Abstract: A method comprises collecting usage data for a plurality of automated resources integrated in a platform, computing a utilization score for one or more automated resources of the plurality of automated resources based at least in part on the usage data, and predicting a future utilization for the one or more automated resources using one or more machine learning algorithms. Integration of the one or more automated resources in the platform is controlled based at least in part on one or more of the utilization score and the future utilization.

Abstract: An example methodology includes, by a computing device, receiving information regarding a product from another computing device and determining one or more relevant features from the information regarding the product, the one or more relevant features influencing predictions of a product execution outcome and a lifespan estimate. The method also includes, by the computing device, generating, using a multi-target machine learning (ML) model, a first prediction of an execution outcome of the product and a second prediction of a lifespan estimate of the product based on the determined one or more relevant features, and sending the first and second predictions to the another computing device.

Abstract: An example methodology includes, by a computing device, receiving information regarding a field service dispatch from another computing device and determining one or more relevant features from the information regarding the field service dispatch, the one or more relevant features influencing prediction of a dispatch duration. The method also includes, by the computing device, generating, using a machine learning (ML) model, a prediction of a dispatch duration for the field service dispatch based on the determined one or more relevant features, and sending the prediction of the dispatch duration for the field service dispatch to the computing device.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving information regarding a customer to onboard to a managed service and determining one or more relevant features from the information regarding the customer, the one or more relevant features correlated with historical onboarding times. The method also includes determining, using a machine learning (ML) model, an expected time to onboard the customer to the managed service based on the one or more relevant features.

Abstract: An example methodology includes, by a computing device, receiving information regarding a new application from another computing device and determining one or more relevant features from the information regarding the new application, the one or more relevant features influencing predictions of any potential performance issue and any potential security issue. The method also includes, by the computing device, generating, using a multi-target machine learning (ML) model, a first prediction of any potential performance issue for the new application and a second prediction of any potential security issue for the new application based on the determined one or more relevant features, and sending the first and second predictions to the another computing device.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a product subscription service, receiving information regarding a hardware asset being returned at an end of a subscription and predicting, using a first machine learning (ML) model, whether the hardware asset has reached EOL. The method also includes, responsive to predicting that the hardware asset has reached EOL, creating, by the product subscription service, a work order to dispatch an eco-partner. The method may further include, by the product subscription service, responsive to predicting that the hardware asset has not reached EOL, predicting, using a second ML model, one or more new subscription orders matching the hardware asset and recommending the one or more matching new subscription orders as possible fits for the hardware asset.

Abstract: An apparatus comprises a processing device configured to obtain a first data structure characterizing a description of a given meeting, to perform natural language processing of the first data structure utilizing a first machine learning model to identify topics for the given meeting, to obtain a second data structure characterizing potential invitees for the given meeting, and to create a third data structure characterizing the identified topics of the given meeting and a given potential invitee for the given meeting. The processing device is also configured to process the third data structure utilizing a second machine learning model to generate a prediction as to a likelihood of the given potential invitee attending the given meeting, and to generate an invitation to the given meeting for the given potential invitee based at least in part on the prediction of the likelihood of the given potential invitee attending the given meeting.

Abstract: A method comprises collecting data corresponding to one or more code changes in response to committing of the one or more code changes to a code repository, and formatting the data into at least one data string. The at least one data string is inputted to one or machine learning models. Using the one or machine learning models, a natural language description of the one or more code changes is generated based at least in part on the at least one data string. The method further comprises causing transmission of the natural language description of the one or more code changes to a document repository.

Abstract: A method comprises receiving product selection data, wherein the product selection data characterizes at least one combination of at least two products. In the method, the product selection data is analyzed using one or more machine learning algorithms. The method further comprises predicting based, at least in part, on the analyzing, whether the at least one combination is anomalous. One or more alerts are generated in response to predicting that the at least one combination is anomalous.

Abstract: Methods, apparatus, and processor-readable storage media for automatically generating device-related temporal predictions using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to one or more aspects of at least one device-related repair task; generating one or more device-related temporal predictions associated with the at least one device-related repair task by processing at least a portion of the obtained data using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on at least a portion of the one or more device-related temporal predictions.

Abstract: A method comprises receiving a request to log at least one operation of a plurality operations, wherein the request includes one or more features of the at least one operation. The one or more features are analyzed using one or more machine learning algorithms. The method further comprises selecting, based at least in part on the analyzing, a log provider of a plurality of log providers to log the at least one operation, and interfacing with the log provider to enable logging of the at least one operation.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving a message for delivery to a message-oriented middleware (MOM) server and determining whether an anomaly is predicted in the MOM server. The method also includes, by the computing device, responsive to a determination that an anomaly is predicted in the MOM server, identifying an alternate MOM server for delivery of the message, and routing the message to the alternate MOM server. The method may also include, by the computing device, responsive to a determination that an anomaly is not predicted in the MOM server, delivering the message to the MOM server.

Abstract: Methods, apparatus, and processor-readable storage media for implementing topology explorers for message-oriented middleware using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to at least one messaging topology associated with at least one message-oriented middleware; predicting one or more anomalies associated with the at least one messaging topology by processing at least a portion of the obtained data using a first set of one or more machine learning techniques; recommending one or more alternate messaging topologies associated with the at least one message-oriented middleware by processing at least a portion of the one or more predicted anomalies and at least a portion of the obtained data using a second set of one or more machine learning techniques; and performing one or more automated actions based on the one or more predicted anomalies and/or the one or more recommended alternate messaging topologies.

Abstract: A method including: generating a conversation flow signature based on a set of communication transcripts, each of the communication transcripts being associated with a support request for a product, each of the communication transcripts being a text transcript of a communication between a respective customer and a respective customer support agent; classifying the conversation flow signature into one of a plurality of categories, the conversation flow signature being classified by using a machine learning classifier that is trained based on customer support records, each of the plurality of categories corresponding to a respective set of steps for configuring or repairing the product; and outputting an indication of the respective set of steps that is associated with the category in which the conversation flow signature is classified.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, determining a source platform code for migration from a source platform to a target platform and determining one or more attributes of the source platform code. The method also includes determining, using a machine learning (ML) model, one or more existing templates based on the one or more attributes of the source platform code, and recommending the one or more existing templates for use in generating a template for migration of the source platform code to the target platform. The template for the source platform code is configured to convert the source platform code to a target platform code suitable for the target platform. The one or more existing template can then be used to generate a template for migrating the source platform code to a target platform code suitable for the target platform.

Abstract: Methods, apparatus, and processor-readable storage media for automatically performing varied security scans on distributed files using machine learning techniques are provided herein. An example computer-implemented method includes obtaining at least one input file from one of multiple source channels; identifying a data security scan operation, from a set of multiple data security scan operations, for the at least one input file by processing historical data attributed to the at least one input file using machine learning techniques; executing the identified data security scan operation on the at least one input file; generating a hash of the at least one input file and information pertaining to results of the executed data security scan operation; caching the generated hash in at least one cache; and performing automated actions based on the caching of the generated hash in the at least one cache.

Abstract: A computer-implemented method is provided. Historical data associated with operation of a plurality of computing entities is received. Inventory information is tracked based on the historical data, including discovering when new computing entities are added to the plurality of computing entities. Transaction information is tracked based on the historical data. Transaction information comprises information associated with transactions of the plurality of computing entities and with a volume of transactions. Cost information associated with the transactions of each computing entity, is tracked. Utilization information, comprising information relating to utilization of an infrastructure of each respective computing entity, is also tracked. A database is built comprising at least one of inventory, transaction, and cost information. An output is generated providing a report of information on one or more computing entities in the plurality of computing entities.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving a data access event, wherein the data access event relates to a data element and determining whether the data element is a personally identifiable information (PII) data element. The method also includes, responsive to a determination that the data element is a PII data element, by the computing device, predicting, using a machine learning (ML) model, a PII protection policy appropriate for the PII data element, and applying the PII protection policy to the PII data element. The method further includes, by the computing device, returning the data access event including the PII data element with the PII protection policy applied.

Abstract: Methods, apparatus, and processor-readable storage media for automatically processing batch jobs in cloud environments using artificial intelligence techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automatically determining resource support parameters using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining input data comprising data pertaining to at least one resource and data pertaining to one or more users associated with the at least one resource; predicting one or more resource support parameters for the at least one resource and the one or more users associated therewith by processing at least a portion of the input data using one or more artificial intelligence techniques; determining one or more resource support-related data allocations, across one or more systems, for the at least one resource and the one or more users associated therewith based on the one or more predicted resource support parameters; and performing one or more automated actions based on the one or more resource support-related data allocations.