Latest revision as of 07:43, 2 February 2021

Abstract

Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requirements. We present KeystoneML, a system that captures and optimizes the end-to-end large-scale machine learning applications for high-throughput training in a distributed environment with a high-level API. This approach offers increased ease of use and higher performance over existing systems for large scale learning. We demonstrate the effectiveness of KeystoneML in achieving high quality statistical accuracy and scalable training using real world datasets in several domains. By optimizing execution KeystoneML achieves up to 15x training throughput over unoptimized execution on a real image classification application.

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1610.09451

• http://arxiv.org/pdf/1610.09451

• http://dx.doi.org/10.1109/icde.2017.109

• http://xplorestaging.ieee.org/ielx7/7929494/7929895/07930005.pdf?arnumber=7930005,

http://dx.doi.org/10.1109/icde.2017.109

• https://dblp.uni-trier.de/db/journals/corr/corr1610.html#SparksVKFR16,

https://arxiv.org/abs/1610.09451,

https://ui.adsabs.harvard.edu/abs/2016arXiv161009451S/abstract,

https://ieeexplore.ieee.org/document/7930005,

http://dblp.uni-trier.de/db/journals/corr/corr1610.html#SparksVKFR16,

http://ieeexplore.ieee.org/document/7930005,

https://doi.org/10.1109/ICDE.2017.109,

https://dissem.in/p/83374154/keystoneml-optimizing-pipelines-for-large-scale-advanced-analytics,

https://academic.microsoft.com/#/detail/2547386789