%0 Generic %1 bailis2017infrastructure %A Bailis, Peter %A Olukotun, Kunle %A Re, Christopher %A Zaharia, Matei %D 2017 %K 2017 arxiv hardware machine-learning resources stanford %T Infrastructure for Usable Machine Learning: The Stanford DAWN Project %U http://arxiv.org/abs/1705.07538 %X 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.

@misc{bailis2017infrastructure, 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.}, added-at = {2018-04-26T07:38:47.000+0200}, author = {Bailis, Peter and Olukotun, Kunle and Re, Christopher and Zaharia, Matei}, biburl = {https://www.bibsonomy.org/bibtex/2c9a9e584e78661483bba67d9cdc5177a/achakraborty}, description = {[1705.07538] Infrastructure for Usable Machine Learning: The Stanford DAWN Project}, interhash = {5b47a3d68dbe15bd48e7eda4662cff8d}, intrahash = {c9a9e584e78661483bba67d9cdc5177a}, keywords = {2017 arxiv hardware machine-learning resources stanford}, note = {cite arxiv:1705.07538}, timestamp = {2018-04-26T07:38:57.000+0200}, title = {Infrastructure for Usable Machine Learning: The Stanford DAWN Project}, url = {http://arxiv.org/abs/1705.07538}, year = 2017 }