Abstract: A method comprises receiving a request to predict a type and a quantity of respective ones of a plurality of resources for a computing environment. Using a multiple output classification and regression machine learning model, the type and the quantity of the respective ones of the plurality of resources are predicted in response to the request. The machine learning model is trained with a dataset comprising historical resource data corresponding to respective ones of a plurality of users.

Abstract: Methods, apparatus, and processor-readable storage media for automatically managing event-related communication data using machine learning techniques are provided herein.

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: In one aspect, an example methodology implementing the disclosed techniques includes, by a warranty system, receiving a customer-specific usage-related data for a product at a customer location and generating a feature vector for the product, wherein the feature vector represents one or more features from the customer-specific usage-related data. The method also includes, by the warranty system using a trained incident prediction module, predicting a number of future incidents for the product at the customer location based on the first feature vector. The method further includes, by the warranty system, determining a price for an extended warranty for the product based on the predicted number of future incidents for the product at the customer location.

Abstract: A method comprises collecting parameters corresponding to processing by a first application programming interface of at least one application programming interface transaction, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, whether the at least one application programming interface transaction is anomalous. In the method, the first application programming interface is designated as being in an anomalous state responsive to predicting that the at least one application programming interface transaction is anomalous. One or more application programming interface requests for the first application programming interface are routed to a second application programming interface responsive to the anomalous state designation.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a corpus of historical recycling settlement data regarding a plurality of recycled assets, the historical recycling settlement data including information pertaining to a recycling of each asset of the plurality of recycled assets, wherein the information pertaining to the recycling includes a recovery value of each recycled asset. The method also includes generating a training dataset from the corpus of historical recycling settlement data, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a recycled asset, and training a recovery value prediction module using the plurality of training samples. Once trained, the recovery value prediction module can predict a recovery value of a provided asset.

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

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: 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.