning algorithms using a math-like notation for the programs. The secondary goal is to allow such programs to be fairly performant by allowing alternative back-end computational engines. The primary back-end for Mahout is currently Spark, but there is also work going on with the h2o system. I will talk about how these back-ends help achieve these two goals, with particular attention to how speed is achieved.
KEYWORDS: Machine Learning, Mahout, Scala, Spark
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