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: A system includes: a memory operable to store a predictive model; a first processor communicatively coupled to the memory, the first processor operable to execute the predictive model to perform operations including generating knowledge score metrics based on a set of attributes for individuals included in a specified population, where the knowledge score metrics quantify a prediction of a capability of an individual for performing metadata labeling; a second processor communicatively coupled to the memory and the first processor, the second processor is operable to perform operations including comparing the knowledge score metrics to a specified threshold, and identifying attributes of individuals from a specified population having knowledge score metrics exceeding the specified threshold as attributes of individuals capable of performing metadata labeling.

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: A system includes: a memory operable to store a predictive model; a first processor communicatively coupled to the memory, the first processor operable to execute the predictive model to perform operations including generating knowledge score metrics based on a set of attributes for individuals included in a specified population, where the knowledge score metrics quantify a prediction of a capability of an individual for performing metadata labeling; a second processor communicatively coupled to the memory and the first processor, the second processor is operable to perform operations including comparing the knowledge score metrics to a specified threshold, and identifying attributes of individuals from a specified population having knowledge score metrics exceeding the specified threshold as attributes of individuals capable of performing metadata labeling.

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, 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.

Abstract: A system includes: a memory operable to store a predictive model; a first processor communicatively coupled to the memory, the first processor operable to execute the predictive model to perform operations including generating knowledge score metrics based on a set of attributes for individuals included in a specified population, where the knowledge score metrics quantify a prediction of a capability of an individual for performing metadata labeling; a second processor communicatively coupled to the memory and the first processor, the second processor is operable to perform operations including comparing the knowledge score metrics to a specified threshold, and identifying attributes of individuals from a specified population having knowledge score metrics exceeding the specified threshold as attributes of individuals capable of performing metadata labeling.

Abstract: A self-contained, vehicle-mounted system for operating a retractable cover with respect to proximate open top containers transported by the vehicle to cover and uncover the containers. The system includes all of the items necessary to accomplish the covering and uncovering of a container and needs only to be mounted on the vehicle and connected to a source of electric power on the vehicle.

Abstract: A self-contained, vehicle-mounted system for operating a retractable cover with respect to proximate open top containers transported by the vehicle to cover and uncover the containers. The system includes all of the items necessary to accomplish the covering and uncovering of a container and needs only to be mounted on the vehicle and connected to a source of electric power on the vehicle.

Abstract: A cover assembly for a vehicle carried open top container wherein a cover is connected at one end to a vertically adjustable tower and an opposite end to a cross member connected to a pair of pivot arms that are mounted at their lower ends to be pivoted by at least one hydraulic actuator that is mounted to the vehicle intermediate opposite ends of the container and adjacent the bottom thereof. An effective length of each pivot arm may be adjusted and locked in adjusted position.

Abstract: A covering system for covering an open top container supported on a frame of a truck which includes a tarp device and a lifting device. The lifting device vertically moves the tarp device between a raised position at least ten feet above the frame to position tarp device high above the container and a lowered position below the top edge of the container. When the tarp device is raised, a tarp is extended from the tarp device to cover the container. The lifting device is then lowered to cover the open top of the container.

Abstract: A covering system for covering an open top container supported on a frame of a truck which includes a tarp device and a lifting device. The lifting device vertically moves the tarp device between a raised position at least twelve feet above the frame to position tarp device high above the container and a lowered position below the top edge of the container. When the tarp device is raised, a tarp is extended from the tarp device to cover the container. The lifting device is then lowered to cover the open top of the container.

Abstract: A covering system for covering a transportable container with a tarp. The covering system includes a covering device provided with an extendable tarp. The covering device is mounted on a support structure which is manipulable to position the covering device to enable covering of a container which is unsupported by the vehicle. The covering device is preferably mounted to a hook arm which also functions to load the container onto the vehicle after the container is covered.