[A version of this post appears on the O’Reilly Radar blog.]
Editor’s note: Carlos Guestrin will be part of the team teaching Large-scale Machine Learning Day at Strata + Hadoop World in San Jose. Visit the Strata + Hadoop World website for more information on the program.
I only really started playing around with GraphLab when the companion project GraphChi came onto the scene. By then I’d heard from many avid users and admired how their user conference instantly became a popular San Francisco Bay Area data science event. For this podcast episode, I sat down with Carlos Guestrin, co-founder/CEO of Dato, a start-up launched by the creators of GraphLab. We talked about the early days of GraphLab, the evolution of GraphLab Create, and what’s he’s learned from starting a company.
MATLAB for graphs
Guestrin remains a professor of computer science at the University of Washington, and GraphLab originated when he was still a faculty member at Carnegie Mellon. GraphLab was built by avid MATLAB users who needed to do large scale graphical computations to demonstrate their research results. Guestrin shared some of the backstory:
“I was a professor at Carnegie Mellon for about eight years before I moved to Seattle. A couple of my students, Joey Gonzales and Yucheng Low were working on large scale distributed machine learning algorithms specially with things called graphical models. We tried to implement them to show off the theorems that we had proven. We tried to run those things on top of Hadoop and it was really slow. We ended up writing those algorithms on top of MPI which is a high performance computing library and it was just a pain. It took a long time and it was hard to reproduce the results and the impact it had on us is that writing papers became a pain. We wanted a system for my lab that allowed us to write more papers more quickly. That was the goal. In other words so they could implement this machine learning algorithms more easily, more quickly specifically on graph data which is what we focused on.”