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MODEL EXECUTION WORKFLOW ENGINE

发明专利审中
20权利要求 · 3 独立
§ Ⅰ

卷宗概要

申请人

FMR LLC

发明人

Raghuram Vemuri; Bhargav Tumu; Arun Aithal Subbanna; Jatinder Kumar; Ramanathan Natarajan; Avishek Pradhan; Sunil Gurusiddappa; Ravi Krishnamurthy

IPC 分类

G6F 8/20G6F 8/33

CPC 分类

G6F8/20G6F8/33

A method for executing a machine learning model using a workflow engine includes receiving a model configuration including data related to the machine learning model, pre-processing steps having first prerequisites, and post-processing steps having second prerequisites; in response to a determination that the first prerequisites are not met, executing first operations; in response to a determination that the second prerequisites are not met, executing second operations; executing the pre-processing steps to provide first data, the first data including model inputs; causing transmission of the first data from the computer system to the cloud server system; causing execution of the machine learning model on the cloud server system; causing transmission of second data from the cloud server system to the computer system, the second data including an output of the machine learning model; executing the post-processing steps.

原文(中文)

A method for executing a machine learning model using a workflow engine includes receiving a model configuration including data related to the machine learning model, pre-processing steps having first prerequisites, and post-processing steps having second prerequisites; in response to a determination that the first prerequisites are not met, executing first operations; in response to a determination that the second prerequisites are not met, executing second operations; executing the pre-processing steps to provide first data, the first data including model inputs; causing transmission of the first data from the computer system to the cloud server system; causing execution of the machine learning model on the cloud server system; causing transmission of second data from the cloud server system to the computer system, the second data including an output of the machine learning model; executing the post-processing steps.