Collaboration challenges in building ML-enabled systems-Reference-Cited by-同舟云学术

Collaboration challenges in building ML-enabled systems

Published:2022-05-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 44th International Conference on Software Engineering
language:
Short-container-title:

Author:

Nahar Nadia¹,Zhou Shurui²,Lewis Grace³,Kästner Christian¹

Affiliation:

1. Carnegie Mellon University

2. University of Toronto, Toronto, Ontario, Canada

3. Carnegie Mellon Software Engineering Institute

Funder

Natural Sciences and Engineering Research Council of Canada (NSERC)

NSF

Carnegie Mellon University (CMU)

Department of Defense

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3510003.3510209

Reference116 articles.

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5. Amershi , S. et al. 2019. Software Engineering for Machine Learning: A Case Study . In Proc. of 41st Int'l Conf. on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 291--300 . Amershi, S. et al. 2019. Software Engineering for Machine Learning: A Case Study. In Proc. of 41st Int'l Conf. on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 291--300.

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