Abstract: A system for automatically managing the delivery of media assets allocates the media assets to delivery slots of a media delivery servers so that consumers will receive the media assets when they consume digital media programming at times that correspond to the delivery slots. An example is the automated allocation of sponsored videos to television programs airing on a particular afternoon. The system includes data stores and a campaign manager system. The campaign manager system will automatically allocate digital media assets to delivery slots in a campaign to generate scheduling files that media servers will use to present the allocated media assets to consumers during the assigned delivery slots via media consumption devices.

Abstract: In some embodiments, a first plurality of representations are extracted from a first data set. A first set of distributions are generated based on the first plurality of representations. A machine learning model is trained based on the first plurality of representations and the first set of distributions. A second plurality of representations are extracted from a second data set different from the first data set. The machine learning model is executed based on the second plurality of representations to produce a second set of distributions. An anomaly score is determined for each datum from the second data set to produce a set of anomaly scores. The set of anomaly scores are determined based on the first set of distributions and the second set of distributions. A notification is generated when at least one anomaly score from the set of anomaly scores is larger than a predetermined threshold.

Abstract: In some embodiments, a first plurality of representations are extracted from a first data set. A first set of distributions are generated based on the first plurality of representations. A machine learning model is trained based on the first plurality of representations and the first set of distributions. A second plurality of representations are extracted from a second data set different from the first data set. The machine learning model is executed based on the second plurality of representations to produce a second set of distributions. An anomaly score is determined for each datum from the second data set to produce a set of anomaly scores. The set of anomaly scores are determined based on the first set of distributions and the second set of distributions. A notification is generated when at least one anomaly score from the set of anomaly scores is larger than a predetermined threshold.

Abstract: A system for automatically managing the delivery of media assets allocates the media assets to delivery slots of a media delivery servers so that consumers will receive the media assets when they consume digital media programming at times that correspond to the delivery slots. An example is the automated allocation of sponsored videos to television programs airing on a particular afternoon. The system includes data stores and a campaign manager system. The campaign manager system will automatically allocate digital media assets to delivery slots in a campaign to generate scheduling files that media servers will use to present the allocated media assets to consumers during the assigned delivery slots via media consumption devices.