Name
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 Whatu0027s Next) project at Stanford. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Learning | Data science,USable,Data analysis,Computer science,Artificial intelligence,Statistical model,Data preparation,Machine learning |
DocType | Volume | Citations |
Journal | abs/1705.07538 | 3 |
PageRank | References | Authors |
0.37 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Bailis | 1 | 563 | 49.89 |
| Kunle Olukotun | 2 | 4532 | 373.50 |
| Ré Christopher | 3 | 3422 | 192.34 |
| Matei Zaharia | 4 | 9101 | 407.89 |