Abstract: Discussed herein is a framework that provisions for customized processing for different classes of traffic. A network device in a communication path between a source host machine and a destination host machine extracts a tag from a packet received by the network device. The packet originates at a source executing on the source host machine and whose destination is the destination host machine. The tag set by the source and indicative of a first traffic class to be associated with the packet, the first traffic class being selected by the source from a plurality of traffic classes. The network device determines, based on the tag, that the first traffic class corresponds to a latency sensitive traffic and processes the packet using one or more settings configured at the network device for processing packets associated with the first traffic class.

Abstract: Various embodiments of apparatuses and methods for an automated machine learning pipeline service and an automated machine learning pipeline generator are described. In some embodiments, the service receives a request from a user to generate a machine learning solution, as well as a dataset that comprises values with different user variable types, and mapping of the user variable types to pre-defined types. The generator can validate the dataset, enrich the values of the dataset using external data sources, transform values of the dataset based on the pre-defined types, train a machine learning model using the enriched and transformed values, and compose an executable package, comprising enrichment recipes, transformation recipes, and the trained machine learning model, that generates scores for other data when executed. The service can further test the executable package using testing data, and provide results of the test to the user.

Abstract: Discussed herein is a framework that provisions for customized processing for different classes of traffic. A network device in a communication path between a source host machine and 5 a destination host machine extracts a tag from a packet received by the network device. The packet originates at a source executing on the source host machine and whose destination is the destination host machine. The tag set by the source and indicative of a first traffic class to be associated with the packet, the first traffic class being selected by the source from a plurality of traffic classes. The network device determines the first traffic class based on the tag extracted from the packet and 10 processes the packet based on the first traffic class.

Abstract: Discussed herein is a framework that provisions for customized processing for different classes of traffic. A network device in a communication path between a source host machine and a destination host machine extracts a tag from a packet received by the network device. The packet originates at a source executing on the source host machine and whose destination is the destination host machine. The tag set by the source and indicative of a first traffic class to be associated with the packet, the first traffic class being selected by the source from a plurality of traffic classes. The network device determines, based on the tag, that the first traffic class corresponds to a bandwidth sensitive traffic and processes the packet using one or more settings configured at the network device for processing packets associated with the first traffic class.

Abstract: Systems, methods, and computer-readable media are disclosed for the dynamic, real-time detection and clustering of emerging fraud patterns. Example methods may include determining an expected account registration volume and an actual account registration volume during a same period of time. Certain methods may include determining an abnormal fluctuation in account registration volume based on a difference between the expected account registration volume and the actual account registration volume during the period of time. Certain methods may include generating subsets of account registrations received during the period of time based on one or more shared characteristics. Certain methods may include generating an account cluster based on the subsets of account registrations. Certain methods may include sending the account cluster to a bulk closure system.