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English(EN) FlexiFlow: Bandit-based Model Switching in ML Workflows

FlexiFlow系统动态切换ML模型以提高准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了FlexiFlow，一个新颖的数据流系统，旨在推理过程中动态切换不同的机器学习模型。这种自适应切换旨在通过利用替代模型处理主要模型表现不佳的特定数据子集来提高整体工作流的准确性和效率。FlexiFlow采用了一种新颖的多臂赌徒方法，结合了模型运行时间和准确性指标，以学习最优的切换策略，其性能优于ML工作流的标准Thompson采样。 AI

影响 通过动态适应数据特征的模型选择，提高了ML工作流的效率和准确性。

排序理由 该集群包含一篇详细介绍机器学习工作流新系统和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

FlexiFlow系统动态切换ML模型以提高准确性

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该集群包含一篇详细介绍机器学习工作流新系统和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Abhilash Jindal, Todd Nief, Bhanu Prakash Vangala, Shankaradithyaa V, Tvisha Malik, Anshik Sahu, Aaron Schein, Amitabh Chaudhary, Tanu Malik ·

    FlexiFlow：ML工作流中的基于Bandit的模型切换

    arXiv:2610.07286v1 Announce Type: cross Abstract: Model optimizations help improve inference performance and accuracy of ML workflows. However, relying on a single model to perform inference across all data batches often fails to maximize accuracy and thus overall performance. In…

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