SIG Project Fund (SPF)

The SIG Project Fund (SPF) was created in order to further the work of the SIGs by pooling SIG funds to support projects that will benefit multiple SIGs. It was managed by the SIG Governing Board with decisions about the SPF being made by those SIGs that contributed to the fund.

Proposals were required to meet the following guidelines:

  • The project proposal should outline: its goals, its operation plan and timelines, its participants and their qualifications, its funding needs and sources, its plan to evaluate its outcomes, and its reporting plans including a required final report that must be submitted within 90 days of the end of the project's operation.

  • The outcomes supporting the goals of the project should be concrete and measurable.

  • The project must describe specifically how it will support the efforts of more than one SIG and must be sponsored by one or more SIGs through a transmission letter from the sponsoring SIG Chairs. The sponsoring SIGs must all be contributors to the SPF by meeting their SPF assessment.

  • For each project, a sponsoring SIG is responsible for submitting the required reports. A SIG that has not met its project reporting requirements may not sponsor a new project. Thus any required interim reports must have been submitted for ongoing projects; the final report must have been submitted for a completed project.

The fund is no longer available.

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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.