Amazon Technologies, Inc.

Pedrito Uriah Maynard-Zhang

Anurag Windlass Gupta

Mehul A Shah

Mehul Y Shah

Prajakta Datta Damle

Stephen Michael Ash

Timothy Jones

Andrew Borthwick

Juliana Saussy

Abhishek Sharma

Shehzad Qureshi

Sergei Dobroshinsky

Chethan Kommaranahalli Rudramuni

Adam Lawrence Joseph Heinermann

Alaykumar Navinchandra Desai

Daniel Bruce Lloyd

Llewellyn James Mason

Samuel A Minter

Specified performance attributes may be used to configure machine learning transformations for ETL jobs. Performance attributes for a machine learning pipeline that applies a model to as part of a transformation for an ETL job may be used to configure a parameter in a stage of the machine learning pipeline. The configured stage may then be used when training the model. The trained machine learning pipeline may then be applied as part of a transformation operation included in an ETL job performed by the ETL system.

Using specified performance attributes to configure machine learning pipepline stages for an ETL job

Using specified performance attributes to configure machine learning pipeline stages for an ETL job

A decision tree is provided as a machine learning classifier to predict a user attribute, such as a geographical location of a user, based on a network address. More specifically, the decision tree is constructed via machine learning on a set of sample data that reflects a relationship between a network address and a user attribute of a 'known user' whose profile information is recognizable. For a given network address, the decision tree can be used as a machine learning classifier to predict the most likely user attribute of a potential user. With the predicted attribute, a network service can target a group of potential users for various campaigns without recognizing the identities of the potential users.

Predicting geographic location associated with network address

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Issaquah, WA, United States of America

Pedrito Uriah Maynard-Zhang