כתבה
arXiv cs.LG ·
FlexiFlow: שינוי דגלי עבודה בזמן רצוף בזרימות ML
FlexiFlow: Bandit-based Model Switching in ML Workflows
FlexiFlow משפרת את דיוק זרימות ML עד 23% על ידי שינוי דגלי עבודה בזמן רצוף. המערכת משתמשת במודלי Gemini ו-LangGraph כדי לשפר את דיוק הזרימות.
תקציר מקורי באנגליתarXiv:2610.07286v1 Announce Type: new 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 many cases, alternate models could perform better on specific subsets of data where a primary model underperforms. Our experiments with real ML workflows indeed show that switching models improves workflow accuracy by up to 23%. Yet, current systems lack the ability to adaptively switch between models based on performance, forcing users to manually test models in sequence. We present FlexiFlow, a dataflow system that dynamically switches between alternate models when the current model exhibits low accuracy. FlexiFlo
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arxiv.org
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