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pro vyhledávání: '"Khermosh, Lior"'
Autor:
Ghanta, Sindhu, Subramanian, Sriram, Khermosh, Lior, Shah, Harshil, Goldberg, Yakov, Sundararaman, Swaminathan, Roselli, Drew, Talagala, Nisha
Operations is a key challenge in the domain of machine learning pipeline deployments involving monitoring and management of real-time prediction quality. Typically, metrics like accuracy, RMSE etc., are used to track the performance of models in depl
Externí odkaz:
http://arxiv.org/abs/1902.08638
Autor:
Ghanta, Sindhu, Subramanian, Sriram, Khermosh, Lior, Sundararaman, Swaminathan, Shah, Harshil, Goldberg, Yakov, Roselli, Drew, Talagala, Nisha
Deployment of machine learning (ML) algorithms in production for extended periods of time has uncovered new challenges such as monitoring and management of real-time prediction quality of a model in the absence of labels. However, such tracking is im
Externí odkaz:
http://arxiv.org/abs/1902.02808
Autor:
Ghanta, Sindhu, Subramanian, Sriram, Khermosh, Lior, Sundararaman, Swaminathan, Shah, Harshil, Goldberg, Yakov, Roselli, Drew, Talagala, Nisha
Publikováno v:
Proceedings of SPIE; 7/21/2019, Vol. 11139, p111390R-1-111390R-12, 12p
Autor:
Zelinski, Michael E., Taha, Tarek M., Howe, Jonathan, Awwal, Abdul A. S., Iftekharuddin, Khan M., Ghanta, Sindhu, Subramanian, Sriram, Khermosh, Lior, Sundararaman, Swaminathan, Shah, Harshil, Goldberg, Yakov, Roselli, Drew, Talagala, Nisha
Publikováno v:
Proceedings of SPIE; September 2019, Vol. 11139 Issue: 1 p111390R-111390R-12, 11027623p
Akademický článek
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