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• W2951312680 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." @default.
• W2951312680 created "2019-06-27" @default.
• W2951312680 creator A5012870568 @default.
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• W2951312680 date "2016-10-29" @default.
• W2951312680 modified "2023-09-26" @default.
• W2951312680 title "KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics" @default.
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• W2951312680 hasPublicationYear "2016" @default.
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