US 12,423,613 B2
Environment-specific training of machine learning models
Pei Yu, Bellevue, WA (US); Zicheng Liu, Bellevue, WA (US); Ying Jin, Kirkland, WA (US); Yinpeng Chen, Sammamish, WA (US); and Kun Luo, Redmond, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Apr. 21, 2021, as Appl. No. 17/236,794.
Prior Publication US 2022/0343205 A1, Oct. 27, 2022
Int. Cl. G06N 3/096 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06N 20/00 (2019.01) [G06N 3/096 (2023.01); G06N 5/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for environment-specific training of a machine learning model, comprising:
for two or more local environments:
receiving, for a respective local environment of the two or more local environments, a data stream including a plurality of sequential data snippets;
using a student version of a machine learning model specific to the respective local environment, generating programmed labels for each data snippet of the plurality of sequential data snippets;
based at least on the generated programmed labels, selecting a portion of the plurality of sequential data snippets for evaluation by a teacher version of the machine learning model;
uploading the selected portion of the data snippets and associated programmed labels to a server-side computing device that includes the teacher version of the machine learning model;
receiving a respective environment-specific training update from the server- side computing device, the respective environment-specific training update based at least on a comparison of the programmed labels associated with the selected portion of the data snippets and pseudolabels generated by the teacher version of the machine learning model for the selected portion of the data snippets;
applying the respective environment-specific training update to the student version of the machine learning model specific to the respective local environment to generate an updated student version of the machine learning model specific to the respective local environment;
using the updated student version of the machine learning model specific to the respective local environment, generating programmed labels for newly received data snippets; and
providing at least some of the newly received data snippets and generated programmed labels to a respective local output for use in evaluating events within the respective local environment.