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Usability in machine learning at scale with graphlab

Published: 27 October 2013 Publication History

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:
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.

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Published In

cover image ACM Conferences
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
October 2013
2612 pages
ISBN:9781450322638
DOI:10.1145/2505515
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

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Author Tags

  1. big data
  2. data mining
  3. databases
  4. distributed systems
  5. graphs
  6. machine learning

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  • Keynote

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CIKM'13
Sponsor:
CIKM'13: 22nd ACM International Conference on Information and Knowledge Management
October 27 - November 1, 2013
California, San Francisco, USA

Acceptance Rates

CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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