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- W2893890695 abstract "Many machine learning applications operate in dynamic environments that change over time, in which models must be continually updated to capture the recent trend in data. However, most of today's learning frameworks perform training offline, without a system support for continual model updating. In this paper, we design and implement Continuum, a general-purpose platform that streamlines the implementation and deployment of continual model updating across existing learning frameworks. In pursuit of fast data incorporation, we further propose two update policies, cost-aware and best-effort, that judiciously determine when to perform model updating, with and without accounting for the training cost (machine-time), respectively. Theoretical analysis shows that cost-aware policy is 2-competitive. We implement both polices in Continuum, and evaluate their performance through EC2 deployment and trace-driven simulations. The evaluation shows that Continuum results in reduced data incorporation latency, lower training cost, and improved model quality in a number of popular online learning applications that span multiple application domains, programming languages, and frameworks." @default.
- W2893890695 created "2018-10-05" @default.
- W2893890695 creator A5023717729 @default.
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- W2893890695 date "2018-10-11" @default.
- W2893890695 modified "2023-09-25" @default.
- W2893890695 title "Continuum" @default.
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- W2893890695 doi "https://doi.org/10.1145/3267809.3267817" @default.
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