Amazon Technologies, Inc.

Michael Luis Collado

Rui Chen

Serguei B Stepaniants

Vijay Pratap Singh

Siamak Irantash

Kevin Donald Kelly

Slavik Dimitrovich

Venkata Uday Kumar Kalepalli Naga

Thomas Lowell Keller

Benjamin Warren Mercier

Diwakar Chakravarthy

Murali Brahmadesam

Daniel R Parshall

Peter Varnum Commons

Sameer R Rajyaguru

Michael C Moore

Sebastian Kohlmeier

Zachary Jared Wiggins

Tyson Christopher Trautmann

Robert Andrew Kreek

Shen Li

Kyle Leon Battisti

Ajay Kant Singh

Pramod Muralidharan

Ray Chen

Donald Kane

Vignesh Kannappan

Kevin McAlister

Hancao Li

Mike C Moore

Richard Bargreen

Technologies are disclosed for determining network-accessible content changes based on computed models and providing a long term forecast of user interaction at a network accessible site based upon a short term experiment at the site. A forecast model for a period of time is generated based upon historical data of user interactions at the site. An experiment is run for a short term at the site based upon a potential change at the site. Based upon data obtained during the experiment, scores are generated for a control group (no change) and a treatment group (potential change) and compared. If there are statistically significant differences between the control group and the treatment group scores, the long term forecast may be used to forecast what the long term impact of the experiment would be based upon the short term experiment.

Computationally and network bandwidth-efficient technique to determine network-accessible content changes based on computed models

Described are techniques for determining long-term effects of an experimental change to a user experience after the end of the experiment. A control state and a treatment state of a statistical hypothesis experiment may be assigned to first and second client devices, respectively, during an experiment time period. Subsequent to the end of the experiment, presentation of the control state may be discontinued. Result data corresponding to the treatment state may be determined during the experiment time period and for a length of time subsequent to the experiment time period. Result data corresponding to the control state may be determined during the experiment time period, and for a length of time prior to assignment of the control state to the first client device.

Systems for determining long-term effects in statistical hypothesis testing

In some implementations, a testing service receives a test execution request for executing test operations on a test target. The testing service may map the test execution request to a particular type of supported test framework from among a plurality of types of supported test frameworks. The testing service may obtain a test package provided by a user that requested the testing, such as from a cloud storage location. The testing service determines a computing capacity for executing the testing and appropriates a plurality of workers in a cloud computing service. The testing service configures the plurality of workers for executing the test operations based on at least one of the test framework, the test execution request or the test package. The testing service provides test execution chunks from the test package to the plurality of workers for executing the testing on the test target.

Cloud-based test execution

A machine generated service cache that utilizes one or more machine learning classifiers is trained using service requests directed to a human-generated service and service responses generated by the human-generated service in response to the service requests. Once the machine generated service cache has been trained to a predetermined level of performance, the machine generated service cache can be utilized to process actual service requests directed to the human-generated service. The machine generated service cache might be utilized to process service requests for which it is not essential that the returned service response be identical to a response that would be generated by the human-generated service.

Machine generated service cache

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Seattle, WA, United States of America

Michael Luis Collado