Abstract: An example method includes initializing a configuration file for a machine learning model, wherein the initializing is performed in response to receiving a request from a user, and wherein the configuration file comprises a plurality of sections that is configurable by the user, configuring at least one parameter of a feature engineering rules section of the configuration file, wherein the configuring the at least one parameter of the feature engineering rules section is based on a first value provided by the user, configuring at least one parameter of an algorithm definitions section of the configuration file, wherein the configuring the at least one parameter of the algorithm definitions section is based on a second value provided by the user, and populating the configuration file using the feature engineering rules section as configured and the algorithm definitions section as configured, to generate the machine learning model.

Abstract: An example method includes initializing a configuration file for a machine learning model, wherein the initializing is performed in response to receiving a request from a user, and wherein the configuration file comprises a plurality of sections that is configurable by the user, configuring at least one parameter of a feature engineering rules section of the configuration file, wherein the configuring the at least one parameter of the feature engineering rules section is based on a first value provided by the user, configuring at least one parameter of an algorithm definitions section of the configuration file, wherein the configuring the at least one parameter of the algorithm definitions section is based on a second value provided by the user, and populating the configuration file using the feature engineering rules section as configured and the algorithm definitions section as configured, to generate the machine learning model.

Abstract: Aspects of the subject disclosure may include, for example, a non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations including selecting modeling logic for an artificial intelligence (AI) model that solves a use case of a plurality of use cases; executing the AI model using holdout data to obtain a sub-result; evaluating the sub-result based on an evaluation metric; and combining the sub-result with other sub-results of the plurality of use cases to determine whether an exit criteria has been met. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, training a machine learning model on training data, generating, by the machine learning model, a plurality of prediction data records which each has an associated probability, and promoting prediction data records of the plurality of prediction data records having an associated probability exceeding a threshold. The subject disclosure may further include combining the promoted prediction data records with the training data to form new training data, retraining the machine learning model on the new training data and generating, by the machine learning model, new prediction data records. The subject disclosure may further include identifying a real-time condition based on the new prediction data records, the real-time condition being one that requires prompt attention, and resolving the real-time condition. Other embodiments are disclosed.

Abstract: An example method includes initializing a configuration file for a machine learning model, wherein the initializing is performed in response to receiving a request from a user, and wherein the configuration file comprises a plurality of sections that is configurable by the user, configuring at least one parameter of a feature engineering rules section of the configuration file, wherein the configuring the at least one parameter of the feature engineering rules section is based on a first value provided by the user, configuring at least one parameter of an algorithm definitions section of the configuration file, wherein the configuring the at least one parameter of the algorithm definitions section is based on a second value provided by the user, and populating the configuration file using the feature engineering rules section as configured and the algorithm definitions section as configured, to generate the machine learning model.

Abstract: An example method includes building a set of test data for a machine learning model, in response to receiving a target data set from a user, wherein the target data set is a data set on which the machine learning model is to be trained to operate, identifying a subset of predefined features engineering action scripts from among a plurality of predefined features engineering action scripts, wherein the subset is determined to be applicable to the set of test data, and automatically generating a recommended features engineering action script for operating on the target data set, wherein the automatically generating includes customizing a parameter of a predefined features engineering action script of the subset to extract data values from locations in the target data set, and wherein the recommended features engineering action script is recommended to the user for inclusion in a features engineering component of the machine learning model.