公开(公告)号：US20240272953A1

公开(公告)日：2024-08-15

申请号：US18642668

申请日：2024-04-22

Applicant: Amazon Technologies, Inc.

Inventor： Ramyanshu Datta ,  Ishaaq Chandy ,  Arvind Sowmyan ,  Wei You ,  Kunal Mehrotra ,  Kohen Berith Chia ,  Andrea Olgiati ,  Lakshmi Naarayanan Ramakrishnan ,  Saurabh Gupta

IPC: G06F9/50

CPC classification number: G06F9/5038 ,  G06F9/5022 ,  G06F9/5055

Abstract: A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.

公开(公告)号：US20240177049A1

公开(公告)日：2024-05-30

申请号：US18058840

申请日：2022-11-25

Applicant: Amazon Technologies, Inc.

Inventor： Lakshmi Naarayanan Ramakrishnan ,  Andrea Olgiati ,  Ankur Mehrotra ,  Karthik Gurumoorthy Subramanya Bharathy ,  Rakesh Ramakrishnan

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Confidential tuning of pre-trained machine learning models may be provided. A request associated with a model user account to fine-tune a pre-trained machine learning model with model access restrictions may be received. The pre-trained machine learning model may be one of many pre-trained machine learning models uploaded for selection and fine-tuning. The pre-trained machine learning model may be further trained using a request specified data set, with the model access restrictions and access restrictions for the data set being enforced as part of the training. Then, the fine-tuned machine learning model may be made available for invocation by an application associated with the model user account without violating the model access restrictions and data access restrictions.

公开(公告)号：US11449798B2

公开(公告)日：2022-09-20

申请号：US16588952

申请日：2019-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Andrea Olgiati ,  Maximiliano Maccanti ,  Arun Babu Nagarajan ,  Lakshmi Naarayanan Ramakrishnan ,  Urvashi Chowdhary ,  Gowda Dayananda Anjaneyapura Range ,  Zohar Karnin ,  Laurence Louis Eric Rouesnel ,  Stefano Stefani ,  Vladimir Zhukov

IPC: G06F11/00 ,  G06N20/00 ,  G06F11/07

Abstract: Methods, systems, and computer-readable media for automated problem detection for machine learning models are disclosed. A machine learning analysis system receives data associated with use of a machine learning model. The data was collected by a machine learning inference system and comprises input to the model or a plurality of inferences representing output of the machine learning model. The machine learning analysis system performs analysis of the data associated with the use of the machine learning model. The machine learning analysis system detects one or more problems associated with the use of the machine learning model based at least in part on the analysis. The machine learning analysis system initiates one or more remedial actions associated with the one or more problems associated with the use of the machine learning model.

公开(公告)号：US12052285B1

公开(公告)日：2024-07-30

申请号：US16267332

申请日：2019-02-04

Applicant: Amazon Technologies, Inc.

Inventor： Gowda Dayananda Anjaneyapura Range ,  Srinivasan Sankaran ,  Leo Dirac ,  Lakshmi Naarayanan Ramakrishnan ,  Stefano Stefani

IPC: H04L9/40 ,  G06F9/50 ,  G06F9/54 ,  G06F21/53 ,  G06N20/00 ,  H04L9/32 ,  H04L47/78

CPC classification number: H04L63/20 ,  G06F9/5077 ,  G06F9/544 ,  G06F21/53 ,  G06N20/00 ,  H04L9/3228 ,  H04L47/78

Abstract: At a first resource to be used to perform a computing operation, a pair of execution environments is configured. I/O permissions of programs running in the different environments are based on respective sets of constraints. A program performs the operation in one of the environments, with input data being provided to the program from the second environment. A result of the operation is provided to a destination from the second environment.

公开(公告)号：US12039415B2

公开(公告)日：2024-07-16

申请号：US16588913

申请日：2019-09-30

Applicant: Amazon Technologies, Inc.

Inventor： Andrea Olgiati ,  Lakshmi Naarayanan Ramakrishnan ,  Jeffrey John Geevarghese ,  Denis Davydenko ,  Vikas Kumar ,  Rahul Raghavendra Huilgol ,  Amol Ashok Lele ,  Stefano Stefani ,  Vladimir Zhukov

IPC: G06N20/00 ,  G06N5/04 ,  G06N5/046 ,  G06F11/36

CPC classification number: G06N20/00 ,  G06N5/046 ,  G06F11/36

Abstract: Methods, systems, and computer-readable media for debugging and profiling of machine learning model training are disclosed. A machine learning analysis system receives data associated with training of a machine learning model. The data was collected by a machine learning training cluster. The machine learning analysis system performs analysis of the data associated with the training of the machine learning model. The machine learning analysis system detects one or more conditions associated with the training of the machine learning model based at least in part on the analysis. The machine learning analysis system generates one or more alarms describing the one or more conditions associated with the training of the machine learning model.

公开(公告)号：US11995476B1

公开(公告)日：2024-05-28

申请号：US17482276

申请日：2021-09-22

Applicant: Amazon Technologies, Inc.

Inventor： Ramyanshu Datta ,  Ishaaq Chandy ,  Arvind Sowmyan ,  Wei You ,  Kunal Mehrotra ,  Kohen Berith Chia ,  Andrea Olgiati ,  Lakshmi Naarayanan Ramakrishnan ,  Saurabh Gupta

IPC: G06F9/46 ,  G06F9/50

CPC classification number: G06F9/5038 ,  G06F9/5022 ,  G06F9/5055

Abstract: A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.

公开(公告)号：US12248815B2

公开(公告)日：2025-03-11

申请号：US18642668

申请日：2024-04-22

Applicant: Amazon Technologies, Inc.

Inventor： Ramyanshu Datta ,  Ishaaq Chandy ,  Arvind Sowmyan ,  Wei You ,  Kunal Mehrotra ,  Kohen Berith Chia ,  Andrea Olgiati ,  Lakshmi Naarayanan Ramakrishnan ,  Saurabh Gupta

IPC: G06F9/46 ,  G06F9/50

Abstract: A post-task-completion retention period for which a computing resource is to be retained, without de-activating the resource, on behalf of a set of requesters of machine learning tasks is determined at a machine learning service. A first task, identified at the service prior to expiration of the retention period at a first computing resource at which a second task has completed, is initiated at the first computing resource. In response to obtaining an indication of a third task and determining that a threshold criterion associated with the retention period satisfies a criterion, the third task is initiated at an additional computing resource. The additional computing resource is de-activated after the third task completes, without waiting for the retention period to expire.

公开(公告)号：US12229600B1

公开(公告)日：2025-02-18

申请号：US17482272

申请日：2021-09-22

Applicant: Amazon Technologies, Inc.

Inventor： Ramyanshu Datta ,  Zhihan Li ,  Arun Babu Nagarajan ,  Arvind Sowmyan ,  Kohen Berith Chia ,  Wei You ,  Ishaaq Chandy ,  Kunal Mehrotra ,  Andrea Olgiati ,  Lakshmi Naarayanan Ramakrishnan ,  Saurabh Gupta

IPC: G06F9/50

Abstract: Parameters of a pool of computing resources to be utilized for machine learning tasks from a set of entities are stored, including a category of the computing resources, and a post-task-completion retention period during which, after completion of a task, at least a portion of data stored at the resource is not to be deleted. A compute instance of the pool is assigned to a task requested from the set of entities after determining that one or more configuration settings of the instance satisfy a preference indicated in the request for the task, and that the retention period of the instance relative to a completion of an earlier task on the instance has not expired. A result of the task is stored.

公开(公告)号：US20240348661A1

公开(公告)日：2024-10-17

申请号：US18752544

申请日：2024-06-24

Applicant: Amazon Technologies, Inc.

Inventor： Gowda Dayananda Anjaneyapura Range ,  Srinivasan Sankaran ,  Leo Dirac ,  Lakshmi Naarayanan Ramakrishnan ,  Stefano Stefani

IPC: H04L9/40 ,  G06F9/50 ,  G06F9/54 ,  G06F21/53 ,  G06N20/00 ,  H04L9/32 ,  H04L47/78

CPC classification number: H04L63/20 ,  G06F9/5077 ,  G06F9/544 ,  G06F21/53 ,  G06N20/00 ,  H04L9/3228 ,  H04L47/78

Abstract: At a first resource to be used to perform a computing operation, a pair of execution environments is configured. I/O permissions of programs running in the different environments are based on respective sets of constraints. A program performs the operation in one of the environments, with input data being provided to the program from the second environment. A result of the operation is provided to a destination from the second environment.

公开(公告)号：US11704299B1

公开(公告)日：2023-07-18

申请号：US17205373

申请日：2021-03-18

Applicant: Amazon Technologies, Inc.

Inventor： Tanya Bansal ,  Vidhi Kastuar ,  Saurabh Gupta ,  Alex Tang ,  Lakshmi Naarayanan Ramakrishnan ,  Stefano Stefani ,  Xingyuan Wang ,  Mukesh Karki

IPC: G06F7/00 ,  G06F16/22 ,  G06F21/60 ,  G06F16/21 ,  G06F16/25 ,  G06N20/00 ,  G06F18/214

CPC classification number: G06F16/2291 ,  G06F16/219 ,  G06F16/252 ,  G06F18/214 ,  G06F21/602 ,  G06N20/00

Abstract: Techniques and technologies for providing a fully managed datastore for clients to securely store, discover, retrieve, remove, and share curated data, or features, to develop machine learning (ML) models in an efficient manner. The feature store service may provide clients with the ability to create and store feature groups that include features and associated metadata providing clients with a quick understanding of features so that they may determine which features are suitable for training ML models and/or use with ML models. The feature store service may provide first a data store configured to store the most recent values associated with a feature group, such that client can access the features and utilize ML models to make real-time predictions with low latency and high throughput, and a second datastore configured to store historical values associated with a feature group, such that a client can utilize the features to train ML models.