Nov 1, 2017 · Machine learning classifiers are basic research tools used in numerous types of network analysis and modeling. To reduce the need for domain ...
These services run on the cloud, and provide a query interface to an ML classifier trained on uploaded datasets. They simplify the process of running ML systems ...
Why Study ML-as-a-Service? Q: How well do they perform? Q: How much does the amount of user control impact. ML performance?
In this paper, we evaluate the effectiveness of MLaaS systems ranging from fully-automated, turnkey systems to fully-customizable systems, and find that with ...
(2015) propose an architecture to create a flexible and scalable Machine Learning service, while Yao et al. (2017) provide a detailed empirical comparison ...
In this paper, we evaluate the effectiveness of MLaaS systems ranging from fully-automated, turnkey systems to fully-customizable systems, and find that with ...
Zhao, Complexity vs Performance: Empirical Analy- sis of Machine Learning as a Service IMC '17: Proceedings of the. 2017 Internet Measurement Conference ...
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... and Bolun Wang and Bimal Viswanath and Haitao Zheng and Ben Y. Zhao }, title = {Complexity vs. Performance: Empirical Analysis of Machine Learning as a Service } ...
Nov 11, 2018 · Complexity vs. performance: empirical analysis of machine learning as a service Proceeding of theInternet Measurement Conference / pages 384-397.
Complexity vs. performance: empirical analysis of machine learning as a service. Conference Paper. Nov 2017. Yuanshun Yao · Zhujun Xiao · Bolun Wang ...