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.
Description
[1705.07538] Infrastructure for Usable Machine Learning: The Stanford DAWN Project
%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
}