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公开(公告)号:US11449797B1
公开(公告)日:2022-09-20
申请号:US16579744
申请日:2019-09-23
Applicant: Amazon Technologies, Inc.
Inventor: Kurniawan Kurniawan , Bhavesh A. Doshi , Umar Farooq , Patrick Ian Wilson , Vivek Bhadauria
Abstract: 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.
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公开(公告)号:US11269888B1
公开(公告)日:2022-03-08
申请号:US15362591
申请日:2016-11-28
Applicant: Amazon Technologies, Inc.
Inventor: Umar Farooq , Rishabh Animesh
Abstract: A data storage system implements techniques to efficiently store and retrieve structured data. For example, structured data is transformed into correlated segments, which are then redundancy coded and archived in a correlated fashion. The characteristics of the redundancy code used enable flexible handling of the archived data without excessive latency.
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公开(公告)号:US11137980B1
公开(公告)日:2021-10-05
申请号:US15277922
申请日:2016-09-27
Applicant: Amazon Technologies, Inc.
Inventor: Rishabh Animesh , Adam Frederick Brock , Umar Farooq , James Caleb Kirschner
Abstract: A data storage system implements techniques for efficient retrieval of data stored thereon, using time of upload or another monotonically increasing variable as a key or identifier for the data to be stored and/or retrieved. Data is sorted according to, e.g., upload time, and the data is addressed with respect to time of upload and byte offset within the archive.
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公开(公告)号:US10949661B2
公开(公告)日:2021-03-16
申请号:US16198040
申请日:2018-11-21
Applicant: Amazon Technologies, Inc.
Inventor: Rahul Bhotika , Shai Mazor , Amit Adam , Wendy Tse , Andrea Olgiati , Bhavesh Doshi , Gururaj Kosuru , Patrick Ian Wilson , Umar Farooq , Anand Dhandhania
IPC: G06K9/00
Abstract: 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.
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