Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting dispatch-related data using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data, from one or more user channels, pertaining to at least one issue; determining, based at least in part on the obtained data, that at least one dispatch is to be carried out in connection with attempting to resolve the at least one issue; predicting, by processing at least a portion of the obtained data using one or more machine learning techniques, an approval mode associated with the at least one dispatch and at least one outcome associated with the at least one dispatch; and performing one or more automated actions based at least in part on one or more of the predicted approval mode and the at least one predicted outcome.

Abstract: Methods, apparatus, and processor-readable storage media for automatically classifying cloud infrastructure components for prioritized multi-tenant cloud environment resolution using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining historical data pertaining to a multi-tenant cloud environment; training one or more artificial intelligence techniques, using at least a portion of the obtained historical data, for classifying cloud infrastructure components for prioritizing incident-related resolution; classifying one or more cloud infrastructure components, within the multi-tenant cloud environment and associated with one or more server-related issues, into one or more of multiple resolution priority classes; and performing one or more automated actions based at least in part on the classifying of the one or more cloud infrastructure components.

Abstract: Methods, apparatus, and processor-readable storage media for automatically classifying images for storage-related determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one image associated with an enterprise; classifying one or more portions of the at least one image into one or more enterprise-related categories by processing at least a portion of the at least one image using one or more artificial intelligence techniques; and performing, based at least in part on the classifying of the one or more portions of the at least one image, one or more automated actions pertaining to storing the at least one image in at least one of multiple storage options.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a survey workflow module, receiving a survey request event for which a survey is requested and generating a first feature vector for the survey request event, wherein the first feature vector comprises features extracted from the survey request event. The method also includes predicting, by a first trained prediction module, whether the survey request event will result in a survey based on the first feature vector, wherein the first trained prediction module is trained using a training dataset generated from a corpus of historical survey request event-survey data.

Abstract: Methods, apparatus, and processor-readable storage media for automatically classifying cloud infrastructure components for prioritized multi-tenant cloud environment resolution using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining historical data pertaining to a multi-tenant cloud environment; training one or more artificial intelligence techniques, using at least a portion of the obtained historical data, for classifying cloud infrastructure components for prioritizing incident-related resolution; classifying one or more cloud infrastructure components, within the multi-tenant cloud environment and associated with one or more server-related issues, into one or more of multiple resolution priority classes; and performing one or more automated actions based at least in part on the classifying of the one or more cloud infrastructure components.

Abstract: Methods, apparatus, and processor-readable storage media for generating temporal predictions for provisioning cloud resources using trained machine learning techniques are provided herein. An example computer-implemented method includes training one or more machine learning techniques using historical data pertaining to cloud resource provisioning and related order information; obtaining data pertaining to at least one order related to one or more hardware components, wherein the at least one order includes one or more cloud resources; generating at least one temporal prediction for provisioning the one or more cloud resources, in fulfillment of the at least one order, by processing at least a portion of the obtained data using the one or more trained machine learning techniques; and performing one or more automated actions based at least in part on the at least one generated temporal prediction.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a survey workflow module, receiving a survey request event for which a survey is requested and generating a first feature vector for the survey request event, wherein the first feature vector comprises features extracted from the survey request event. The method also includes predicting, by a first trained prediction module, whether the survey request event will result in a survey based on the first feature vector, wherein the first trained prediction module is trained using a training dataset generated from a corpus of historical survey request event-survey data.