Would you like to have a chance to deploy cutting edge machine learning algorithms in practice? Do you want to get your hands on the largest and most interesting datasets out there? Do you have valuable applied experience working with machine learning in the cloud? If so, you should consider our internship program. Candidates
Thanks again for the 570 data scientists who attended, to the great speakers and our sponsors. We have just released most of the workshop talks. If you missed the workshop, you are encouraged to catch up! Here is the GraphLab Keynote talk by Prof. Carlos Guestrin, CEO of GraphLab & Prof. at University of Washington:
Thank you for participating in the 2013 GraphLab Workshop! We had over 570 attendees from a diverse range of backgrounds — spanning theoretical research in graph algorithms and machine learning to applied data analytics and system design. There were even a few live demos of new tools! We would especially like to thank the speakers,
We are announcing today, our move from Google Code to Github! Github will allow us to work better with the open source community and streamline the process of contributing code to the project. As of today, we have closed all branches in the Google Code project, and have moved all source code (history and all)
The 2nd GraphLab Workshop will take place on Monday July 1st at the Nikko Hotel, in downtown San Francisco. Join us for a full day to learn about state-of-the-art development in Graph analytics solutions. Registration is still open The aim of the workshop is to bring together researchers from academia, as well as data scientists
So it’s been a while … We know, but we’ve been busy! We are putting the finishing touches on GraphLab 2.2 and expect to release it in early July at the GraphLab workshop. What you can expect in this next release of GraphLab is: Substantially improved loading and runtime performance through new partitioning and layout
Many thanks to Mark Levy (last.fm) for contributing implementation of the CLiMF algorithm to our growing collaborative filtering toolkit: read more.
Many thanks to Dhruv Batra from Virginia Tech for kindly donating this implementation to our growing graphical models toolkit. Full documentation is here.
We just presented our work on PowerGraph at OSDI! PowerGraph is the system and abstraction that forms the foundation of the current version of GraphLab. The PowerGraph research effort attempts to answer the question: “How can we efficiently design and implement distributed computation on the large-scale natural-graphs graphs found in settings ranging from social networks to
We have two papers accepted to OSDI 2012! One on GraphLab: PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs. Joseph Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson and Carlos Guestrin. [pdf][bib/abs] And another on GraphChi: GraphChi: Large-Scale Graph Computation on Just a PC. Aapo Kyrola, Guy Blelloch, and Carlos Guestrin. [pdf] [bib]