March 16, 2012 SGB Meeting - Best Practice Session

Best Practices Session

 

Group 1

Andrew Sears, SIGACCESS

Ricky Sward, SIGAda

Sung Shin, SIGAPP

Netiva Caftori, SIGCAS

Brad Mehlenbacher, SIGDOC

Jeremy Johnson, SIGSAM

Drew Hamilton, SIGSIM

Alex Wolf

 

Group 2

David Pennock, SIGecom

Gang Luo, SIGHIT

Mark Stockman, SIGITE

Erik Altman, SIGMICRO

Hanan Sammet, SIGSPATIAL

Gultekin Ozsoyoglu, SIGBioinformatics

Kelly Wainright, SIGUCCS

Joe Konstan

 

Group 3

Dick Bulterman, SIGWEB

Cherri Pancake, SIGHPC

Balakrishnan Prabhakaran, SIGMM

Una-May O'Reilly, SIGEVO

Janis Sipior, SIGMIS

Roy Want, SIGMOBILE

X. Sharon Hu, SIGBED

Barbara Boucher Owens

 

Group 4

Lance Fortnow, SIGACT

Ian Soboroff, SIGIR

Yannis Ioannidis, SIGMOD

Jeanna Matthews, SIGOPS

Elisa Bertino, SIGSAC

Henning Schulzrinne, SIGCOMM

Yolanda Gil, SIGART

Scott Owen

 

Group 5

Gerrit Van der Veer, SIGCHI

Jeff Jortner, SIGGRAPH

Andrew Black, SIGPLAN

David Rosenblum, SIGSOFT

David Wood, SIGARCH

Patrick Madden, SIGDA

Renee McCauley, SIGCSE

Donna Cappo

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. 

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