Amazon Technologies, Inc.

Patrick Ian Wilson

Bhavesh Anil Doshi

Umar Farooq

Andrea Olgiati

Rahul Bhotika

Amit Adam

Shai Mazor

Vivek Bhadauria

Anand Dhandhania

Dmitry Vladimir Zhiyanov

Lichao Wang

Wendy Tse

Gururaj Kosuru

Kurniawan Kurniawan

Srinivas K Yellala

Jong Wan Kim

Systems and methods are provided herein for generating a synthetic training data set that can be used to train a machine-learning model to identify when two addresses match (e.g., when a user-defined address and an authoritative address match). The addresses may each be tokenized. Each candidate address can be scored based on a number of common tokens it shares with the user-defined address. The highest-scored candidate address may be selected as a matching address for the user-defined address. In some embodiments, a number of the remaining candidate address can be selected as negative examples (e.g., candidate addresses that do not match the user-defined address) based on, for example, historical delivery information associated with the corresponding addresses. In this manner, an expansive training data set may be generated using addresses associated with user profiles of an online service provider and a set of authoritative addresses obtained from an authoritative source.

Techniques for generating a training data set for a machine-learning model

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

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

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Renton, WA, United States of America

Patrick Ian Wilson