Accenture Global Solutions Limited

Kun Qiu

Vijay Desai

Daniel Marcus Lombardo

Laser Seymour Kaplan

Revathi Subramanian

Qiang Song

Suraj Thulkar

Durga Kalyan Ganjapu

A device receives historical data associated with multiple cloud computing environments, trains one or more machine learning models, with the historical data, to generate trained machine learning models that generate outputs, and trains a model with the outputs to generate a trained model. The device receives particular data, associated with a cloud computing environment, that includes data identifying usage of resources associated with the cloud computing environment, and processes the particular data, with the trained machine learning models, to generate anomaly scores indicating anomalous usage of the resources associated with the cloud computing environment. The device processes the one or more anomaly scores, with the trained model, to generate a final anomaly score indicating anomalous usage of at least one of the resources associated with the cloud computing environment, and performs one or more actions based on the final anomaly score.

Utilizing machine learning models to process resource usage data and to determine anomalous usage of resources

A device may receive data that is related to historical reports associated with an organization, historical audits of the historical reports, and individuals associated with the historical reports. The device may determine a multi-entity profile for the data. The multi-entity profile may include a set of groupings of the data by a set of attributes included in the data. The device may determine, using the multi-entity profile, a set of supervised model features for the historical reports. The device may determine, using the multi-entity profile, a set of unsupervised model features for the historical reports independent of the historical audits. The device may determine, utilizing a model, a score for a report. The device may perform one or more actions.

Detecting an issue related to a report

A device may receive historical cloud data associated with resources of a cloud computing environment, and may receive historical customer data associated with requested resource usage by customers of the cloud computing environment. The device may determine a usage growth profile based on the historical cloud data and the historical customer data, and may determine, based on the historical cloud data and the historical customer data, usage deviation data indicating deviations between actual and planned resource usage. The device may train a model, with the usage growth profile and the usage deviation data, to generate a trained model, and may receive a request for new resource usage by a customer associated with the cloud computing environment. The device may process the request for the new resource usage, with the trained model, to generate projected resource usage data, and may perform actions based on the projected resource usage data.

Utilizing a machine learning model to predict a quantity of cloud resources to allocate to a customer

A device may receive data related to operations of a plurality of managed devices. The device may determine, after receiving the data, a multi-entity profile for the data. The device may determine, using the multi-entity profile, a set of sub-models for the data after determining the multi-entity profile. The set of sub-models may be associated with processing the data in a contextualized manner. The device may generate a model based on the set of sub-models. The device may perform one or more actions related to the plurality of managed devices or the at least one alert based on respective scores associated with the plurality of managed devices after generating the model.

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San Diego, CA, United States of America

Kun Qiu