Abstract: A computer system receives a user input specifying a first data field of a data source and a modeling objective for the first data field. The computer system automatically executing a machine learning (ML) model training workflow to train a model to predict a first outcome for the first data field based on the modeling objective. While automatically executing the ML model training workflow to train the model, the computer system determines a plurality of artifacts across a plurality of steps of the ML model training workflow. The computer system generates and causes display of an artifact dependency view that shows dependency relationships between the plurality of artifacts, including a plurality of visual representations corresponding to the artifacts and a plurality of connectors, a respective connector connecting two visual representations of the plurality of visual representations, corresponding to two artifacts that have a dependency relationship with each other.

Abstract: A method generates map visualizations with multiple map layers. A user selects a data source with geographic data. A device displays a data visualization user interface, including a schema information region with data fields, and shelf regions that defining characteristics for a data visualization. The user selects a first geographic data, and the user interface generates a map data visualization using coordinates associated with the first geographic data field. The visualization includes a first plurality of data marks in a first layer. The user selects a second geographic data field. In response, the user interface displays a new layer icon. Upon activation of the new layer icon by the second geographic data field, the user interface superimposes a second layer over the existing map data visualization to form an updated map data visualization. The second layer includes a second plurality of data marks corresponding to the second geographic data field.