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.
@article{arxiv.1705.07538,
title = {Infrastructure for Usable Machine Learning: The Stanford DAWN Project},
author = {Peter Bailis and Kunle Olukotun and Christopher Re and Matei Zaharia},
journal= {arXiv preprint arXiv:1705.07538},
year = {2017}
}