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First GraphLab Workshop on Large-scale Machine Learning

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Credit: Negozziare

The recent First GraphLab Workshop on Large-scale Machine Learning brought together industry and academic professionals to explore the state-of-the-art on the development of machine-learning techniques for working with huge data sets. The GraphLab Workshop included about 320 participants and 15 talks and demonstrations on systems, abstractions, languages, and algorithms for large-scale data analysis.

The GraphLab distributed computational framework is particularly suited to problems with dependencies in data, which cannot be easily or efficiently separated into independent subproblems. The workshop also included the release of GraphLab 2.1, an updated abstraction that increases the scalability of GraphLab and GraphChi, which is able to solve Web-scale problems on a single personal computer.

Several of the workshop's talks included announcements on new big data developments, including Intel's Ted Wilke's announcement of the development of GraphBuilder, which uses Hadoop to overcome the gap between unstructured data and the formation of the data's graph of dependencies. The workshop also featured several short discussions led by participants from Yahoo!, Twitter, Stanford University, Netflix, Pandora, IBM, and One Kings Lane.

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