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: 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: The concepts and technologies disclosed herein are directed to peer packet transport. According to one aspect disclosed herein, a scheduler system can receive a content delivery request to transport a data payload of content to a destination location by a specific time. A prediction system can identify a data path that meets connectivity between an origin location and the destination location with delivery of the data payload of content by the specific time. The prediction system can inform the scheduler system of the data path that meets connectivity between the origin location and the destination location with delivery of the data payload of content by the specific time. The scheduler system can coordinate, among a plurality of data courier devices, transport, along the data path, of the data payload of the content from the origin location to the destination location by the specific time.

Abstract: Liquid purification methods and apparatus are disclosed. An example apparatus includes a frame to define an interior space, the frame having an inner surface in contact with air of the interior space; a barrier positioned between a body of liquid and the interior space; and an absorber suspended from the frame to transfer liquid from the body of liquid into the interior space via an opening in the barrier.

Abstract: The concepts and technologies disclosed herein are directed to peer packet transport. According to one aspect disclosed herein, a scheduler system can receive a content delivery request to transport a data payload of content to a destination location by a specific time. A prediction system can identify a data path that meets connectivity between an origin location and the destination location with delivery of the data payload of content by the specific time. The prediction system can inform the scheduler system of the data path that meets connectivity between the origin location and the destination location with delivery of the data payload of content by the specific time. The scheduler system can coordinate, among a plurality of data courier devices, transport, along the data path, of the data payload of the content from the origin location to the destination location by the specific time.

Abstract: Liquid purification methods and apparatus are disclosed. An example apparatus includes a frame to define an interior space, the frame having an inner surface in contact with air of the interior space; a barrier positioned between a body of liquid and the interior space; and an absorber suspended from the frame to transfer liquid from the body of liquid into the interior space via an opening in the barrier.

Abstract: Liquid purification methods and apparatus are disclosed. An example apparatus includes a frame to define an interior space, the frame having an inner surface in contact with air of the interior space; a barrier positioned between a body of liquid and the interior space; and an absorber suspended from the frame to transfer liquid from the body of liquid into the interior space via an opening in the barrier.

Abstract: Liquid purification methods and apparatus are disclosed. An example apparatus includes a frame to define an interior space, the frame having an inner surface in contact with air of the interior space; a barrier positioned between a body of liquid and the interior space; and an absorber suspended from the frame to transfer liquid from the body of liquid into the interior space via an opening in the barrier.