Infrastructure for Usable Machine Learning: The Stanford DAWN Project
It aims to make ML more accessible for organizations beyond top-tier ones, but it is incremental as it builds on existing systems and tools.
The paper addresses the high cost and complexity of building machine learning applications by proposing infrastructure to support end-to-end development, from data preparation to monitoring, as part of the Stanford DAWN project.
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.