SIG Governing Board Annual Reports

For the Fiscal Year Ending: June 30, 2018

For the Fiscal Year Ending: June 30, 2017

For the Fiscal Year Ending: June 30, 2016

For the Fiscal Year Ending: June 30, 2015

For the Fiscal Year Ending: June 30, 2014

For the Fiscal Year Ending: June 30, 2013

For the Fiscal Year Ending: June 30, 2012

For the Fiscal Year Ending: June 30, 2011

For the Fiscal Year Ending: June 30, 2010

For the Fiscal Year Ending: June 30, 2009

For the Fiscal Year Ending: June 30, 2008

For the Fiscal Year Ending: June 30, 2007

For the Fiscal Year Ending: June 30, 2006

For the Fiscal Year Ending: June 30, 2005

For the Fiscal Year Ending: June 30, 2004

For the Fiscal Year Ending: June 30, 2003

For the Fiscal Year Ending: June 30, 2002

ACM Case Studies

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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.