SIG Governing Board Annual Reports
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
Why I Belong to ACM
Hear from Bryan Cantrill, vice president of engineering at Joyent, Ben Fried chief information officer at Google, and Theo Schlossnagle, OmniTI founder on why they are members of ACM.
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.
