Dan is a graduate student in the UC Berkeley RISELab and alumni of the AMPLab. He researches systems and techniques for serving and deploying machine learning, with a particular emphasis on low-latency and interactive applications.
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not… Read more
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