Abstract: Examples of systems are described for calculating a probability of exceeding storage capacity of a virtualized system in a particular time period using probabilistic models. The probabilistic models may advantageously take variances of storage capacity into consideration.

Abstract: A machine learning model is trained to quantify the relationship of specific terms or groups of terms to the outcome of an event. To train the model, a set of data including structured and unstructured data and information describing previous outcomes of the event is received. The unstructured data is analyzed and features corresponding to one or more terms are identified, extracted, and merged together with features extracted from the structured data. The model is trained based at least in part on a set of the merged features, each of which is associated with a value quantifying a relationship of the feature to the outcome of the event. An output is generated based at least in part on a likelihood of the outcome of the event that is predicted using the model and input values corresponding to at least some of the set of features used to train the model.