Amazon Technologies, Inc.

Lakshmi Naarayanan Ramakrishnan

Andrea Olgiati

Stefano Stefani

Saurabh Gupta

Ramyanshu Datta

Wei You

Kohen Berith Chia

Kunal Mehrotra

Arvind Sowmyan

Ishaaq Chandy

Vladimir Zhukov

Gowda Dayananda Anjaneyapura Range

Arun Babu Nagarajan

Leo Parker Dirac

Maximiliano Maccanti

Mukesh Karki

Zohar Karnin

Tanya Bansal

Laurence Louis Eric Rouesnel

Vikas Kumar

Rahul Raghavendra Huilgol

Urvashi Chowdhary

Xingyuan Wang

Alex Tang

Vidhi Kastuar

Denis Davydenko

Jeffrey John Geevarghese

Amol Ashok Lele

Srinivasan Sankaran

Zhihan Li

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.

Client-configurable retention periods for machine learning service-managed resources

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.

Resource management techniques to reduce startup overhead for machine learning tasks

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.

Isolation techniques at execution platforms used for sensitive data analysis

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.

Debugging and profiling of machine learning model training

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.

Fully managed repository to create, version, and share curated data for machine learning development

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.

Automated problem detection for machine learning models

Growing community of inventors

Redmond, WA, United States of America

Lakshmi Naarayanan Ramakrishnan