Speaker: Ameet Talwalkar - http://www.cs.berkeley.edu/~ameet/
Host and video recording: Yelp - http://www.yelp.com/
May 30, 2013 meetup: http://www.meetup.com/SF-Bayarea-Machine-Learning/events/115946432/
Machine learning (ML) and statistical techniques are crucial to transforming Big Data into actionable knowledge. However, the complexity of existing ML algorithms is often overwhelming. End-users often do not understand the trade-offs and challenges of parameterizing and choosing between different learning techniques. Furthermore, existing scalable systems that support ML are typically not accessible to ML developers without a strong background in distributed systems and low-level primitives. In this work we present MLbase, a system designed to tackle both of these issues simultaneously. MLbase provides (1) a simple declarative way for end-users to specify ML tasks, (2) a novel optimizer to select and dynamically adapt the choice of learning algorithm, (3) a set of high-level operators to enable ML developers to scalably implement a wide range of ML methods without deep systems knowledge, and (4) a distributed run-time optimized for the data-access patterns of these high-level operators.