Patent ID: 11900231
Assignee: PAYPAL, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A method, comprising:
receiving, by a service provider system comprising one or more hardware processors, a set of training data;
training a set of randomly selected machine learning models based on a set of training data and randomly selecting a set of machine learning algorithms for a set of machine learning platforms;
calculating a set of model performance metrics for the set of randomly selected machine learning models;
iteratively selecting a plurality of machine learning models to train on the set of training data, each iteration of the iteratively selecting comprising:
selecting a first machine learning platform from the set of machine learning platforms based on a first optimization function that past machine learning platforms used for previous training iterations on the set of training data, wherein the first optimization function selects the first machine learning platform based on a greatest optimization metric of the set of model performance metrics from the previous training iterations on the set of training data;
selecting a first algorithm from a set of algorithms supported by the first machine learning platform based on a second optimization function that past algorithms used for the previous training iterations on the set of training data, wherein the first algorithm is separate from the randomly selected set of machine learning algorithms, and wherein the second optimization function uses past performances of the past algorithms from the previous training iterations on the set of training data for the selecting;
determining one or more hyperparameters from a set of hyperparameters supported by the first algorithm based on a third optimization function that past combinations of hyperparameters from the set of hyperparameters used for the previous training iterations on the set of training data, wherein the third optimization function uses previous uses of the set of hyperparameters during the previous training iterations on the set of training data and a hyperparameter value threshold for the determining;

training, after training the set of randomly selected machine learning models, a new machine learning model based on the set of training data using the first machine learning platform, the first algorithm, and the one or more hyperparameters;
calculating a new model performance metric of the new machine learning model;
comparing the calculated new model performance metric of the new machine learning model to a performance threshold metric for the calculated set of model performance metrics for the set of randomly selected machine learning models; and
determining whether to output the new machine learning model as a final machine learning model for a deployment in a production computing environment based on the comparing.