公开(公告)号：US10572321B2

公开(公告)日：2020-02-25

申请号：US15919178

申请日：2018-03-12

Applicant: Amazon Technologies, Inc.

Inventor： Vineet Khare ,  Alexander Johannes Smola ,  Craig Wiley

IPC: G06F3/00 ,  G06F9/54

Abstract: Techniques for providing and servicing listed repository items such as algorithms, data, models, pipelines, and/or notebooks are described. In some examples, web services provider receives a request for a listed repository item from a requester, the request indicating at least a category of the repository item and each listing of a repository item includes an indication of a category that the listed repository item belongs to and a storage location of the listed repository item, determines a suggestion of at least one listed repository item based on the request, and provides the suggestion of the at least one listed repository item to the requester.

公开(公告)号：US12067432B2

公开(公告)日：2024-08-20

申请号：US17572470

申请日：2022-01-10

Applicant: Amazon Technologies, Inc.

Inventor： Vineet Khare ,  Alexander Johannes Smola ,  Craig Wiley

IPC: G06F9/44 ,  G06F9/455 ,  G06F9/50 ,  G06F9/54 ,  G06N20/00 ,  G06Q10/00

CPC classification number: G06F9/547 ,  G06F9/45558 ,  G06F9/5027 ,  G06N20/00 ,  G06Q10/00 ,  G06F2009/45575

Abstract: Techniques for providing and servicing listed repository items such as algorithms, data, models, pipelines, and/or notebooks are described. In some examples, web services provider receives a request for a listed repository item from a requester, the request indicating at least a category of the repository item and each listing of a repository item includes an indication of a category that the listed repository item belongs to and a storage location of the listed repository item, determines a suggestion of at least one listed repository item based on the request, and provides the suggestion of the at least one listed repository item to the requester.

公开(公告)号：US11257002B2

公开(公告)日：2022-02-22

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: G06F16/90 ,  G06F16/95 ,  G06F21/57 ,  G10L15/07 ,  G06N20/00 ,  G06N5/04 ,  G06N3/04 ,  G06N3/08

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.

公开(公告)号：US12277480B1

公开(公告)日：2025-04-15

申请号：US15934091

申请日：2018-03-23

Applicant: Amazon Technologies, Inc.

Inventor： Edo Liberty ,  Thomas Albert Faulhaber, Jr. ,  Zohar Karnin ,  Gowda Dayananda Anjaneyapura Range ,  Amir Sadoughi ,  Swaminathan Sivasubramanian ,  Alexander Johannes Smola ,  Stefano Stefani ,  Craig Wiley

IPC: G06N20/00 ,  G06F9/48

Abstract: Techniques for in-flight scaling of machine learning training jobs are described. A request to execute a machine learning (ML) training job is received within a provider network, and the ML training job is executed using a first one or more compute instances. Upon a determination that a performance characteristic of the ML training job satisfies a scaling condition, a second one or more compute instances are added to the ML training job while the first one or more compute instances continue to execute portions of the ML training job.

公开(公告)号：US11126927B2

公开(公告)日：2021-09-21

申请号：US15822061

申请日：2017-11-24

Applicant: Amazon Technologies, Inc.

Inventor： Stefano Stefani ,  Steven Andrew Loeppky ,  Thomas Albert Faulhaber, Jr. ,  Craig Wiley ,  Edo Liberty

IPC: G06N3/02 ,  G06F9/455 ,  G06N20/00 ,  G06F9/50 ,  G06Q10/10 ,  H04L12/24 ,  H04L12/26 ,  H04L29/12

Abstract: Techniques for auto-scaling hosted machine learning models for production inference are described. A machine learning model can be deployed in a hosted environment such that the infrastructure supporting the machine learning model scales dynamically with demand so that performance is not impacted. The model can be auto-scaled using reactive techniques or predictive techniques.

公开(公告)号：US12045693B2

公开(公告)日：2024-07-23

申请号：US16001548

申请日：2018-06-06

Applicant: Amazon Technologies, Inc.

Inventor： Charles Drummond Swan ,  Edo Liberty ,  Steven Andrew Loeppky ,  Stefano Stefani ,  Alexander Johannes Smola ,  Swaminathan Sivasubramanian ,  Craig Wiley ,  Richard Shawn Bice ,  Thomas Albert Faulhaber, Jr. ,  Taylor Goodhart

IPC: G06N20/00 ,  G06F9/455

CPC classification number: G06N20/00 ,  G06F9/45558 ,  G06F2009/45595

Abstract: Techniques for using scoring algorithms utilizing containers for flexible machine learning inference are described. In some embodiments, a request to host a machine learning (ML) model within a service provider network on behalf of a user is received, the request identifying an endpoint to perform scoring using the ML model. An endpoint is initialized as a container running on a virtual machine based on a container image and used to score data and return a result of said scoring to a user device.

公开(公告)号：US11537439B1

公开(公告)日：2022-12-27

申请号：US15934046

申请日：2018-03-23

Applicant: Amazon Technologies, Inc.

Inventor： Edo Liberty ,  Thomas Albert Faulhaber, Jr. ,  Zohar Karnin ,  Gowda Dayananda Anjaneyapura Range ,  Amir Sadoughi ,  Swaminathan Sivasubramanian ,  Alexander Johannes Smola ,  Stefano Stefani ,  Craig Wiley

IPC: G06F3/00 ,  G06F9/50

Abstract: Techniques for intelligent compute resource selection and utilization for machine learning training jobs are described. At least a portion of a machine learning (ML) training job is executed a plurality of times using a plurality of different resource configurations, where each of the plurality of resource configurations includes at least a different type or amount of compute instances. A performance metric is measured for each of the plurality of the executions, and can be used along with a desired performance characteristic to generate a recommended resource configuration for the ML training job. The ML training job is executed using the recommended resource configuration.

公开(公告)号：US11249827B2

公开(公告)日：2022-02-15

申请号：US16799443

申请日：2020-02-24

Applicant: Amazon Technologies, Inc.

Inventor： Vineet Khare ,  Alexander Johannes Smola ,  Craig Wiley

IPC: G06F9/44 ,  G06F9/54 ,  G06F9/50 ,  G06F9/455 ,  G06N20/00 ,  G06Q10/00

Abstract: Techniques for providing and servicing listed repository items such as algorithms, data, models, pipelines, and/or notebooks are described. In some examples, web services provider receives a request for a listed repository item from a requester, the request indicating at least a category of the repository item and each listing of a repository item includes an indication of a category that the listed repository item belongs to and a storage location of the listed repository item, determines a suggestion of at least one listed repository item based on the request, and provides the suggestion of the at least one listed repository item to the requester.