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Presentation: Scalability Challenges in Big Data Science

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Scalability Challenges in Big Data Science

Yesterday I gave a talk on scalability and machine learning at the BerlinBuzzword conference. I give an overview of different ways to scale data analysis and machine learning methods. I cover MapReduce (of course), large scale training of SVMs via stochastic gradient descent, but also stream mining, and real-time (as you know, “you don’t just scale into real-time”).

The conference continues today, follow the conference on Twitter on the #bbuzz hashtag.

Update: On scribd, the hyperlinks are somehow lost, so here is the list:

Scalable Databases

Multithreadding and Messaging Frameworks

MapReduce

Large Scale Classifier Training

Other frameworks

Stream processing

TWIMPACT:

 

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Published at DZone with permission of Mikio Braun, DZone MVB. See the original article here.

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