公开(公告)号：US11797878B2

公开(公告)日：2023-10-24

申请号：US15821585

申请日：2017-11-22

Applicant: Amazon Technologies, Inc.

Inventor： Thomas Albert Faulhaber, Jr. ,  Stefano Stefani ,  Owen Thomas

IPC: G06F9/455 ,  G06N20/00 ,  G06F9/50 ,  G06N20/10

CPC classification number: G06N20/00 ,  G06F9/45558 ,  G06F9/5072 ,  G06N20/10 ,  G06F2009/4557

Abstract: A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and/or transmitted to user devices via the network.

公开(公告)号：US20230281276A1

公开(公告)日：2023-09-07

申请号：US18171244

申请日：2023-02-17

Applicant: Amazon Technologies, Inc.

Inventor： Owen Thomas ,  Kenneth O Henderson, JR. ,  Sumit Thakur ,  Glenn Danthi ,  Hugh Payton Staub ,  Thomas Albert Faulhaber ,  Vladimir Zhukov

IPC: G06F18/214 ,  G06N20/20 ,  G06F9/54 ,  G06F16/14 ,  G06F16/16 ,  G06F9/48 ,  G06F9/451 ,  G06F8/71 ,  G06N20/00 ,  G06F18/2113

CPC classification number: G06F18/2148 ,  G06N20/20 ,  G06F9/54 ,  G06F16/144 ,  G06F16/164 ,  G06F9/485 ,  G06F9/451 ,  G06F8/71 ,  G06N20/00 ,  G06F18/2113 ,  G06F18/2155

Abstract: Artifacts, including parameters are data sets, associated with experiment tasks are stored at an experiment management service. A query specifying a particular value of a parameter and a particular data set is received, and an indication of an experiment result associated with the particular data set and the particular parameter value is provided.

公开(公告)号：US11586847B1

公开(公告)日：2023-02-21

申请号：US16894707

申请日：2020-06-05

Applicant: Amazon Technologies, Inc.

Inventor： Owen Thomas ,  Kenneth O Henderson, Jr. ,  Sumit Thakur ,  Glenn Danthi ,  Hugh Payton Staub ,  Thomas Albert Faulhaber ,  Vladimir Zhukov

IPC: G06N20/00 ,  G06K9/62 ,  G06N20/20 ,  G06F9/54 ,  G06F16/14 ,  G06F16/16 ,  G06F9/48 ,  G06F9/451 ,  G06F8/71

Abstract: Artifacts, including parameters are data sets, associated with experiment tasks are stored at an experiment management service. A query specifying a particular value of a parameter and a particular data set is received, and an indication of an experiment result associated with the particular data set and the particular parameter value is provided.

公开(公告)号：US11977958B2

公开(公告)日：2024-05-07

申请号：US15821585

申请日：2017-11-22

Applicant: Amazon Technologies, Inc.

Inventor： Thomas Albert Faulhaber, Jr. ,  Stefano Stefani ,  Owen Thomas

IPC: G06F9/455 ,  G06F9/50 ,  G06N20/00 ,  G06N20/10

CPC classification number: G06N20/00 ,  G06F9/45558 ,  G06F9/5072 ,  G06N20/10 ,  G06F2009/4557

Abstract: A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and/or transmitted to user devices via the network.

公开(公告)号：US20190156244A1

公开(公告)日：2019-05-23

申请号：US15821585

申请日：2017-11-22

Applicant: Amazon Technologies, Inc.

Inventor： Thomas Albert Faulhaber, Jr. ,  Stefano Stefani ,  Owen Thomas

IPC: G06N99/00 ,  G06F9/455

Abstract: A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and/or transmitted to user devices via the network.