Deep learning has yielded a step function improvement on important problems ranging from computer vision to natural language processing, and there is enormous excitement about its broader potential. However, building practical applications powered by deep learning remains an enormous challenge: the necessary expertise is scarce, the hardware requirements can be prohibitive, and current software tools are immature and limited in scope.
Evan Sparks outlines the current state of deep learning application development and details several key challenges associated with standard workflows, such as the cost of human and compute resources, training time and the development cycle, data and cluster resource management, and several issues around application deployment. Drawing on academic research from CMU, Berkeley, and UCLA and his experience at Determined AI, a startup that provides software to make deep learning engineers fantastically more productive, Evan then shares potential solutions that can dramatically enhance the velocity of application development
Evan Sparks is a cofounder and CEO of Determined AI, a software company that makes machine learning engineers and data scientists fantastically more productive. Previously, Evan worked in quantitative finance and web intelligence. He holds a PhD in computer science from the University of California, Berkeley, where, as a member of the AMPLab, he contributed to the design and implementation of much of the large-scale machine learning ecosystem around Apache Spark, including MLlib and KeystoneML. He also holds an AB in computer science from Dartmouth College.
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