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Real-Time Machine Learning: The Missing Pieces

Abstract · Mar 11, 2017 07:46 ·

cs-dc cs-ai cs-lg

Arxiv Abstract

  • Robert Nishihara
  • Philipp Moritz
  • Stephanie Wang
  • Alexey Tumanov
  • William Paul
  • Johann Schleier-Smith
  • Richard Liaw
  • Michael I. Jordan
  • Ion Stoica

Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a new set of requirements, none of which are difficult to achieve in isolation, but the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources. We assert that a new distributed execution framework is needed for such ML applications and propose a candidate approach with a proof-of-concept architecture that achieves a 63x performance improvement over a state-of-the-art execution framework for a representative application.

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