2017/05/22 by Peter Bailis, Kunle Olukotun, Bailis, Peter +6 · 2 voices
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #Data Stream Mining Techniques #Machine Learning and Data Classification #cs.DB #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1705.07538
openalex publication_date 2017/05/22 · arxiv created 2017/06/09 · arxiv updated 2017/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.