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ACM members gain access to a worldwide network of nearly 100,000 members from 190 countries. Each of ACM’s 37 Special Interest Groups (SIGs), sponsors conferences and publications that attract respected peers from around the world to address computing challenges. Regional chapters and ACM’s councils in Europe, India, and China host additional member activities and initiatives. By participating in ACM’s multifaceted global resources, members develop friendships and relationships with colleagues and mentors who can be invaluable for professional development.
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You can use your technical skills for social good and offer volunteer support on software development projects to organizations who could not otherwise afford it. SocialCoder connects volunteer programmers/software developers with registered charities and helps match them to suitable projects based on their skills, experience, and the causes they care about. Learn more about ACM’s new partnership with SocialCoder, and how you can get involved.
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.