公开(公告)号：US11295165B1

公开(公告)日：2022-04-05

申请号：US16587400

申请日：2019-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Vinayak Ashutosh Agarwal ,  Jason Lenox Copeland ,  Matthew James Wood ,  Long Gao ,  Ricardo Elizondo Costa ,  Jiajun Sun ,  Naga Krishna Teja Komma

IPC: G06N20/00 ,  G06K9/62 ,  G06K9/00

Abstract: Techniques for training a machine learning model based on captured images are described. A method described include filtering a first set of collected images using one or more machine learning models; labeling the first set of filtered, collected images using a data labeling service using a service of the provider network; training a machine learning model from a machine learning algorithm using the first set of filtered, collected images using a service of the provider network; and causing deployment of the trained machine learning model onto a device.

公开(公告)号：US11960935B2

公开(公告)日：2024-04-16

申请号：US16020819

申请日：2018-06-27

Applicant: Amazon Technologies, Inc.

Inventor： Sudipta Sengupta ,  Poorna Chand Srinivas Perumalla ,  Dominic Rajeev Divakaruni ,  Nafea Bshara ,  Leo Parker Dirac ,  Bratin Saha ,  Matthew James Wood ,  Andrea Olgiati ,  Swaminathan Sivasubramanian

IPC: G06F9/50 ,  G06F8/65 ,  G06F9/455 ,  G06N5/046 ,  G06N20/00 ,  G06T1/20

CPC classification number: G06F9/5027 ,  G06F8/65 ,  G06F9/45558 ,  G06N5/046 ,  G06N20/00 ,  G06T1/20 ,  G06F2009/4557 ,  G06F2009/45583 ,  G06F2009/45595

Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes attaching a first set of one or more accelerator slots of an accelerator appliance to an application instance of a multi-tenant provider network according to an application instance configuration, the application instance configuration to define per accelerator slot capabilities to be used by an application of the application instance, wherein the multi-tenant provider network comprises a plurality of computing devices configured to implement a plurality of virtual compute instances, and wherein the first set of one or more accelerator slots is implemented using physical accelerator resources accessible to the application instance; while performing inference using the loaded machine learning model of the application using the first set of one or more accelerator slots on the attached accelerator appliance, managing resources of the accelerator appliance using an accelerator appliance manager of the accelerator appliance.

公开(公告)号：US11853401B1

公开(公告)日：2023-12-26

申请号：US15997948

申请日：2018-06-05

Applicant: Amazon Technologies, Inc.

Inventor： Nagajyothi Nookula ,  Poorna Chand Srinivas Perumalla ,  Matthew James Wood

IPC: G06F3/04842 ,  G06N20/00 ,  G06F8/34 ,  H04L67/10 ,  G06F18/40 ,  G06F18/214 ,  G06F18/21 ,  G06N3/045

CPC classification number: G06F18/40 ,  G06F3/04842 ,  G06F8/34 ,  G06F18/214 ,  G06F18/217 ,  G06F18/2163 ,  G06N3/045 ,  G06N20/00 ,  H04L67/10

Abstract: Techniques for machine learning (ML) model training and deployment using model building blocks via graphical user interfaces (GUIs) are described. Users can use a GUI provided by an electronic device to select and configure ML aspects for one or more ML models to be trained using identified training data. The electronic device can send a request to cause a model construction service to train one or more ML models based on the user configuration, return results of the training to the user within the GUI, and deploy one or more of the ML models.

公开(公告)号：US11768914B1

公开(公告)日：2023-09-26

申请号：US17711391

申请日：2022-04-01

Applicant: Amazon Technologies, Inc.

Inventor： Vinayak Ashutosh Agarwal ,  Jason Lenox Copeland ,  Matthew James Wood ,  Long Gao ,  Ricardo Elizondo Costa ,  Jiajun Sun ,  Naga Krishna Teja Komma

IPC: G06N20/00 ,  G06F18/214 ,  G06V10/94 ,  G06F18/21 ,  G06F18/2413

CPC classification number: G06F18/214 ,  G06F18/217 ,  G06F18/2413 ,  G06N20/00 ,  G06V10/95

Abstract: Techniques for training a machine learning model based on captured images are described. A method described include filtering a first set of collected images using one or more machine learning models; labeling the first set of filtered, collected images using a data labeling service using a service of the provider network; training a machine learning model from a machine learning algorithm using the first set of filtered, collected images using a service of the provider network; and causing deployment of the trained machine learning model onto a device.

公开(公告)号：US11599821B2

公开(公告)日：2023-03-07

申请号：US16020776

申请日：2018-06-27

Applicant: Amazon Technologies, Inc.

Inventor： Sudipta Sengupta ,  Poorna Chand Srinivas Perumalla ,  Dominic Rajeev Divakaruni ,  Nafea Bshara ,  Leo Parker Dirac ,  Bratin Saha ,  Matthew James Wood ,  Andrea Olgiati ,  Swaminathan Sivasubramanian

IPC: G06N20/00 ,  G06F9/50 ,  G06N5/04 ,  G06F9/455 ,  G06N3/04 ,  G06N3/063

Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes receiving an application instance configuration, an application of the application instance to utilize a portion of an attached accelerator during execution of a machine learning model and the application instance configuration including: an indication of the central processing unit (CPU) capability to be used, an arithmetic precision of the machine learning model to be used, an indication of the accelerator capability to be used, a storage location of the application, and an indication of an amount of random access memory to use.

公开(公告)号：US11494621B2

公开(公告)日：2022-11-08

申请号：US16020788

申请日：2018-06-27

Applicant: Amazon Technologies, Inc.

Inventor： Sudipta Sengupta ,  Poorna Chand Srinivas Perumalla ,  Dominic Rajeev Divakaruni ,  Nafea Bshara ,  Leo Parker Dirac ,  Bratin Saha ,  Matthew James Wood ,  Andrea Olgiati ,  Swaminathan Sivasubramanian

IPC: G06N3/063 ,  G06N3/08

Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes receiving an application instance configuration, an application of the application instance to utilize a portion of an attached accelerator during execution of a machine learning model and the application instance configuration including an arithmetic precision of the machine learning model to be used in determining the portion of the accelerator to provision; provisioning the application instance and the portion of the accelerator attached to the application instance, wherein the application instance is implemented using a physical compute instance in a first location, wherein the portion of the accelerator is implemented using a physical accelerator in the second location; loading the machine learning model onto the portion of the accelerator; and performing inference using the loaded machine learning model of the application using the portion of the accelerator on the attached accelerator.

公开(公告)号：US11422863B2

公开(公告)日：2022-08-23

申请号：US16020810

申请日：2018-06-27

Applicant: Amazon Technologies, Inc.

Inventor： Sudipta Sengupta ,  Poorna Chand Srinivas Perumalla ,  Dominic Rajeev Divakaruni ,  Nafea Bshara ,  Leo Parker Dirac ,  Bratin Saha ,  Matthew James Wood ,  Andrea Olgiati ,  Swaminathan Sivasubramanian

IPC: G06F9/50 ,  G06N5/04 ,  G06F9/455 ,  G06F9/38 ,  G06N20/00

Abstract: Implementations detailed herein include description of a computer-implemented method. In an implementation, the method at least includes provisioning an application instance and portions of at least one accelerator attached to the application instance to execute a machine learning model of an application of the application instance; loading the machine learning model onto the portions of the at least one accelerator; receiving scoring data in the application; and utilizing each of the portions of the attached at least one accelerator to perform inference on the scoring data in parallel and only using one response from the portions of the accelerator.