Invention Grant
- Patent Title: Machine learning pipeline optimization
-
Application No.: US16891834Application Date: 2020-06-03
-
Publication No.: US11615271B2Publication Date: 2023-03-28
- Inventor: Eyal Ben Zion , Alain Charles Briancon , Pranav Mahesh Makhijani , Thejas Narayana Prasad , Sara Amini , Jian Deng , Ngoc Thu Nguyen , Jean Joseph Belanger
- Applicant: Cerebri AI Inc.
- Applicant Address: US TX Austin
- Assignee: Cerebri AI Inc.
- Current Assignee: Cerebri AI Inc.
- Current Assignee Address: US TX Austin
- Agency: Pillsbury Winthrop Shaw Pittman, LLP
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06F8/20 ; G06N20/00 ; G06N20/20 ; G06F30/20 ; G06F8/10 ; G06F8/30 ; G06Q40/08 ; G06F8/36 ; G06F16/25 ; G06F9/445 ; G06N5/04 ; G06Q10/0631 ; G06Q10/0637 ; G06Q10/0639 ; G06Q10/067 ; G06Q30/012 ; G06Q30/016 ; G06Q30/0204 ; G06Q40/02 ; G06Q30/0202

Abstract:
Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.
Public/Granted literature
- US20200380416A1 MACHINE LEARNING PIPELINE OPTIMIZATION Public/Granted day:2020-12-03
Information query