ACM SGB Meeting Materials, March 27, 2009

March 27, 2009 SGB Meeting


SIGSAM Program Review

  • SIGSAM Program Review March 2009


SIGSAM Slides

  • SIGSAM Slides March 2009


SIGMICRO Program Review

  • SIGMICRO Program Review March 2009


SIGMICRO Slides

  • SIGMICRO Slides March 2009


SIGSOFT Program Review

  • SIGSOFT Program Review March 2009


SIGMM Program Review

  • SIGMM Program Review March 2009


SIGMM Slides

  • SIGMM Slides March 2009


SIGSIM Program Review

  • SIGSIM Program Review March 2009


SIGSAC Program Review

  • SIGSAC Program Review March 2009


SIGSAC Slides

  • SIGSAC Slides March 2009


SIGMOD Program Review

  • SIGMOD Program Review March 2009


March 27, 2009 Agenda


SIGSIM Slides

  • SIGSIM Slides March 2009


History Committee Presentation

  • History Committee Presentation March 2009


SIGSOFT Slides

  • SIGSOFT Slides March 2009


SIGMOD Slides

  • SIGMOD Slides March 2009


March 2009 SGB Meeting Minutes

  • March 2009 SGB Meeting Minutes

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Prediction-Serving Systems

ACM Queue’s “Research for Practice” is your number one resource for keeping up with emerging developments in the world of theory and applying them to the challenges you face on a daily basis. In this installment, Dan Crankshaw and Joey Gonzalez provide an overview of machine learning server systems. What happens when we wish to actually deploy a machine learning model to production, and how do we serve predictions with high accuracy and high computational efficiency? Dan and Joey’s curated research selection presents cutting-edge techniques spanning database-level integration, video processing, and prediction middleware. Given the explosion of interest in machine learning and its increasing impact on seemingly every application vertical, it's possible that systems such as these will become as commonplace as relational databases are today.