Abstract: In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.

Abstract: In some examples, a server may determine a specification associated with a software module that is to be integrated with a software system. The specification identifies how the software module interacts with the software system. The server may execute a machine learning module to perform an analysis of the specification. The machine learning module may suggest at least one modification to at least a first portion of the specification and may automatically modify at least a second portion of the specification. The server may convert the specification to one or more application programming interface (API) calls and provide a system interface that includes the one or more API calls to enable the software module to interact with the software system. The API calls may include calls to a data integration API, a file transfer API, a messaging API, a database API, or any combination thereof.

Abstract: Methods, apparatus, and processor-readable storage media for identifying and remediating anomalies through cognitively assorted machine learning algorithms are provided herein. A computer-implemented method includes: identifying, using system log data, a target variable based at least in part on correlations between a set of performance indicators of a system and the target variable, and threshold values for the performance indicators relative to the target variable; generating an inference model to predict when the system will enter an adverse state and identify one or more root causes of the system entering the adverse state; using machine reinforcement learning to determine an action policy including actions that remediate the adverse state; predicting that the system will enter the adverse state by applying the inference model to further system log data; and automatically executing one or more actions of the action policy in response to the prediction.

Abstract: Methods, apparatus, and processor-readable storage media for device manufacturing cycle time reduction using machine learning techniques are provided herein. An example computer-implemented method includes obtaining video input related to one or more manufacturing resources in a manufacturing environment; determining availability status information for at least one of the one or more manufacturing resources by applying one or more machine learning models to the obtained video input; and outputting the determined availability status information to at least one user device associated with the manufacturing environment.

Abstract: An information handling system determines context features for an electronic mail message based on a payload of the electronic mail message. A processor adds the context features to the payload of the electronic mail message to create an updated payload. Based on the updated payload, the processor sets a quarantine indication for the electronic mail message to either a first state or a second state. In response to the quarantine indication for the electronic mail message being in the first state, the processor assigns the electronic mail message to an electronic mail storm. In response to the assigning of the electronic mail message being to the electronic mail storm, the processor quarantines the electronic mail message.

Abstract: In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.

Abstract: Methods, apparatus, and processor-readable storage media for device manufacturing cycle time reduction using machine learning techniques are provided herein. An example computer-implemented method includes obtaining video input related to one or more manufacturing resources in a manufacturing environment; determining availability status information for at least one of the one or more manufacturing resources by applying one or more machine learning models to the obtained video input; and outputting the determined availability status information to at least one user device associated with the manufacturing environment.

Abstract: Methods, apparatus, and processor-readable storage media for augmenting end-to-end transaction visibility using artificial intelligence are provided herein. An example computer-implemented method includes obtaining data related to multiple transaction flows across multiple data sources within an enterprise system, and forecasting anomalies in connection with at least one of the transaction flows by applying one or more of a first set of artificial intelligence techniques to portions of the obtained data, wherein applying the artificial intelligence techniques is based on which of the multiple data sources correspond to the portions of the obtained data. Such a method further includes determining automated actions to be performed in connection with the forecasted anomalies by applying one or more of a second set of artificial intelligence techniques to portions of the obtained data related to the forecasted anomalies, and performing the automated actions in connection with the at least one transaction flow.

Abstract: A method is disclosed including: obtaining one or more values of a system metric, the system metric being associated with a hardware resource of a computing device; detecting whether the system metric is approaching a threshold, the threshold being associated with a key performance indicator (KPI) of the computing device, the detecting being performed based on the obtained values of the system metric; calculating a predicted value of the system metric in response to detecting that the system metric is approaching the threshold, the predicted value of the system metric being calculated by using a linear predictor that is trained using unevenly-sampled training data; detecting whether the predicted value of the system metric exceeds the threshold; and reconfiguring the computing device to prevent the system metric from reaching the predicted value in response to detecting that the predicted value exceeds the threshold.

Abstract: An information handling system determines context features for an electronic mail message based on a payload of the electronic mail message. A processor adds the context features to the payload of the electronic mail message to create an updated payload. Based on the updated payload, the processor sets a quarantine indication for the electronic mail message to either a first state or a second state. In response to the quarantine indication for the electronic mail message being in the first state, the processor assigns the electronic mail message to an electronic mail storm. In response to the assigning of the electronic mail message being to the electronic mail storm, the processor quarantines the electronic mail message.

Abstract: In some examples, a server may determine a specification associated with a software module that is to be integrated with a software system. The specification identifies how the software module interacts with the software system. The server may execute a machine learning module to perform an analysis of the specification. The machine learning module may suggest at least one modification to at least a first portion of the specification and may automatically modify at least a second portion of the specification. The server may convert the specification to one or more application programming interface (API) calls and provide a system interface that includes the one or more API calls to enable the software module to interact with the software system. The API calls may include calls to a data integration API, a file transfer API, a messaging API, a database API, or any combination thereof.

Abstract: A method is disclosed including: obtaining one or more values of a system metric, the system metric being associated with a hardware resource of a computing device; detecting whether the system metric is approaching a threshold, the threshold being associated with a key performance indicator (KPI) of the computing device, the detecting being performed based on the obtained values of the system metric; calculating a predicted value of the system metric in response to detecting that the system metric is approaching the threshold, the predicted value of the system metric being calculated by using a linear predictor that is trained using unevenly-sampled training data; detecting whether the predicted value of the system metric exceeds the threshold; and reconfiguring the computing device to prevent the system metric from reaching the predicted value in response to detecting that the predicted value exceeds the threshold.

Abstract: Methods, apparatus, and processor-readable storage media for identifying and remediating anomalies through cognitively assorted machine learning algorithms are provided herein. A computer-implemented method includes: identifying, using system log data, a target variable based at least in part on correlations between a set of performance indicators of a system and the target variable, and threshold values for the performance indicators relative to the target variable; generating an inference model to predict when the system will enter an adverse state and identify one or more root causes of the system entering the adverse state; using machine reinforcement learning to determine an action policy including actions that remediate the adverse state; predicting that the system will enter the adverse state by applying the inference model to further system log data; and automatically executing one or more actions of the action policy in response to the prediction.