Abstract: A machine learning feature studio comprises a user interface configured to allow a user to define features associated with an entity. The features are calculated using historical or real-time data stored in an event store and associated with the entity. Visualizations and values of the calculated feature are displayed in the user interface and the user may interact with the features, such as to edit and compare them. The user commits the features to the project associated with a machine learning model and selects to export the project. Feature vectors may are calculated using the committed features and are exported to a production environment.

Abstract: A user selects a node that specifies an operation for a dataset. In response, a computer system displays data values for the dataset, in a grid in a data pane. The grid includes a first column and a second column. In the grid, the user edits a first data value in the first column in a first row. The user input changes the first data value to a replacement data value. The computer system identifies a second data value in the second column in the first row, and identifies one or more additional rows in the grid, whose data values for the first column and the second column match the first data value and the second data value. The computer system then updates the data value in the first column for a second row in the grid to the replacement data value.

Abstract: A machine learning feature studio comprises a user interface configured to allow a user to define features associated with an entity. The features are calculated using historical or real-time data stored in an event store and associated with the entity. Visualizations and values of the calculated feature are displayed in the user interface and the user may interact with the features, such as to edit and compare them. The user commits the features to the project associated with a machine learning model and selects to export the project. Feature vectors may are calculated using the committed features and are exported to a production environment.

Abstract: Machine learning feature engineering systems and methods comprise an event ingestion module that receives event data associated with entities. The ingestion module determines which entities are associated with events of the event data. The ingestion module stores the events, grouped by associated entity, in a related event store. A user defines features associated with the entities via an API and/or a feature studio. A feature computation layer determines values for the features based on the grouped events stored to the related event store. The feature computation layer stores the computed feature values and timestamps to a feature store. When new data is received, the feature computation layer computes one or more of the feature values for different times based on the timestamps. Feature vectors are generated using the computed feature values and output to the user via the API and/or feature studio.

Abstract: A user selects a node that specifies an operation for a dataset. In response, a computer system displays data values for the dataset, in a grid in a data pane. The grid includes a first column and a second column. In the grid, the user edits a first data value in the first column in a first row. The user input changes the first data value to a replacement data value. The computer system identifies a second data value in the second column in the first row, and identifies one or more additional rows in the grid, whose data values for the first column and the second column match the first data value and the second data value. The computer system then updates the data value in the first column for a second row in the grid to the replacement data value.

Abstract: A method prepares data for subsequent analysis. The method displays a user interface that includes a plurality of panes, including a data flow pane and a data pane. The data flow pane includes a flow diagram having a plurality of nodes, each node specifying a respective primary operation or specifying a plurality of secondary operations to clean a respective data set. The data pane includes a plurality of data values in rows and columns. The data values correspond to a selected node in the data flow pane. A first user input selects a first data value in a first column and receives a second user input to edit a second data value in a second column according to one or more predefined conditions. The method then highlights the second column. When the predefined conditions are met, the method changes the second data value to a replacement data value.

Abstract: A machine learning feature studio comprises a user interface configured to allow a user to define features associated with an entity. The features are calculated using historical or real-time data stored in an event store and associated with the entity. Visualizations and values of the calculated feature are displayed in the user interface and the user may interact with the features, such as to edit and compare them. The user commits the features to the project associated with a machine learning model and selects to export the project. Feature vectors may are calculated using the committed features and are exported to a production environment.

Abstract: A machine learning feature studio comprises a user interface configured to allow a user to define features associated with an entity. The features are calculated using historical or real-time data stored in an event store and associated with the entity. Visualizations and values of the calculated feature are displayed in the user interface and the user may interact with the features, such as to edit and compare them. The user commits the features to the project associated with a machine learning model and selects to export the project. Feature vectors may are calculated using the committed features and are exported to a production environment.

Abstract: Machine learning feature engineering systems and methods comprise an event ingestion module that receives event data associated with entities. The ingestion module determines which entities are associated with events of the event data. The ingestion module stores the events, grouped by associated entity, in a related event store. A user defines features associated with the entities via an API and/or a feature studio. A feature computation layer determines values for the features based on the grouped events stored to the related event store. The feature computation layer stores the computed feature values and timestamps to a feature store. When new data is received, the feature computation layer computes one or more of the feature values for different times based on the timestamps. Feature vectors are generated using the computed feature values and output to the user via the API and/or feature studio.