US 12,265,895 B2
Artificial intelligence model and data collection/development platform
Matthew Zeiler, Fort Lee, NJ (US); Daniel Kantor, New York, NY (US); Christopher Fox, New York, NY (US); and Cassidy Williams, New York, NY (US)
Assigned to CLARIFAI, INC., Wilmington, DE (US)
Filed by CLARIFAI, INC., Wilmington, DE (US)
Filed on Jul. 8, 2021, as Appl. No. 17/370,043.
Application 17/370,043 is a continuation of application No. 15/715,433, filed on Sep. 26, 2017, granted, now 11,080,616.
Claims priority of provisional application 62/400,543, filed on Sep. 27, 2016.
Prior Publication US 2021/0342745 A1, Nov. 4, 2021
Int. Cl. G06N 20/00 (2019.01); G06F 3/048 (2013.01); G06F 8/00 (2018.01); G06F 8/30 (2018.01); G06F 8/65 (2018.01); G06N 3/045 (2023.01); G06N 5/04 (2023.01)
CPC G06N 20/00 (2019.01) [G06F 3/048 (2013.01); G06F 8/00 (2013.01); G06F 8/31 (2013.01); G06F 8/65 (2013.01); G06N 3/045 (2023.01); G06N 5/04 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A computing system comprising:
a storage configured to store a collection of training data that is previously consumed by machine learning models during training of the machine learning models via a host platform; and
a processor configured to;
receive a request to train a first machine learning model, the request comprising one or more parameters of input data to be used to train the first machine learning model,
select a training data set previously consumed by a second machine learning model based on input items within the training data set matching the one or more parameters of the input data in the received request,
train the first machine learning model via execution of the first machine learning model on the selected training data set via the host platform;
automatically test, in response to the training of the first machine learning model on the selected training data set, the trained first machine learning model to determine a predictive accuracy of the trained first machine learning model;
determine, based on the determined predictive accuracy of the trained first machine learning model and a predictive accuracy of the second machine learning model, that the trained first machine learning model is more accurate than the second machine learning model;
merge, automatically in response to the determination that the trained first machine learning model is more accurate than the second machine learning model, components from the first machine learning model with the second machine learning model to obtain an updated instance of the second machine learning model;
replace, in a memory storing the second machine learning model, the second machine learning model with the updated instance of the second machine learning model, the updated instance of the second machine learning model including a distinct version indicator associated therewith;
present a user-selectable model representation of the updated instance of the second machine learning model via a user interface;
receive, via the user interface, an indication of a selection of the updated instance of the second machine learning model; and
generate, in response to the reception of the selection of the updated instance of the second machine learning model, at least a portion of a software application.