Abstract: Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias.

Abstract: Aspects of the subject disclosure may include, for example, detecting data streams by a processing system including a processor, wherein the processing system is associated with an anomaly detection and alerting system in which stream processing and model maintenance is decoupled from one another, and wherein one or more dedicated virtual machines (VMs) store and maintain anomaly detection and alerting models, based on the detecting, causing, by the processing system, a plurality of stream-processing VMs to be instantiated for processing the data streams, and managing, by the processing system, data stream assignments for the plurality of stream-processing VMs based on monitoring of one or more conditions, wherein the plurality of stream-processing VMs process assigned data streams by executing instances of the anomaly detection and alerting models, and provide model outputs to the one or more dedicated VMs for updating of the anomaly detection and alerting models. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving, via an interactive user interface, user selections of a dataset and a first dimension of a plurality of dimensions in a hierarchical structure, wherein the dataset corresponds to a specific domain and comprises anomalies or alerts that are aggregated in certain dimensions, and wherein a lattice of aggregations of the anomalies or alerts is defined for the specific domain, causing the interactive user interface to present a time series chart and a heatmap, wherein the time series chart portrays alert density across dimensions below the first dimension, wherein the heatmap displays, according to a visual scheme, concentrations of the anomalies or alerts across at least a portion of the dimensions below the first dimension, and wherein user interaction with the time series chart and/or the heatmap facilitates navigation of subspaces of the lattice and exploration of the concentrations of the anomalies or alerts.

Abstract: Aspects of the subject disclosure may include, for example, a device including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, define operations of modules that monitor a computational hierarchy, the modules include a first module for generating base alerts with a hierarchical key associated with an operation of the computational hierarchy and for rolling up the base alerts based on the computational hierarchy, resulting in rolled-up base alerts; a second module for generating super alerts from a concentration of the rolled up base alerts; and a third module comprising a first plurality of models that selects the super alerts based on priority, persistence of anomalies, pervasiveness of the super alerts generated, recency, or a combination thereof, generates a smart alert based on voting on the super alerts selected by the first plurality of models, and presents the smart alert on a user interface.

Abstract: Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations including monitoring, by a plurality of models implemented by the executable instructions, a plurality of data streams comprising super alerts; and generating a smart alert based on voting on the super alerts by the plurality of models. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning(ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias.

Abstract: Aspects of the subject disclosure may include, for example, training a decision scoring model for an e-sport based on historical data for the e-sport. In various embodiments, the decision scoring model may be trained based on the historical data and on metadata associated with the e-sport. Some embodiments can include identifying a plurality of candidate in-game decision sequences based on decision parameters for a gameplay decision of an ongoing gaming session and a gaming session history for the ongoing gaming session. Various embodiments can include applying the decision scoring model to rank the plurality of candidate in-game decision sequences. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias.

Abstract: Aspects of the subject disclosure may include, for example, training a decision scoring model for an e-sport based on historical data for the e-sport. In various embodiments, the decision scoring model may be trained based on the historical data and on metadata associated with the e-sport. Some embodiments can include identifying a plurality of candidate in-game decision sequences based on decision parameters for a gameplay decision of an ongoing gaming session and a gaming session history for the ongoing gaming session. Various embodiments can include applying the decision scoring model to rank the plurality of candidate in-game decision sequences. Other embodiments are disclosed.

Abstract: Techniques for knowledge discovery based on indirect inference of association are presented. A data management component (DMC) can determine and extract, in a structured format, entities, relationships between entities, and concepts relating thereto in documents, based on analysis of information in the documents and/or keywords relating to concepts, to generate an association inference model. Using artificial intelligence techniques, DMC can embed the entities and relationships to a common representation to generate and train a scoring model that can be used to evaluate and score similarity strength between entities, including entities that do not have a known relationship, and can predict or infer relationships, including indirect relationships, between entities or between concepts. In that regard, DMC or user can evaluate concept-level scores to determine a level of relationship between concepts. DMC can feedback information from the scoring model or evaluation to update the association inference model.

Abstract: Techniques for determining, modifying, and correcting data elements of documents, tables, and databases are presented. A data management component (DMC) can determine and extract entities of a group of entities, and relationships between entities, in documents, tables, and databases based on analysis of the entities and information relating thereto. DMC can determine a trained model representative of the entities and their relationships based on the relationships. For a subsequently received entity, DMC can predict a relationship between the subsequent entity and an entity of the entity group based on the model. DMC can determine candidate data modifications associated with the subsequent entity based on the relationship between the subsequent entity and the entity. DMC can rank the candidate data modifications based on probabilities that the candidate data modifications are a correct data modification, wherein data modification information relating to the ranking can be presented as an output.

Abstract: Aspects of the subject disclosure may include, for example, a device that includes a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations such as collecting historical data on viewership of media content; gathering metadata of events in the media content; training a scoring model on the metadata and historical data, wherein the scoring model ranks placement choices and provide a score for each placement choice for advertisements within the media content; and evaluating a placement choice for ad completion rate, thereby generating an evaluation of the placement choice. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a device that includes a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations such as collecting historical data on viewership of media content; gathering metadata of events in the media content; training a scoring model on the metadata and historical data, wherein the scoring model ranks placement choices and provide a score for each placement choice for advertisements within the media content; and evaluating a placement choice for ad completion rate, thereby generating an evaluation of the placement choice. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, training a decision scoring model for an e-sport based on historical data for the e-sport. In various embodiments, the decision scoring model may be trained based on the historical data and on metadata associated with the e-sport. Some embodiments can include identifying a plurality of candidate in-game decision sequences based on decision parameters for a gameplay decision of an ongoing gaming session and a gaming session history for the ongoing gaming session. Various embodiments can include applying the decision scoring model to rank the plurality of candidate in-game decision sequences. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias.