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• W2963065629 abstract "Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training-time, prediction serving has different requirements such as low latency, high throughput and graceful performance degradation under heavy load. Current prediction serving systems consider models as black boxes, whereby prediction-time-specific optimizations are ignored in favor of ease of deployment. In this paper, we present PRETZEL, a prediction serving system introducing a novel white box architecture enabling both end-to-end and multi-model optimizations. Using production-like model pipelines, our experiments show that PRETZEL is able to introduce performance improvements over different dimensions; compared to state-of-the-art approaches PRETZEL is on average able to reduce 99th percentile latency by 5.5× while reducing memory footprint by 25×, and increasing throughput by 4.7×." @default.
• W2963065629 created "2019-07-30" @default.
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• W2963065629 date "2018-10-08" @default.
• W2963065629 modified "2023-09-23" @default.
• W2963065629 title "Pretzel: opening the black box of machine learning prediction serving systems" @default.
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• W2963065629 doi "https://doi.org/10.5555/3291168.3291213" @default.
• W2963065629 hasPublicationYear "2018" @default.
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