Invention Grant
- Patent Title: Using machine-learning methods to facilitate experimental evaluation of modifications to a computational environment within a distributed system
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Application No.: US17884829Application Date: 2022-08-10
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Publication No.: US11669374B2Publication Date: 2023-06-06
- Inventor: Alexandra Savelieva , Srinivas Rao Choudam , Isidro Rene Hegouaburu
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Agency: Ray Quinney & Nebeker P.C
- Agent James Bullough
- Main IPC: G06F9/50
- IPC: G06F9/50 ; G06N20/00 ; G06F17/10 ; H04L41/0866 ; G06F18/21

Abstract:
The present disclosure provides an experimentation framework for a computational environment in a distributed system. A machine-learning model may be created that predicts at least one output produced by the computational environment based on at least one input provided to the computational environment. During an evaluation time period that is subsequent to at least one modification being made to the computational environment, at least one modified output produced by the computational environment may be determined. The machine-learning model may be used to calculate at least one predicted output that would have been produced by the computational environment during the evaluation time period if the at least one modification had not been made. A determination may also be made about how the at least one modification affected the computational environment based on a comparison of the at least one modified output and the at least one predicted output.
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Information query