FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply

12 June 2020

Papers citing "FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply"

10 / 10 papers shown

Title
What is the Role of Small Models in the LLM Era: A Survey Lihu Chen Gaël Varoquaux ALM 69 23 0 10 Sep 2024
MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs Quang H. Nguyen Duy C. Hoang Juliette Decugis Saurav Manchanda Nitesh Chawla Khoa D. Doan Khoa D. Doan 52 8 0 15 Jul 2024
RouteLLM: Learning to Route LLMs with Preference Data Isaac Ong Amjad Almahairi Vincent Wu Wei-Lin Chiang Tianhao Wu Joseph E. Gonzalez M. W. Kadous Ion Stoica 81 77 0 26 Jun 2024
OCCAM: Towards Cost-Efficient and Accuracy-Aware Classification Inference Dujian Ding Bicheng Xu L. Lakshmanan VLM 49 1 0 06 Jun 2024
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions Lingjiao Chen Zhihua Jin Sabri Eyuboglu Christopher Ré Matei A. Zaharia James Zou 56 9 0 18 Sep 2022
Creating Training Sets via Weak Indirect Supervision Jieyu Zhang Bohan Wang Xiangchen Song Yujing Wang Yaming Yang Jing Bai Alexander Ratner OffRL 54 17 0 07 Oct 2021
Data Pricing in Machine Learning Pipelines Zicun Cong Xuan Luo J. Pei Feida Zhu Yong Zhang 28 47 0 18 Aug 2021
Did the Model Change? Efficiently Assessing Machine Learning API Shifts Lingjiao Chen Tracy Cai Matei A. Zaharia James Zou 20 17 0 29 Jul 2021
MLDemon: Deployment Monitoring for Machine Learning Systems Antonio A. Ginart Martin Jinye Zhang James Zou 54 18 0 28 Apr 2021
Efficient Online ML API Selection for Multi-Label Classification Tasks Lingjiao Chen Matei A. Zaharia James Zou 45 17 0 18 Feb 2021