What Chapter sub-types are available to Chapter Organizers?
Professional Chapter Organizers can form the following types of Chapters:
- General Interest/Geographic – Chapter Organizers want to attract as many computing professionals as possible in the same geographic area. It is likely that many different technical and professional areas will be addressed.
- Computing Topic – Chapter Organizers are interested in addressing specific technical and/or professional topics with computing professionals in the same geographic areas.
- Industry – Chapter Organizers want to address the technical and professional needs of computing professionals in a specific industry, e.g., healthcare, entertainment, financial, etc.
- Job Function – Chapter Organizers want to address the technical and professional needs of computing professionals who perform a similar job function, e.g., software testers, software developers, etc.
- Company – Chapter Organizers want to address the technical and professional needs of computing professionals centered within a company.
- ACM Special Interest Group – Chapter Organizers want to address the technical and professional needs of computing professionals interested in an area covered by one of ACM’S Special Interest Groups, e.g, SIGGRAPH, SIGCHI, SIGMOBILE, etc.
- ACM-W (Women’s Group) – Chapter Organizers want to specifically address the technical and professional needs of women in computing, and ally themselves with ACM’s Council on Women in Computing.
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.
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.
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.