Keynote Talk: Usability in Machine Learning at Scale with GraphLab
At the ACM Conference on Information and Knowledge Management 2013
By Prof. Carlos Guestrin, CEO of GraphLab
Abstract - Today, machine learning (ML) methods play a central role in industry and science. The growth of the Web and improvements in sensor data collection technology have been rapidly increasing the magnitude and complexity of the ML tasks we must solve. This growth is driving the need for scalable, parallel ML algorithms that can handle Big Data. In this talk, we will focus on:
1. Examining common algorithmic patterns in distributed ML methods.
2. Qualifying the challenges of implementing these algorithms in real distributed systems.
3. Describing computational frameworks for implementing these algorithms at scale.
4. Addressing a significant core challenge to large-scale ML -- enabling the widespread adoption of machine learning beyond experts.
In the latter part, we will focus mainly on the GraphLab framework, which naturally expresses asynchronous, dynamic graph.