The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 24, 2023

Filed:

Jan. 17, 2020
Applicant:

Visa International Service Association, San Francisco, CA (US);

Inventors:

Yinhe Cheng, Austin, TX (US);

Yu Gu, Austin, TX (US);

Igor Karpenko, Dublin, CA (US);

Peter Walker, Cedar Park, TX (US);

Ranglin Lu, Austin, TX (US);

Subir Roy, Austin, TX (US);

Assignee:

Visa International Service Association, San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/46 (2006.01); G06N 5/04 (2006.01); G06F 9/50 (2006.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G06F 9/5011 (2013.01); G06N 20/00 (2019.01);
Abstract

A method, system, and computer program product for dynamically scheduling machine learning inference jobs receive or determine a plurality of performance profiles associated with a plurality of system resources, wherein each performance profile is associated with a machine learning model; receive a request for system resources for an inference job associated with the machine learning model; determine a system resource of the plurality of system resources for processing the inference job associated with the machine learning model based on the plurality of performance profiles and a quality of service requirement associated with the inference job; assign the system resource to the inference job for processing the inference job; receive result data associated with processing of the inference job with the system resource; and update based on the result data, a performance profile of the plurality of the performance profiles associated with the system resource and the machine learning model.


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