Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Bhavesh Anil Doshi

Anand Chakraborty

Miles Childs Kaufmann

Arvinth Ravi

Phillip H Pruett, Iv

Nikhil Menon

Shikha Sukumaran

Suresh Kumar Golconda

Leon Thrane

Alexander Hopmann

Nikita Voronkov

Viktoriya Taranov

Ramanathan Somasundaram

Daniel Blood

Artsiom Kokhan

Tarkan Sevilmis

Siddharth Rajendra Shah

Laura Varnum Finney

Anand Dhandhania

Prashant Hegde

Umar Farooq

Neil David Cooper

Kevin Robert McGehee

John Ryan Sherritt

Patrick Ian Wilson

Thayn Moore

Marc Stephen Olson

James Michael Thompson

Yue Liu

Andrea Olgiati

Rahul Bhotika

Benjamin Arthur Hawks

Amit Adam

Avnish Sikka

Matthew Joseph Cole

Shai Mazor

David Paul Ramos

Wei Yu

Michael Christopher Wenneman

Vivek Bhadauria

Sonjeev Jahagirdar

Ganesh Viswanathan

Emilie Noelle McConville

Utkarsh Prateek

Wendy Tse

Nengwu Zhu

Joshua Stephen Ullom

Hyen Vui Chung

Ankur Datta

Gururaj Kosuru

Dmytro Ivashchenko

Qihui Li

Nathan Manning

Yubai Di

Michael Vanne

Antonio Marcos Da Silva, Jr

Kai Yiu Luk

Kurniawan Kurniawan

Sid R Shah

Antonio Marcos Da Silva

An object-based data storage service receives a request to store a data object in association with a smart data storage tier. Based at least in part on characteristics of the data object, the object-based data storage service identifies and stores the data object in a first location corresponding to a first data storage tier. The object-based data storage service monitors access to the data object to identify a second set of characteristics of the data object. This second set of characteristics is used to determine that the data object is to be transitioned to a second data storage tier. The object-based data storage service, based at least in part on this determination, stores the data object in a second location corresponding to the second data storage tier.

Smart data storage tiers for data object transitioning

An object-based data storage service receives a request to store a data object in a first location corresponding to a first data storage tier. The request may specify a parameter to enable transitioning of the data object to another data storage tier. In response to the request, the object-based data storage service stores the data object in the first location and monitors access of the data object to determine usage data associated with the data object. The object-based data storage service processes the usage data to determine that the data object is to be transitioned to a second data storage tier. As a result of this determination, the object-based data storage service transitions the data object to a second location corresponding to the second data storage tier.

Automated tier-based transitioning for data objects

A secondary location is configured as a recovery service for a primary location of the service. The secondary location is maintained in a warm state that is configured to replace the primary location in a case of a failover. During normal operation, the secondary location is automatically updated to reflect a current state of the primary location that is actively servicing user load. Content changes to the primary location are automatically reflected to the secondary location. System changes applied to the primary location are automatically applied to the secondary location. For example, removing/adding machines, updating machine/role assignments, removing adding/database are automatically applied to the secondary location such that the secondary location substantially mirrors the primary location. After a failover to the secondary location, the secondary location becomes the primary location and begins to actively service the user load.

Automatic configuration of a recovery service

Technologies are described for analyzing interactions with data objects stored by a network-based storage service. The analysis of the interactions can identify patterns of the data object interactions and outcomes that can result from the patterns. Models can be developed that include the patterns and the outcomes corresponding to the patterns. As requests related to data object interactions are subsequently obtained by the system, the requests can be analyzed with respect to the models to identify an outcome that may be associated with the requests.

Analysis of interactions with data objects stored by a network-based storage service

An indication of training artifacts for a machine learning model to be trained with an input data set having an access restriction is obtained. A representation of a software execution environment containing the artifacts is deployed to a computing platform within an isolated resource group which satisfies the access restriction. A trained version of the machine learning model is generated at the computing platform, and transferred outside the isolated resource group.

Secure machine learning workflow automation using isolated resources

Smart data storage hers for data object transitioning

Techniques for a framework for building, orchestrating, and deploying complex, large-scale Machine Learning (ML) or deep learning (DL) inference applications is described. A ML application orchestration service is disclosed that enables the construction, orchestration, and deployment of complex ML inference applications in a provider network. The disclosed service provides customers with the ability to define machine learning (ML) models and define transformation operations on data before and/or after being provided to the ML models to construct a complex ML inference application. The service provides a framework for the orchestration (co-ordination) of the workflow logic (e.g., of the request and/or response flows) involved in building and deploying a complex ML inference application in the provider network.

Framework for building, orchestrating and deploying large-scale machine learning applications

An extendable augmented reality (AR) system for recognizing objects. The AR system may incorporate plugins to improve the AR system's ability to recognize objects, such as by adding external matchable data or using external recognition algorithms. The plugins may be installed/activated dynamically by a user to expand the system's AR capabilities. The AR system may also incorporate plugins to improve the AR system's post-recognition features such as supplying a user with supplemental content or follow-up functionality. The AR system may filter communications with the plugins to centralize identification processes, to shield plugins, and to ensure plugins are only activated at appropriate times.

Extendable architecture for augmented reality system

Techniques for layout-agnostic complex document processing are described. A document processing service can analyze documents that do not adhere to defined layout rules in an automated manner to determine the content and meaning of a variety of types of segments within the documents. The service may chunk a document into multiple chunks, and operate upon the chunks in parallel by identifying segments within each chunk, classifying the segments into segment types, and processing the segments using special-purpose analysis engines adapted for the analysis of particular segment types to generate results that can be aggregated into an overall output for the entire document that captures the meaning and context of the document text.

Layout-agnostic complex document processing system

Growing community of inventors

Redmond, WA, United States of America

Bhavesh Anil Doshi