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MLBase: A User-friendly System for Distributed Machine Learning

  • May 30, 2013 · 6:30 PM

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Title: MLBase: A User-friendly System for Distributed Machine Learning

Speaker: Ameet Talwalkar (UC-Berkeley AMPLab)


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.


Speaker Bio

Ameet Talwalkar is an NSF post-doctoral fellow in the Computer Science Division at UC Berkeley. His research focuses on devising scalable machine learning algorithms, and more recently, on interdisciplinary approaches for connecting advances in machine learning to large-scale problems in science and technology. He graduated summa cum laude from Yale University and obtained his Ph.D. at New York University. He was awarded the Janet Fabri Prize for the best doctoral dissertation in NYU's Computer Science Department, Yale's undergraduate prize in Computer Science, and a Westinghouse Science Talent Search Scholarship.


Tentative Agenda

6:30-7:00 - socializing

7:00-8:00 - talk

8:00-8:30 - socializing


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