Abstract: A method comprises analyzing, using one or more machine learning algorithms, parameters corresponding to at least one microservice operation processed by a first instance of a microservice, and predicting, based at least in part on the analyzing, whether the at least one microservice operation is anomalous. The first instance of the microservice is designated as being in an anomalous state responsive to predicting that the at least one microservice operation is anomalous. One or more requests for the microservice are routed to a second instance of the microservice responsive to the anomalous state designation. The method further comprises causing logging of information corresponding to operation of the second instance of the microservice to be enabled at a designated level of granularity.

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 message-oriented-middleware system parameters from a plurality of message-oriented-middleware clusters, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, at least one anomaly in a message-oriented-middleware cluster of the plurality of message-oriented-middleware clusters. In the method, message metadata is collected from the message-oriented-middleware cluster, and at least part of the message metadata is transmitted to one or more remaining ones of the plurality of message-oriented-middleware clusters. At least the part of the message metadata corresponds to messaging operations to be transferred from the message-oriented-middleware cluster to the one or more remaining ones of the plurality of message-oriented-middleware clusters.

Abstract: One example method includes receiving, by a workspace size predicting engine, a workspace provisioning request including resource requirement information that specifies one or more features that are to be included when a workspace is provisioned. The one or more features include at least a machine learning (ML) model that is to be run in the workspace. The method also includes predicting, by the workspace size predicting engine, the one or more resources for provisioning the workspace that corresponds to the workspace provisioning request.

Abstract: One example method includes pre-processing a dataset, the dataset including data and/or metadata indicating attributes of a user, and the dataset also includes data and/or metadata that was generated as a result of an interaction between the user and a computing system, after the dataset is pre-processed, providing the dataset as an input to a machine learning model, using the machine learning model to generate, based on the input, respective target variable value predictions for each target in a group of targets, and each of the targets corresponds to a respective attribute of the user, using the target value variable predictions to create, or modify, a digital human that has attributes corresponding to the attributes of the user, and deploying the digital human so that the digital human is available to interact with the user.

Abstract: One example method includes receiving, by a workspace size predicting engine, a workspace provisioning request regarding a customer machine learning (ML) model, predicting, by the workspace size predicting engine, a size of a workspace that corresponds to the workspace provisioning request, receiving, by a datacenter host prediction engine from the workspace size predicting engine, the workspace size, and predicting, by the datacenter host prediction engine, a datacenter and/or host that is able to support requirements of the workspace.

Abstract: One example method includes instantiating an ambassador component in a cloud computing environment that comprises container instances and databases, accessing, by the ambassador component, a prediction as to a number of database connections expected to be used by a particular one of the container instances, and the prediction was generated by a prediction engine, receiving, by the ambassador component, a request from the particular container instance for access to one of the databases, and providing, by the ambassador component to the particular container instance from which the request for access was received, an access object usable by that particular container instance to use a database connection to access the database identified in the request.

Abstract: One example method includes pre-processing a dataset, wherein the dataset includes data and/or metadata that indicates a software configuration of an internet of things (IoT) device, and/or indicates a history of any performance issues and/or security issues experienced by the IoT device, after the dataset is pre-processed, providing the dataset as an input to a machine learning model, using the machine learning model to generate, based on the input, respective target variable value predictions for each target variable in a group of target variables, and a first one of the target variables corresponds to the software configuration, and a second one of the target variables corresponds to the history, and when the target variable value predictions indicate a potential security issue and/or a potential performance issue, with the IoT device, taking a remedial action to resolve the potential security issue and/or the potential performance issue.

Abstract: In one example method metadata about one or more entities of a ransomware threat and one or more relationships between the one or more entities of the ransomware threat is extracted from received cyber threat intelligence data by a threat decipher engine. The metadata about the one or more entities of the ransomware threat and the one or more relationships between the one or more entities of the ransomware threat is stored in a repository. A ransomware attack type included in received security sensor data is predicted by a threat prediction engine based on the metadata about the one or more entities of the ransomware threat and the one or more relationships between the one or more entities of the ransomware threat.

Abstract: One example method includes detecting an operation of an input device, obtaining a clickstream associated with a user, using information from the clickstream, generating a prediction as to a next action by the user using the input device, and presenting the prediction to the user for possible selection by the user. Selection of the prediction by the user eliminates the need for the user to perform input device manipulations that would otherwise be required if the prediction were not selected.

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: Methods, apparatus, and processor-readable storage media for predicting task execution efforts using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining intent information associated with at least a portion of a task by processing data related to the task using at least a first set of artificial intelligence techniques; determining task execution workflow data based at least in part on the intent information associated with at least a portion of the task; predicting one or more efforts associated with executing the task by processing at least a portion of the task execution workflow data using at least a second set of artificial intelligence techniques; and performing one or more automated actions based at least in part on at least one of the one or more predicted efforts associated with executing the task.

Abstract: Methods, apparatus, and processor-readable storage media for audio data-based device failure prediction using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining audio data associated with at least one device; modifying at least a portion of the obtained audio data using one or more data processing techniques; predicting at least one failure associated with the at least one device by classifying, into at least one of multiple device failure-related categories, at least a portion of the modified audio data using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the classifying of the at least a portion of the modified audio data.

Abstract: Methods, apparatus, and processor-readable storage media for automatically prioritizing supply chain-related demand using artificial intelligence techniques are provided herein.

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: 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 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.