IPP Symposium
MLbase: A Distributed Machine-learning System
Tim Kraska, Brown CS Faculty (Data Management)
Machine learning (ML) and statistical techniques are key to transforming big data into actionable knowledge. In spite of the modern primacy of data, the complexity of existing ML algorithms is often overwhelming-many users do not understand the trade-offs and challenges of parameterizing and choosing between different learning techniques. Furthermore, existing scalable systems that support machine learning are typically not accessible to ML researchers without a strong background in distributed systems and low-level primitives.
In this talk I will present MLbase, a novel system harnessing the power of machine learning for both end-users and ML researchers. MLbase provides (1) a simple declarative way to specify ML tasks, (2) a novel optimizer to select and dynamically adapt the choice of learning algorithm, and (3) a set of high-level operators to enable ML researchers to scalably implement a wide range of ML methods without deep systems knowledge.
Tim Kraska is an Assistant Professor in the Computer Science department at Brown University. Currently, his research focuses on Big Data management in the cloud and hybrid human/machine database systems. Before joining Brown, Tim Kraska spent 2 years as a PostDoc in the AMPLab at UC Berkeley after receiving his PhD from ETH Zurich, where he worked on transaction management and stream processing in the cloud. He was awarded a Swiss National Science Foundation Prospective Researcher Fellowship (2010), a DAAD Scholarship (2006), a University of Sydney Master of Information Technology Scholarship for outstanding achievement (2005), the University of Sydney Siemens Prize (2005), a VLDB best demo award (2011) and an ICDE best paper award (2013).
