Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about at least one of respective protocols and respective interfaces associated with respective ones of the plurality of components. The data is analyzed to determine at least one of the respective protocols and the respective interfaces associated with the respective ones of the plurality of components. In the method, operations of one or more components of the plurality of components are tested based at least in part on the determination of the at least one of the respective protocols and the respective interfaces. The method further includes outputting respective statuses of the one or more components, wherein the respective statuses are derived at least in part from the testing.

Abstract: A method comprises collecting operational data for one or more devices and identifying one or more anomalies associated with the one or more devices based at least in part on the collected operational data. At least a portion of the collected operational data corresponding to the identified one or more anomalies is analyzed, and a probability of automatic resolution for respective ones of the identified one or more anomalies is determined based at least in part on the analysis. The identifying, the analyzing and the determining are performed using one or more machine learning models.

Abstract: A method comprises collecting network data associated with data transmission in a computing environment. The method also comprises identifying, using one or more machine learning models, at least one intrusion type affecting the computing environment. The identification of the at least one intrusion type is based at least in part on the collected network data. In the method, one or more remedial communications addressing the at least one intrusion type are generated, and the one or more remedial communications are transmitted to a user.

Abstract: In some examples, a computing device may receive, from an email server, an email rule comprising conditions and actions and determine a subset of archived emails stored on the computing device that satisfy the conditions. The computing device may perform the actions to the subset of archived emails, such as placing a legal hold on the subset of archived emails, creating and sending copies of the subset of archived emails to the email server, or deleting, from the computing device, the subset of archived emails. After determining that a user is attempting to perform a prohibited action on (e.g., delete, edit, forward) an email of the subset of archived emails, the computing device may perform a protective action, such as logging the user out of an email account, preventing the user from logging in to the email account, automatically sending an email to an administrator or the like.

Abstract: A method comprises training at least one machine learning model with training data from a plurality of support cases, and receiving an input comprising data associated with at least one support case. The input is analyzed using the at least one machine learning model to determine one or more resolution options for the at least one support case, and the one or more resolution options are transmitted to an agent.

Abstract: A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about one or more issues with the plurality of components. The data is analyzed and categorized based at least in part on the analysis. In the method, one or more application programming interfaces (APIs) are selected to monitor respective statuses of the plurality of components, wherein the selection is based at least in part on the categorization of the data, and the data is pushed to the one or more APIs.

Abstract: A method comprises receiving at least one natural language input corresponding to at least one task, and analyzing the at least one natural language input using one or more machine learning models to determine one or more parameters of the at least one task. One or more skills and/or availabilities of respective ones of a plurality of resources are identified. The method further comprises identifying at least a subset of the plurality of resources to perform the at least one task based at least in part on the one or more parameters and on the one or more skills and/or the availabilities. The at least one task is routed to one or more resources of the subset.

Abstract: A method comprises receiving a request for validation of software comprising one or more applications, analyzing the request and generating one or more validation steps based at least in part on the analysis. In the method, a time to complete the one or more validation steps is predicted. The predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications.

Abstract: An apparatus comprises a processing device configured to generate a model characterizing relationships between devices in an information technology infrastructure, to build a device dependency chain for the devices based at least in part on the generated model, and to predict workload for each of the devices in each of two or more time slots of a given time period. The processing device is also configured to determine a deployment schedule for the devices based at least in part on the device dependency chain and the predicted workload for each of the devices in each of the two or more time slots of the given time period, to identify an operation to be performed on the devices, and to utilize the deployment schedule to select ones of the devices on which to perform the identified operation in each of the two or more time slots of the given time period.

Abstract: A method comprises receiving at least one natural language input, converting the at least one natural language input to a graphical input, retrieving relationship data from a graph database based at least in part on the graphical input, and generating at least one natural language response of a virtual assistant to the at least one natural language input based at least in part on the relationship data from the graph database. At least the generating is performed using one or more machine learning models.

Abstract: As an example, a software application executing on a computing device of a channel partner may access an application programming interface (API) hosted by a server to access a manufacturer's data. The computing device may provide criteria to the API to identify a subset of a set of configurations. The computing device may be used to select a particular configuration in the subset and to perform one or more modifications to the particular configuration to create a modified configuration. The computing device may use the API to access a verification module to verify whether the modified configuration can be built. If the modified configuration is unbuildable, the computing device may be used to make additional modifications to the particular configuration until the modified configuration is verified as buildable. The channel partner may place an order for a particular quantity of the modified configuration.

Abstract: A method comprises receiving an input of at least one image associated with an issue. In the method, text is extracted from the at least one image, and an intent is determined from the extracted text. The method further includes recommending a response to the issue based at least in part on the determined intent, and transmitting the recommended response to a user. The extracting, determining and recommending are performed at least in part using one or more machine learning models.

Abstract: An apparatus comprises a processing device configured to generate a model of a plurality of devices characterizing relationships between the devices, to build a device dependency chain for the devices based on the model, to predict workload for each of the devices in one or more time slots of a given time period, and to determine a deployment schedule for the devices based on the device dependency chain and the predicted workload. The processing device is also configured to utilize the deployment schedule to select a device of the devices on which to perform an operation, to determine whether the selected device corresponds to an additional device of the devices configured to operate in place of the selected device during performance of the operation, and to control performance of the operation on the selected device responsive to the determination of whether the selected device corresponds to the additional device.

Abstract: A method comprises receiving at least one natural language input, and determining an intent of the at least one natural language input. In the method, a virtual assistant of a plurality of virtual assistants is recommended to respond to the at least one natural language input based at least in part on the determined intent, and the at least one natural language input is transmitted to the recommended virtual assistant. The determining and recommending are performed using one or more machine learning models, and the plurality of virtual assistants respectively correspond to a plurality of different functions of an enterprise.

Abstract: An apparatus comprises a processing device configured to generate a model characterizing relationships between devices in an information technology infrastructure, to build a device dependency chain for the devices based at least in part on the generated model, and to predict workload for each of the devices in each of two or more time slots of a given time period. The processing device is also configured to determine a deployment schedule for the devices based at least in part on the device dependency chain and the predicted workload for each of the devices in each of the two or more time slots of the given time period, to identify an operation to be performed on the devices, and to utilize the deployment schedule to select ones of the devices on which to perform the identified operation in each of the two or more time slots of the given time period.

Abstract: A method comprises receiving configuration data comprising a plurality of parameters of at least one computing environment. One or more of the parameters correspond to integration of one or more elements of the least one computing environment with one or more other elements at least one of within and external to the least one computing environment. In the method, the parameters are analyzed to detect one or more anomalies in the configuration data, and the configuration data and the one or more detected anomalies are inputted to one or more machine learning models. The method also comprises determining, using the one or more machine learning models, one or more modifications to at least one of the plurality of parameters based on the inputted configuration data and one or more detected anomalies, and transmitting the determination comprising the one or more modifications to a user over a communications network.

Abstract: An information handling system receives historical data that includes configuration information and recovery values of recycled assets, and builds a training dataset from a subset of the historical data. The information handling system also builds a validation dataset from another subset of the historical data, and trains a machine learning model on the training dataset to learn the recovery values of the recycled assets. The system also validates the machine learning model based on the validation dataset, tunes a hyperparameter of the machine learning model, and predicts a recovery value of a recyclable asset using the machine learning model utilizing an extreme gradient boosting algorithm.

Abstract: A method may include receiving data from a device within a network, wherein the data is associated with one or more features of the device, and determining a subset of the features of the device that is associated with a runtime behavior of the device. The method may also perform a univariate analysis on a feature dataset that is associated with the subset of the features of the device, perform a multivariate analysis on the feature dataset that is associated with correlated features in the subset of the features, and generate a device signature based on the univariate analysis and the multivariate analysis.

Abstract: Methods, apparatus, and processor-readable storage media for generating data replication configurations using AI techniques are provided herein. An example computer-implemented method includes obtaining input data pertaining to at least one data replication operation; determining a set of configuration parameters for the at least one data replication operation by applying one or more AI techniques to at least a portion of the input data; and performing one or more automated actions based at least in part on the determined set of configuration parameters for the at least one data replication operation.

Abstract: Methods, apparatus, and processor-readable storage media for automated configuration determinations for data center devices using artificial intelligence are provided herein. An example computer-implemented method includes obtaining input information pertaining to one or more device-related changes to a data center; obtaining telemetry data attributed to one or more devices in the data center; determining one or more device configurations for implementation in at least one device in the data center in connection with the one or more device-related changes by processing the input information and the obtained telemetry data using one or more artificial intelligence techniques; and performing at least one automated action based at least in part on the one or more determined device configurations.