Usability in machine learning at scale with graphlab.

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: Examining common algorithmic patterns in distributed ML methods.Qualifying the challenges of implementing these algorithms in real distributed systems.Describing computational frameworks for implementing these algorithms at scale.Addressing a significant core challenge to large-scale ML -- enabling the widespread adoption of machine learning beyond experts. Examining common algorithmic patterns in distributed ML methods.Qualifying the challenges of implementing these algorithms in real distributed systems.Describing computational frameworks for implementing these algorithms at scale.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 computations that are key for state-of-the-art ML algorithms. When these algorithms are expressed in our higher-level abstraction, GraphLab will effectively address many of the underlying parallelism challenges, including data distribution, optimized communication, and guaranteeing sequential consistency, a property that is surprisingly important for many ML algorithms. On a variety of large-scale tasks, GraphLab provides 20-100x performance improvements over Hadoop. In recent months, GraphLab has received many tens of thousands of downloads, and is being actively used by a number of startups, companies, research labs and universities.