In spite of the enormous excitement about the potential of deep learning, 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.
Drawing on academic work done at CMU, Berkeley, and UCLA, as well as their experience at Determined AI, a startup that provides software to make deep learning engineers dramatically more productive, Evan Sparks and Ameet Talwalkar outline the key factors that distinguish data-driven application development from classical logic-driven software engineering and their implications in the context of statistical accuracy, computational performance, debugging, and reproducibility. They then describe several promising opportunities to drastically improve data-driven application development via novel algorithmic and software solutions, including automated hyperparameter optimization, efficient utilization of distributed resources via performance models, and reproducible workflow management.
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.
Ameet Talwalkar is cofounder and chief scientist at Determined AI and an assistant professor in the School of Computer Science at Carnegie Mellon University. His research addresses scalability and ease-of-use issues in the field of statistical machine learning, with applications in computational genomics. Ameet led the initial development of the MLlib project in Apache Spark. He is the coauthor of the graduate-level textbook Foundations of Machine Learning (MIT Press) and teaches an award-winning MOOC on edX, Distributed Machine Learning with Apache Spark.
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