Collaboration challenges in building ML-enabled systems

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

Reference116 articles.

1. Aho , T. , Sievi-Korte , O. , Kilamo , T. , Yaman , S. and Mikkonen , T ., 2020. Demystifying data science projects: A look on the people and process of data science today . In Proc. Int'l Conf. Product-Focused Software Process Improvement, 153--167 . Aho, T., Sievi-Korte, O., Kilamo, T., Yaman, S. and Mikkonen, T., 2020. Demystifying data science projects: A look on the people and process of data science today. In Proc. Int'l Conf. Product-Focused Software Process Improvement, 153--167.

2. Akkerman S.F. and Bakker A. 2011. Boundary Crossing and Boundary Objects. Review of educational research. 81 2 132--169. Akkerman S.F. and Bakker A. 2011. Boundary Crossing and Boundary Objects. Review of educational research. 81 2 132--169.

3. Akkiraju R. Sinha V. Xu A. Mahmud J. Gundecha P. Liu Z. Liu X. and Schumacher J. 2020. Characterizing Machine Learning Processes: A Maturity Framework. Business Process Management 17--31. Akkiraju R. Sinha V. Xu A. Mahmud J. Gundecha P. Liu Z. Liu X. and Schumacher J. 2020. Characterizing Machine Learning Processes: A Maturity Framework. Business Process Management 17--31.

4. Ameisen , E. 2020. Building Machine Learning Powered Applications: Going from Idea to Product . O'Reilly Media, Inc. Ameisen, E. 2020. Building Machine Learning Powered Applications: Going from Idea to Product. O'Reilly Media, Inc.

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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