Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

Abstract: Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing real-time guidance for content collection. One of the methods includes receiving user input from a user through a user interface presentation, determining, from the received user input using a first model, one or more provided data elements occurring in the user input, determining, from the one or more provided data elements occurring in the user input using a second model, one or more intended tasks, determining, for each intended task of the one or more intended tasks using a third model, one or more suggested data elements, ranking the one or more suggested data elements, and updating the user interface presentation with a user interface element suggesting that the user provide the one or more needed data elements.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing real-time guidance for content collection. One of the methods includes receiving user input from a user through a user interface presentation, determining, from the received user input using a first model, one or more provided data elements occurring in the user input, determining, from the one or more provided data elements occurring in the user input using a second model, one or more intended tasks, determining, for each intended task of the one or more intended tasks using a third model, one or more suggested data elements, ranking the one or more suggested data elements, and updating the user interface presentation with a user interface element suggesting that the user provide the one or more needed data elements.