Abstract: Methods, apparatus, and processor-readable storage media for data integration demand management using artificial intelligence are provided herein. An example computer-implemented method includes obtaining at least one data integration demand, wherein the at least one data integration demand comprises textual information provided by at least one user; determining multiple parameters of the at least one data integration demand by applying one or more machine learning natural language processing techniques to at least a portion of the textual information provided by the at least one user; generating at least one delivery date prediction for the at least one data integration demand by applying one or more artificial intelligence techniques to the multiple determined parameters of the at least one data integration demand; and performing one or more automated actions based at least in part on the at least one generated delivery date prediction.

Abstract: Methods, apparatus, and processor-readable storage media for device component management using deep learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more enterprise devices; determining, for each of the one or more enterprise devices, values for multiple device attributes by processing the obtained telemetry data; generating, for each of the one or more enterprise devices, at least one prediction related to lifecycle information of at least one device component by processing the determined attribute values using one or more deep learning techniques; and performing one or more automated actions based at least in part on the at least one generated prediction.

Abstract: Methods, apparatus, and processor-readable storage media for automated hyperparameter tuning in machine learning algorithms are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting device failure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from at least one client device; predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques; predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.

Abstract: Methods, apparatus, and processor-readable storage media for artificial intelligence-based redundancy management are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more client devices within at least one system; predicting one or more hardware component failures in at least a portion of the one or more client devices within the at least one system by processing at least a portion of the telemetry data using a first set of one or more artificial intelligence techniques; determining, using a second set of one or more artificial intelligence techniques, one or more redundant hardware components for implementation in connection with the one or more predicted hardware component failures; and performing at least one automated action based at least in part on the one or more redundant hardware components.

Abstract: Methods, apparatus, and processor-readable storage media for implementing a message management framework using machine learning techniques are provided herein. An example computer-implemented method includes processing a message comprising identifying at least one message type of the message; determining, based at least in part on the at least one identified message type, one or more message failures by applying one or more machine learning-based rules to at least a portion of the message; determining one or more remedial actions by processing, using one or more machine learning techniques, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message; and performing one or more automated actions based at least in part on the one or more determined remedial actions.

Abstract: Methods, apparatus, and processor-readable storage media for dynamically modifying call flow management using a common data model interface for automated call distribution systems are provided herein. An example computer-implemented method includes obtaining data from at least one automated call distribution system; processing at least a portion of the obtained data using at least one common data model pertaining to call flow management; configuring at least one relational database using at least a portion of the processed data; and performing at least one automated action related to call flow management associated with the at least one automated call distribution system using the at least one relational database and one or more user interfaces.

Abstract: Methods, apparatus, and processor-readable storage media for cognitive device management using artificial intelligence are provided herein.

Abstract: Techniques are provided for centralized enterprise integration and services monitoring in computing environments. For example, a system comprises one or more processors operatively coupled to one or more memories to form a monitoring portal system, wherein the monitoring portal framework is configured to perform steps of: providing one or more graphical user interfaces; and enabling centralized enterprise integration and services monitoring through the one or more graphical user interfaces.

Abstract: Methods, apparatus, and processor-readable storage media for implementing extensible upgrade and modification as a service are provided herein. An example computer-implemented method includes processing one or more modifiers, wherein each modifier includes an independent processing unit having a given canonical structure and is configured to execute one or more automated actions related to at least one of application modification and application migration; obtaining data pertaining to multiple applications across multiple computing environments; determining, based at least in part on processing at least a portion of the obtained data, at least one of the one or more modifiers applicable for use in executing at least one of the one or more automated actions in connection with at least a portion of the multiple applications; and executing the at least one of the one or more automated actions using the at least one determined modifier.

Abstract: Methods, apparatus, and processor-readable storage media for dynamically modifying call flow management using a common data model interface for automated call distribution systems are provided herein. An example computer-implemented method includes obtaining data from at least one automated call distribution system; processing at least a portion of the obtained data using at least one common data model pertaining to call flow management; configuring at least one relational database using at least a portion of the processed data; and performing at least one automated action related to call flow management associated with the at least one automated call distribution system using the at least one relational database and one or more user interfaces.

Abstract: Methods, apparatus, and processor-readable storage media for implementing Internet of Things- (IoT-) enabled connectivity devices for processing operation information of devices lacking network connectivity are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automated capacity management using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining image data pertaining to occupancy of a confined space; determining a level of occupancy in the confined space and one or more types of entities occupying the confined space by processing the image data using a first set of one or more artificial intelligence techniques comprising at least a first machine learning model; automatically determining one or more capacity management parameters with respect to the confined space by analyzing the determined level of occupancy and the one or more determined types of entities using a second set of one or more artificial intelligence techniques comprising at least a second machine learning model; and performing one or more automated actions based at least in part on the one or more determined capacity management parameters.

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 artificial intelligence-based redundancy management are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more client devices within at least one system; predicting one or more hardware component failures in at least a portion of the one or more client devices within the at least one system by processing at least a portion of the telemetry data using a first set of one or more artificial intelligence techniques; determining, using a second set of one or more artificial intelligence techniques, one or more redundant hardware components for implementation in connection with the one or more predicted hardware component failures; and performing at least one automated action based at least in part on the one or more redundant hardware components.

Abstract: Methods, apparatus, and processor-readable storage media for machine learning-based determinations of lifespan information for devices in an Internet of Things (IoT) environment are provided herein. An example computer-implemented method includes automatically obtaining device telemetry data from one or more IoT-enabled devices within an IoT network, automatically determining lifespan-related information pertaining to at least a portion of the one or more IoT-enabled devices by applying a machine learning model to the device telemetry data, and initiating at least one automated action in response to the determined lifespan-related information.

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.

Abstract: Methods, apparatus, and processor-readable storage media for device temperature impact management using machine learning techniques are provided herein. An example computer-implemented method includes obtaining one or more notifications pertaining to temperature information associated with one or more devices; generating one or more predictions pertaining to at least one potential problem with at least one of the one or more devices by applying one or more machine learning models to the one or more obtained notifications; determining one or more automated actions related to the one or more predictions by utilizing at least one neural network to process data associated with temperature control for the at least one device; and automatically initiating the one or more automated actions.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting device failure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from at least one client device; predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques; predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.