Policy on Authorship
Anyone listed as Author on an ACM paper must meet all the following criteria:
- they have made substantial intellectual contributions to some components of the original work described in the paper; and
- they have participated in drafting and/or revision of the paper; and
- they are aware that the paper has been submitted for publication; and
- they agree to be held accountable for any issues relating to correctness or integrity of the work.
Other contributors may be acknowledged at the end of the paper, before the bibliography, with explicitly described roles, preferably using the roles found in the CASRAI Contributor Roles Taxonomy at http://casrai.org/CRediT.
Approved January 2016
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.
Written by leading domain experts for software engineers, ACM Case Studies provide an in-depth look at how software teams overcome specific challenges by implementing new technologies, adopting new practices, or a combination of both. Often through first-hand accounts, these pieces explore what the challenges were, the tools and techniques that were used to combat them, and the solution that was achieved.