公开(公告)号：US20200311617A1

公开(公告)日：2020-10-01

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: G06N99/00 ,  G06F9/455

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：US20190155633A1

公开(公告)日：2019-05-23

申请号：US15901751

申请日：2018-02-21

Applicant: Amazon Technologies, Inc.

Inventor： Thomas Albert FAULHABER, JR. ,  Gowda Dayananda ANJANEYAPURA RANGE ,  Jeffrey John GEEVARGHESE ,  Taylor GOODHART ,  Charles Drummond SWAN

IPC: G06F9/455 ,  G06F9/50 ,  G06N5/04 ,  G06N99/00

Abstract: Techniques for packaging and deploying algorithms utilizing containers for flexible machine learning are described. In some embodiments, users can create or utilize simple containers adhering to a specification of a machine learning service in a provider network, where the containers include code for how a machine learning model is to be trained and/or executed. The machine learning service can automatically train a model and/or host a model using the containers. The containers can use a wide variety of algorithms and use a variety of types of languages, libraries, data types, etc. Users can thus implement machine learning training and/or hosting with extremely minimal knowledge of how the overall training and/or hosting is actually performed.

公开(公告)号：US20210073021A1

公开(公告)日：2021-03-11

申请号：US17067285

申请日：2020-10-09

Applicant: Amazon Technologies, Inc.

Inventor： Thomas Albert FAULHABER, JR. ,  Gowda Dayananda ANJANEYAPURA RANGE ,  Jeffrey John GEEVARGHESE ,  Taylor GOODHART ,  Charles Drummond SWAN

IPC: G06F9/455 ,  G06F9/50 ,  G06N5/04 ,  G06N20/00

Abstract: Techniques for packaging and deploying algorithms utilizing containers for flexible machine learning are described. In some embodiments, users can create or utilize simple containers adhering to a specification of a machine learning service in a provider network, where the containers include code for how a machine learning model is to be trained and/or executed. The machine learning service can automatically train a model and/or host a model using the containers. The containers can use a wide variety of algorithms and use a variety of types of languages, libraries, data types, etc. Users can thus implement machine learning training and/or hosting with extremely minimal knowledge of how the overall training and/or hosting is actually performed.

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.