Abstract: An approach for generating an artificial intelligence system configurable for use with assets. In this approach, a model recipe is selected for generating the artificial intelligence system for use with assets. Recipe parameters specified in the model recipe are identified. A training dataset is created using the model recipe and input data. A set of artificial intelligence models is trained using the training dataset, the recipe parameters, and the model recipe. The training creates artifact models. The artifact models resulting from training are evaluated. The evaluation is used to select a set of the artifact models in the artifacts that form the artificial intelligence system that is configurable for use in assets.

Abstract: A computer-implemented method for developing a hierarchical machine-learning pipeline can include receiving a hierarchy specification, a set of estimators for the root node, and one or more transformer options for each of the transformer nodes. The hierarchy specification provides a configuration of the root node, transformer nodes, and edges interconnecting the root and transformer nodes. A rank can be obtained for each estimator in the root node. A hierarchy pipeline traverser can then traverse a first child layer of the transformer nodes connected to the root node via one of the edges. A first ranked list of pathways can be determined with respect to the one or more transformer options selected for the first child layer and at least one selected estimator of the root node.

Abstract: Systems and methods for optimizing and training machine learning (ML) models are provided. In embodiments, a computer implemented method includes: performing, by a processor set, a group execution of ML pipelines using a first subset of a training data set as input data for the ML pipelines, thereby generating a trained ML model for each of the ML pipelines, wherein data transformations that are common between the ML pipelines are implemented only once to generate an output, and the output is shared between the ML pipelines during the group execution of the ML pipelines; generating, by the processor set, performance metrics for each of the trained ML models based on validation data; ranking, by the processor set, the trained ML models based on the performance metrics, thereby generating a list of ranked ML models; and outputting, by the processor set, the list of ranked ML models to a user.

Abstract: An apparatus includes circuitry configured to maintain a record of a plurality of owners and at least one test operation owned by an owner; prompt automatically the owner in response to a failure of the one test operation; maintain a log of actions taken on the one test operation, and provide availability to the log of actions; update an estimated time to completion, and notify a management entity of the updated estimated time to completion; mark and prioritize an order related to the one test operation, in response to the estimated time to completion being within a threshold of a delivery date; rank the marked order with other marked orders by a risk of not being able to meet the delivery date; and notify the owner of the ranking with an urgent message, in response to the marked order failing to meet the delivery date.

Abstract: An apparatus includes circuitry configured to maintain a record of a plurality of owners and at least one test operation owned by an owner; prompt automatically the owner in response to a failure of the one test operation; maintain a log of actions taken on the one test operation, and provide availability to the log of actions; update an estimated time to completion, and notify a management entity of the updated estimated time to completion; mark and prioritize an order related to the one test operation, in response to the estimated time to completion being within a threshold of a delivery date; rank the marked order with other marked orders by a risk of not being able to meet the delivery date; and notify the owner of the ranking with an urgent message, in response to the marked order failing to meet the delivery date.

Abstract: Temporal and spatially integrated forecast modeling includes generating a plurality of forecast models for a plurality of short-term to long-term time periods for a plurality of locations. Temporally integrating the plurality of forecast models sequentially over the plurality of time periods for the plurality of locations and spatially integrating the temporally integrated plurality of forecast models for each location hierarchically over the geographic areas. The forecast models are autoregressive distributed lag models with different explanatory variables for the short-term and long-term forecast models. The temporally integrating includes recursively integrating the plurality of forecast models over the time periods from the short-term to the long-term time periods and the spatially integrating includes recursively integrating the temporally integrated plurality of forecast models hierarchically from larger size geographic areas to smaller size geographic areas.

Abstract: A concept associated with a feature used in machine learning model can be determined, the feature extracted from a first data source. A second data source containing the concept can be identified. An additional feature can be generated by performing a natural language processing on the second data source. The feature and the additional feature can be merged. A second machine learning model can be generated, which use the merged feature. A prediction result of the first machine learning model can be compared with a prediction result of the second machine learning model relative to ground truth data, to evaluate effective of the merged feature. Based on the evaluated effectiveness, the feature can be augmented with the merged feature in machine learning.

Abstract: A method is provided for creating a semantic model for submitting search queries thereto. The method includes an act of receiving data from one or more input sources in an entity and relationship capture service of a situational awareness engine. The method further includes an act of extracting entities and relationships between the entities in two or more extraction services, where the two or more extraction services include at least two of a table-to-graph service, an event-to-graph service, a sensor-to-graph service, a text-to-graph service, and an image-to-graph service. The method includes an act of generating a semantic model based on fusion and labeling the extracted data provided by the at least two extraction services, where the semantic model can receive a search query and respond to the search query based on the generated semantic model.