Abstract: For each corresponding configuration item type of a plurality of different configuration item types, a corresponding multi-variate machine learning model of a plurality of multi-variate machine learning models is trained to perform anomaly detection for a corresponding configuration item type of the plurality of different configuration item types. In response to detecting, via a univariate machine learning model, an anomaly associated with a specific configuration item type of the plurality of different configuration item types, an execution of a particular multi-variate machine learning model of the plurality of multi-variate machine learning models is initiated for the specific configuration item type. An output of the execution of the particular multi-variate machine learning model is evaluated to determine an anomaly detection result.

Abstract: A training dataset for anomaly detection is received. An unsupervised machine learning model is trained using at least a portion of the training dataset to generate a trained unsupervised machine learning model. A supervised machine learning model is trained using an output from the unsupervised machine learning model and an anomaly detection feedback associated with the output from the unsupervised machine learning model to generate a trained supervised machine learning model. Both the trained unsupervised machine learning model and the trained supervised machine learning model are provided for combined use in machine learning anomaly detection inference.

Abstract: Systems and methods for automatically applying style characteristics to images. The images may comprise text. Additionally, the images may be synthetically generated. A style template containing information about style characteristics is passed to an extraction module, which extracts that information and thus determines the style characteristics. The style characteristics are then passed to an application module, which also receives an input image. The application module applies the style characteristics to the image, thereby producing an output image in the intended style. The extraction module and the application module may comprise machine learning elements. The output image may be used in later processes, including, among others, in training processes for optical character recognition models.

Abstract: A system for anomaly estimation for a telephonic call is described, receiving a call object including an identifier field associating the call object to a purported user; retrieving a user data object associated with the purported user, and processing the user data object to retrieve one or more vectorized user features associated with the purported user. A neural network processes the one or more vectorized call features and the one or more vectorized user features through a machine learning model.

Abstract: A system for anomaly estimation for a telephonic call is described, receiving a call object including an identifier field associating the call object to a purported user; retrieving a user data object associated with the purported user, and processing the user data object to retrieve one or more vectorized user features associated with the purported user. A neural network processes the one or more vectorized call features and the one or more vectorized user features through a machine learning model.