公开(公告)号：US20210097444A1

公开(公告)日：2021-04-01

申请号：US16587301

申请日：2019-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Tanya BANSAL ,  Piali DAS ,  Leo Parker DIRAC ,  Fan LI ,  Zohar KARNIN ,  Philip GAUTIER ,  Patricia GRAO GIL ,  Laurence Louis Eric ROUESNEL ,  Ravikumar Anantakrishnan VENKATESWAR ,  Orchid MAJUMDER ,  Stefano Stefani ,  Vladimir Zhukov

IPC: G06N20/20 ,  G06F9/50 ,  G06F9/54

Abstract: Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.

公开(公告)号：US20190156247A1

公开(公告)日：2019-05-23

申请号：US15919628

申请日：2018-03-13

Applicant: Amazon Technologies, Inc.

Inventor： Thomas Albert FAULHABER, JR. ,  Edo LIBERTY ,  Stefano STEFANI ,  Zohar KARNIN ,  Craig WILEY ,  Steven Andrew LOEPPKY ,  Swaminathan SIVASUBRAMANIAN ,  Alexander Johannes SMOLA ,  Taylor GOODHART

IPC: G06N99/00 ,  G06N5/04

Abstract: Techniques for dynamic accuracy-based experimentation and deployment of machine learning (ML) models are described. Inference traffic flowing to ML models and the accuracy of the models is analyzed and used to ensure that better performing models are executed more often via model selection. A predictive component can evaluate which model is more likely to be accurate for certain input data elements. Ensemble techniques can combine inference results of multiple ML models to aim to achieve a better overall result than any individual model could on its own.