Abstract: Data characterizing a prompt and/or a parameter set can be received. Data characterizing an enhanced prompt can be generated. The enhanced prompt can be stored in a template. When a change in ta type of the artificial intelligence based model, a setting of the artificial intelligence based model, or a configuration for an enterprise in which the artificial intelligence based model is deployed, is determined to be above a threshold, the enhanced prompt can be modified. The modified enhanced prompt can be provided to one or more applications interfacing with the artificial intelligence model, or derivative of the template. Related apparatus, systems, techniques, and articles are also described.

Abstract: 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.

Abstract: A method includes receiving data characterizing a first output of one or more of a first set of models associated with a first organization, the first set of models trained on a first dataset using a first set of resourcing levels; training one or more of a second set of models associated with a second organization based on a second dataset using a second set of resourcing levels, global constraints and the first output, wherein the second set of resourcing levels specifying a second condition on outputs of the one or more of the second set of models; assessing, based on a second output of the one or more of the second set of models, performance of the one or more of second set of models; and retraining the first set of models or a subset thereof. Related apparatus, systems, articles, and techniques are also described.

Abstract: Data characterizing a prompt can be received. Data characterizing an enhanced prompt can be generated. A prompt response can be determined by providing the data characterizing the enhanced prompt to an artificial intelligence based model. The prompt response can be provided to a user. Related apparatus, systems, techniques, and articles are also described.

Abstract: 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.

Abstract: Data characterizing a query can be received. A dataset specific to an enterprise and a parameter set specific to the enterprise can be determined using an information model. A query response using a foundational model, the dataset specific to the enterprise, and the parameter set specific to the enterprise can be determined. The query response can be provided to a user. Related apparatus, systems, techniques, and articles are also described.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A first set of nodes indicative of a multi-step analytical process including a plurality of analytical tasks is provided in a first graphical user interface (GUI). A first node of the first set of nodes is indicative of a first analytical task of the multi-step analytical process. A spatial arrangement of the first set of nodes in the GUI is indicative of a temporal order associated with the plurality of analytical tasks in the multi-step analytical process. Data characterizing a first input from a first user indicative of a request for assistance with the first analytical task is received. A second user with a second GUI including the spatial arrangement of the first set of nodes is provided. Data characterizing a second input indicative of interaction of a second user with the first analytical task via the first node is received. Related apparatus, systems, techniques and articles are also described.

Abstract: 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.

Abstract: Systems, methods, and computer program products for detecting, locating, and identifying gunshots. In response to detecting a discharge of a firearm, a discharge alert indicating one or more characteristics of the discharge is transmitted to a gunshot location system. The one or more characteristics of the discharge are compared to one or more characteristics of one or more gunshots detected by the gunshot location system based on acoustic signals received at one or more listening stations. In response to finding a match between the one or more characteristics of the discharge and the one or more characteristics of acoustically detected gunshot, the gunshot is associated with the firearm.

Abstract: Data characterizing a set of models trained on a dataset using a set of resourcing levels can be received. The set of resourcing levels can specify a condition on outputs of models in the set of models. Performance of the set of models can be assessed using the set of resourcing levels. A feasible performance region can be determined using the assessment. The feasible performance region can associate each constraint in the set of resourcing levels with a model in the set of models. The feasible performance region can be displayed. Related apparatus, systems, articles, and techniques are also described.

Abstract: 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.