Abstract: A cloud compute resource provider implements a method for automatically adjusting a quota of compute resources allocated to an individual customer subscription. The method includes determining a current usage metric for the individual customer subscription for a recent time interval; determining whether a subscription-based historical usage model has been trained on historical usage data of the individual customer subscription; and responsive to determining that the subscription-based historical usage model has been trained, executing the subscription-based historical usage model to generate a future resource usage metric predicting a usage of the customer subscription over a future time interval; and outputting a recommended adjusted resource quota for the individual subscription, the predicted future resource usage metric satisfying a target utilization of the recommended adjusted resource quota.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for automating server orders and generating interactive server inventory insights are provided. An aggregate target service level for a data center may be determined. A number of server clusters needed to fulfill the target service level on a future date may be determined. Safety stock values corresponding to a buffer number of server clusters needed to account for supply and demand variability for the data center and for one or more upstream nodes in the supply chain may be determined. The safety stock values that correspond to the most cost-effective scenario that still meets the target service level may be determined. Server orders corresponding to that scenario may be automatically placed and interactive server inventory insights corresponding to one or more scenario iterations may be generated and surfaced.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for forecasting cloud service capacity and demand are presented. A cloud capacity forecast comprising a plurality of projections for a future timeframe may be generated via application of a predictive simulation model to one or more cloud service supply variables. A cloud demand forecast comprising a plurality of projections for the future timeframe may be generated via application of a predictive simulation model to a plurality of cloud service demand variables. A determination may be made as to a number and/or percentage of the projections for which the cloud capacity exceeds the cloud demand A confidence score that cloud service demand can be met may be calculated. In some examples, one or more prophylactic actions may be automatically performed if the confidence score is below a threshold value.

Abstract: An “Engagement Predictor” provides various techniques for predicting whether things and concepts (i.e., “nuggets”) in content will be engaging or interesting to a user in arbitrary content being consumed by the user. More specifically, the Engagement Predictor provides a notion of interestingness, i.e., an interestingness score, of a nugget on a page that is grounded in observable behavior during content consumption. This interestingness score is determined by evaluating arbitrary documents using a learned transition model. Training of the transition model combines web browsing log data and latent semantic features in training data (i.e., source and destination documents) automatically derived by a Joint Topic Transition (JTT) Model. The interestingness scores are then used for highlighting one or more nuggets, inserting one or more hyperlinks relating to one or more nuggets, importing content relating to one or more nuggets, predicting user clicks, etc.

Abstract: A method is disclosed for facilitating electronic communication between an external contact and an internal point person and between the internal point person and one or more internal team members. An internal collaboration network is created based on a first e-mail received from or sent to the external contact. The membership of the internal collaboration network is based on recipient information from the first e-mail and includes the internal point person but excludes the external contact. A message from the internal point person is automatically sent to the members of the internal collaboration network for display in a private collaboration view of an app on a computing device. Later-sent e-mails from the external contact that have subjects matching the subject of the first e-mail are automatically sent to the membership for display in a customer view of the app.

Abstract: A method is disclosed for facilitating electronic communication between an external contact and an internal point person and between the internal point person and one or more internal team members. An internal collaboration network is created based on a first e-mail received from or sent to the external contact. The membership of the internal collaboration network is based on recipient information from the first e-mail and includes the internal point person but excludes the external contact. A message from the internal point person is automatically sent to the members of the internal collaboration network for display in a private collaboration view of an app on a computing device. Later-sent e-mails from the external contact that have subjects matching the subject of the first e-mail are automatically sent to the membership for display in a customer view of the app.

Abstract: In various embodiments, methods, computer-storage media, and systems for generating optimal allocation of hardware inventory procurements are provided. An adjusted total cost, based on assessment properties for hardware providers is determined. Assessment properties can be classified as assessment-property-metrics and assessment-property-data, where the assessment-property-metrics are computed using the assessment-property-data. At least two cost assessment-property-metrics that correspond to hardware providers of a hardware requestor having hardware inventory procurements, are identified to determine the adjusted total cost.

Abstract: An “Engagement Predictor” provides various techniques for predicting whether things and concepts (i.e., “nuggets”) in content will be engaging or interesting to a user in arbitrary content being consumed by the user. More specifically, the Engagement Predictor provides a notion of interestingness, i.e., an interestingness score, of a nugget on a page that is grounded in observable behavior during content consumption. This interestingness score is determined by evaluating arbitrary documents using a learned transition model. Training of the transition model combines web browsing log data and latent semantic features in training data (i.e., source and destination documents) automatically derived by a Joint Topic Transition (JTT) Model. The interestingness scores are then used for highlighting one or more nuggets, inserting one or more hyperlinks relating to one or more nuggets, importing content relating to one or more nuggets, predicting user clicks, etc.