	US 12,265,891 B2
	Methods and apparatus for automatic attribute extraction for training machine learning models
Sakib Abdul Mondal, Bangalore (IN); Tuhin Bhattacharya, Kolkata (IN); and Abhijit Mondal, Bangalore (IN)
Assigned to Walmart Apollo, LLC, Bentonville, AR (US)
Filed by Walmart Apollo LLC, Bentonville, AR (US)
Filed on Dec. 9, 2020, as Appl. No. 17/116,698.
Prior Publication US 2022/0180246 A1, Jun. 9, 2022
Int. Cl. G06N 20/00 (2019.01); G06N 5/04 (2023.01)

CPC G06N 20/00 (2019.01) [G06N 5/04 (2013.01)]

18 Claims

14. A method comprising:

obtaining training data for training a machine learning model comprising a plurality of hyperparameters and a plurality of coefficients;

generating a first training dataset based on the obtained training data;

training the machine learning model by applying the machine learning model to the first training dataset to generate first output data;

applying a loss algorithm to the first training dataset and the first output data to generate a first loss value;

determining a first updated value for each of the plurality of coefficients based on the first loss value and a corresponding previous value of each of the plurality of coefficients;

in accordance with a determination that the first updated value for each of the plurality of coefficients is outside of a threshold of a last value:

generating a second training dataset based on the obtained training data, wherein the second training dataset is different from the first training dataset;

retraining the machine learning model by applying the machine learning model with the first updated values of the plurality of coefficients to the second training dataset to generate second output data;

applying the loss algorithm to the second training dataset and the second output data to generate a second loss value;

determining a second updated value for each of the plurality of coefficients based on the second loss value and the first updated value of each of the plurality of coefficients;

generating a comparison value based on the first updated value for each of the plurality of coefficients of the machine learning model and the second updated value for each of the plurality of coefficients of the machine learning model;

determining that the comparison value is less than or equal a predetermined threshold; and

storing, in a database associated with a computing device, the first updated value and the second updated value for each of the plurality of coefficients in a data repository; and

applying the first updated value and the second update value for each of the plurality of coefficient in a data repository on data received from a source external to the computing device to generate predicted outputs.