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:
Nov. 02, 2021

Filed:

Jan. 14, 2020
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Alexandra Johnson, San Francisco, CA (US);

Patrick Hayes, San Francisco, CA (US);

Scott Clark, San Francisco, CA (US);

Assignee:

Intel Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/54 (2006.01); G06F 11/34 (2006.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 9/54 (2013.01); G06F 11/3495 (2013.01); G06N 20/00 (2019.01);
Abstract

Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the proposed hyperparameter values.


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