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:
Aug. 30, 2022

Filed:

May. 20, 2019
Applicant:

Emc Ip Holding Company Llc, Hopkinton, MA (US);

Inventors:

Jinpeng Liu, Shanghai, CN;

Pengfei Wu, Shanghai, CN;

Junping Zhao, Beijing, CN;

Kun Wang, Beijing, CN;

Assignee:

EMC IP Holding Company LLC, Hopkinton, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/10 (2019.01); G06F 8/41 (2018.01); G06F 8/60 (2018.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06N 20/10 (2019.01); G06F 8/447 (2013.01); G06F 8/60 (2013.01); G06N 20/00 (2019.01); G06F 8/41 (2013.01);
Abstract

Embodiments of the present disclosure relate to a method, device and computer program product for deploying a machine learning model. The method comprises: receiving an intermediate representation indicating processing of a machine learning model, learning parameters of the machine learning model, and a computing resource requirement for executing the machine learning model, the intermediate representation, the learning parameters, and the computing resource requirement being determined based on an original code of the machine learning model, the intermediate representation being irrelevant to a programming language of the original code; determining, at least based on the computing resource requirement, a computing node and a parameter storage node for executing the machine learning model; storing the learning parameters in the parameter storage node; and sending the intermediate representation to the computing node for executing the machine learning model with the stored learning parameters.


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