Aible Inc.

Arijit Sengupta

Jonathan W Wray

Geoffrey Grant

Daniel S Kane

Grigory Nudelman

Richard Cooke

Joseph Veltkamp

Robert Shaw

John Delaney

A method includes receiving, via a graphical user interface, data characterizing user input specifying a cost of error of a prediction, a model type requirement, and a computational resource allocation for model development; determining, based on the received data and using a model, a prediction of model performance; and rendering, within the graphical user interface, a plot including a first axis, a second axis, and a region characterizing the determined prediction of model performance, the first axis including a characterization of a first performance metric and the second axis including a characterization of a second performance metric. Related apparatus, systems, techniques and articles are also described.

Interface for visualizing and improving model performance

Data is received characterizing an impact function, a first plurality of inputs to the impact function, and a plurality of weights associated with the first plurality of inputs. A first coefficient characterizing an interaction between a first input and a second input of the plurality of inputs is calculated based on a first set of weights of the plurality of weights. The first set of weights are indicative of interaction between the first input and the second input. A graphical object including a plurality of regions is displayed in a graphical user interface (GUI). A first region and a second region of the plurality of regions are indicative of the first input and the second input, respectively. A second graphical object connecting the first region to the second region is displayed in the GUI. Related apparatus, systems, techniques and articles are also described.

User interface for impact analysis

A method includes monitoring performance of a generated model while the generated model is being used for classification on live data, the monitoring including determining a first performance value of the generated model at a first point in time and determining a second performance value of the generated model at a second point in time; rendering, within a graphical user interface, a plot including a first axis and a second axis, the first axis including a characterization of a first performance metric and the second axis including a characterization of a second performance metric; and rendering, within the graphical user interface and the plot, a first graphical object at a first location characterizing the first performance value and a second graphical object at a second location characterizing the second performance value. Related apparatus, systems, techniques and articles are also described.

Data characterizing inputs to a prediction process that classifies events, an output of the prediction process, and feedback data characterizing a performance of the outcome is monitored. A resource capacity affecting the outcome of the prediction process, and/or a cost-benefit affecting the outcome of the prediction process is determined from the monitoring. The determined resource capacity and/or the determined cost-benefit is provided. Related apparatus, systems, techniques and articles are also described.

Automatically learning process characteristics for model optimization

A first node having a first location in the first GUI and indicative of a first analytical task is provided in a first graphical user interface (GUI). A multi-step analytical process includes the first analytical task. Data is received characterizing a first user input indicative of selection of the first node. A first set of nodes is displayed in the first GUI. The first set of nodes are adjacent to the first node and are associated with a next step of the multi-step analytical process. The multi-step analytical process includes at least one of importing a dataset, building a model using the dataset, and/or deploying the model to operate on live data. Related apparatus, systems, techniques and articles are also described.

User interface for collaborative analytics platform

A method includes receiving, via a model building platform, historical user behavior including historical data analysis characteristics; generating, based on the historical data analysis characteristics, a blueprint for guiding user action to accomplish a task, the generating including constructing the blueprint using the historical data analysis characteristics; receiving, via graphical user interface, user input requesting generation of a model and a task description; determining, using the blueprint and based on the task description, data analysis characteristics; and rendering, within the graphical user interface, a prompt to select the determined data analysis characteristics. Related apparatus, systems, techniques and articles are also described.

Interface for generating models with customizable interface configurations

Performance of a first generated model can be monitored while the first generated model is deployed for use on live data. The monitoring can include determining a first performance value of the first generated model. Performance of a second generated model can be monitored while the second generated model is deployed for use on live data. The monitoring can include determining a second performance value of the second generated model. A plot including a first axis and a second axis can be rendered. The first axis can include a characterization of a first performance metric and the second axis can include a characterization of a second performance metric. A first graphical object at a first location characterizing the first performance value and a second graphical object at a second location characterizing the second performance value can be rendered. Related apparatus, systems, techniques and articles are also described.

A method includes receiving data characterizing a target accuracy and a performance metric of a model; rendering, within a graphical user interface display space, a plot including a first axis and a second axis, the first axis including a characterization of false positive and the second axis including a characterization of false negative; and rendering, within the graphical user interface display space and within the plot, a graphical object at a location characterizing the performance metric and a visualization indicative of the target accuracy. Related apparatus, systems, techniques and articles are also described.

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San Francisco, CA, United States of America

Arijit Sengupta