Abstract: At an artificial intelligence system, one or more classifier training iterations are performed until a training completion criterion is met. A particular iteration comprises obtaining, via an interactive interface, asynchronously with respect to the start of the iteration, class labels for data items identified in a previous iteration as candidates for labeling feedback. The particular iteration also comprises identifying, based on an analysis of classification predictions generated using classifiers trained using class labels obtained via the interface, another set of data items as candidates for labeling feedback. After the training criterion is met, a classifier trained using labels obtained during the iterations is stored.

Abstract: At an artificial intelligence system, respective status indicators for classifier training iterations are determined. A visualization data set comprising the status indicators is presented via an interactive programmatic interface. A training enhancement action, based at least partly on an objective associated with a status indicator, is initiated. The action includes selecting data items for which labeling feedback is to be obtained programmatically during one or more classifier training iterations. A classification model that is trained using the labeling feedback obtained as a result of the action is stored.