US 11,954,565 B2
Automated machine learning system
Killian B. Dent, Sandy, UT (US); James M. Friedman, Ormond Beach, FL (US); Allan D. Johnson, Lehi, UT (US); Shauna J. Moran, Taylorsville, UT (US); Tyler P. Cooper, Bluffdale, UT (US); Chris K. Knoch, Sandy, UT (US); Nicholas R. Magnuson, Park City, UT (US); and Daniel J. Wallace, Salt Lake City, UT (US)
Assigned to QLIKTECH INTERNATIONAL AB, Lund (SE)
Filed by QLIKTECH INTERNATIONAL AB, Lund (SE)
Filed on May 20, 2019, as Appl. No. 16/416,773.
Claims priority of provisional application 62/694,908, filed on Jul. 6, 2018.
Prior Publication US 2020/0012962 A1, Jan. 9, 2020
Int. Cl. G06N 20/00 (2019.01); G06F 9/50 (2006.01); G06N 3/08 (2023.01); G06N 20/20 (2019.01)
CPC G06N 20/00 (2019.01) [G06F 9/5011 (2013.01); G06N 3/08 (2013.01); G06N 20/20 (2019.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus comprising:
at least one processor; and
memory storing processor-executable instructions that, when executed by the at least one processor, cause the apparatus to:
receive, via a graphical user interface (GUI) output at a client device, a selection of a target metric to predict, wherein the GUI comprises at least one input control that facilitates adjusting an amount of influence for a prediction driver associated with the target metric, wherein the client device is in communication with a user data store and a service provider environment;
determine, based on the selection of the target metric, one or more datasets of a plurality of datasets comprising data records storing values based on the prediction driver, wherein the plurality of data sets are stored at the user data store;
cause, based on the one or more datasets and the prediction driver, a first version of a machine learning model to be generated and a first plurality of predictions for the target metric to be generated by the first version of the machine learning model;
receive, via the at least one input control, an adjustment to the amount of influence for the prediction driver, wherein the adjustment causes at least one value stored in the data records within the one or more datasets to be modified with a substitute value, wherein the substitute value is based on the at least one value and the adjustment;
cause, based on the modified at least one value within the one or more datasets, at least one further version of the machine learning model to be generated and at least one further plurality of predictions for the target metric to be generated by the at least one further version of the machine learning model;
output, via the GUI, a first prediction score for the first version of the machine learning model and at least one further prediction score for the at least one further version of the machine learning model, wherein the first prediction score is based on the first plurality of predictions for the target metric, and wherein the at least one further prediction score is based on the at least one further plurality of predictions for the target metric;
receive, via the GUI, a selection of a version of the machine learning model for deployment, wherein the selection is made following the output of the first prediction score and the at least one further prediction score; and
based on the selection, cause:
the version of the machine learning model selected for deployment to be deployed to computing resources in the service provider environment, and
data associated with the at least one further plurality of predictions and the modified at least one value within the one or more datasets to be stored in the user data store.