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arXiv · 1705.07538

Infrastructure for Usable Machine Learning: The Stanford DAWN Project

Peter Bailis·Kunle Olukotun·Christopher Re·Matei Zaharia
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Abstract

Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring. In this document, we outline opportunities for infrastructure supporting usable, end-to-end machine learning applications in the context of the nascent DAWN (Data Analytics for What's Next) project at Stanford.

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Peter Bailis, Kunle Olukotun, Christopher Re, Matei Zaharia. 2017-06-09. Infrastructure for Usable Machine Learning: The Stanford DAWN Project. https://arxiv.org/abs/1705.07538

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