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Home/Magazine Archive/August 2018 (Vol. 61, No. 8)/Research for Practice: Prediction-Serving Systems/Abstract

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Research for Practice: Prediction-Serving Systems

By Dan Crankshaw, Joseph Gonzalez, Peter Bailis
Communications of the ACM, August 2018, Vol. 61 No. 8, Pages 45-49
10.1145/3190574
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Research for Practice, illustration — Credit: Aha-Soft

This installment of Research for Practice features a curated selection from Dan Crankshaw and Joey Gonzalez, who provide an overview of machine learning serving 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 picks provide a thoughtful selection of 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 is possible that systems such as these will become as commonplace as relational databases are today. Enjoy your read!

—Peter Bailis

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