Abstract: A content recommendation system includes a computing platform having a hardware processor and a system memory storing a content surfacing software code providing a user interface. The hardware processor executes the content surfacing software code to receive an initiation signal identifying a user, and, in response, identify content items as desirable content items to the user based on the user and metadata describing each of the desirable content items. In addition, the content surfacing software code generates a networked map of nodes corresponding respectively to the desirable content items, where the distances between nodes are based on the similarity of the metadata describing the desirable content items corresponding to the nodes. The content surfacing software code further outputs the networked map for display to the user via the user interface, the nodes being displayed as thumbnail images depicting the desirable content and selectable by the user.

Abstract: In various embodiments, a broker application automatically allocates tasks to application programming interfaces (APIs) in microservice architectures. After receiving a task from a client application, the broker application performs operation(s) on content associated with the task to compute predicted performance data for multiple APIs. The broker application then determines that a first API included in the APIs should process the first task based on the predicted performance data. The broker application transmits an API request associated with the first task to the first API for processing. After receiving a result associated with the first task from the first API, the client application performs operation(s) based on the result.. Advantageously, because the broker application automatically allocates the first task to the first API based on the content, time and resource inefficiencies are reduced compared to prior art approaches that indiscriminately allocate tasks to APIs.

Abstract: An embodiment provides a monopod jib for cameras, including: a pole; a multi-axis gimbal disposed at one end of the pole; and a user interface comprising: a first user interface element having a first plurality of camera controls; and a second user interface element having a second plurality of camera controls. Other embodiments are shown and described.

Abstract: Systems, methods, and articles of manufacture to perform an operation comprising processing, by a machine learning (ML) algorithm and a ML model, a plurality of images in a first dataset, wherein the ML model was generated based on a plurality of images in a training dataset, receiving user input reviewing a respective set of tags applied to each image in the first data set as a result of the processing, identifying, based on a first confusion matrix generated based on the user input and the sets of tags applied to the images in the first data set, a first labeling error in the training dataset, determining a type of the first labeling error based on a second confusion matrix, and modifying the training dataset based on the determined type of the first labeling error.

Abstract: According to one implementation, a content classification system includes a computing platform having a hardware processor and a system memory storing a video asset classification software code. The hardware processor executes the video asset classification software code to receive video clips depicting video assets and each including images and annotation metadata, and to preliminarily classify the images with one or more of the video assets to produce image clusters. The hardware processor further executes the video asset classification software code to identify key features data corresponding respectively to each image cluster, to segregate the image clusters into image super-clusters based on the key feature data, and to uniquely identify each of at least some of the image super-clusters with one of the video assets.

Abstract: According to one implementation, a media content annotation system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first version of media content and a second version of the media content altered with respect to the first version, and to map each of multiple segments of the first version of the media content to a corresponding one segment of the second version of the media content. The software code further aligns each of the segments of the first version of the media content with its corresponding one segment of the second version of the media content, and utilizes metadata associated with each of at least some of the segments of the first version of the media content to annotate its corresponding one segment of the second version of the media content.

Abstract: A media content tagging system includes a computing platform having a hardware processor, and a system memory storing a tag selector software code configured to receive media content having segments, each segment including multiple content elements each associated with metadata tags having respective pre-computed confidence scores. For each content element, the tag selector software code assigns each of the metadata tags to at least one tag group, determines a confidence score for each tag group based on the pre-computed confidence scores of its assigned metadata tags, discards tag groups having less than a minimum number of assigned metadata tags, and filters the reduced number of tag groups based on the second confidence score to identify a further reduced number of tag groups. The tag selector software code then selects at least one representative tag group for a segment from among the further reduced number of tag groups.

Abstract: Systems, methods, and articles of manufacture to perform an operation comprising processing, by a machine learning (ML) algorithm and a ML model, a plurality of images in a first dataset, wherein the ML model was generated based on a plurality of images in a training dataset, receiving user input reviewing a respective set of tags applied to each image in the first data set as a result of the processing, identifying, based on a first confusion matrix generated based on the user input and the sets of tags applied to the images in the first data set, a first labeling error in the training dataset, determining a type of the first labeling error based on a second confusion matrix, and modifying the training dataset based on the determined type of the first labeling error.

Abstract: According to one implementation, a system for performing graph-based media content evaluation includes a computing platform having a hardware processor, and a system memory storing a media content evaluation software code and a graph database. The hardware processor is configured to execute the media content evaluation software code to receive a query from a system user, and to identify one or more media content evaluation metrics corresponding to the query. In addition, the hardware processor is configured to execute the media content evaluation software code to search the graph database for a media content data relevant to the one or more media content evaluation metrics, and to retrieve the media content data from the graph database. The hardware processor is further configured to execute the media content evaluation software code to generate a report responsive to the query using the media content data.

Abstract: According to one implementation, a system for programmatic generation of media content digests includes a computing platform having a hardware processor and a system memory storing a media content digest software code. The hardware processor executes the media content digest software code to identify a media content for use in generating a content digest, the media content including a timecode of the media content, to access a metadata describing the media content and indexed to the timecode, and to identify one or more constraints for the content digest. In addition, the hardware processor executes the media content digest software code to programmatically extract content segments from the media content using the metadata indexed to the timecode and based on the one or more constraints, and to generate the content digest based on the media content from the content segments.

Abstract: According to one implementation, a content classification system includes a computing platform having a hardware processor and a system memory storing a video asset classification software code. The hardware processor executes the video asset classification software code to receive video clips depicting video assets and each including images and annotation metadata, and to preliminarily classify the images with one or more of the video assets to produce image clusters. The hardware processor further executes the video asset classification software code to identify key features data corresponding respectively to each image cluster, to segregate the image clusters into image super-clusters based on the key feature data, and to uniquely identify each of at least some of the image super-clusters with one of the video assets.

Abstract: According to one implementation, a content classification system includes a computing platform having a hardware processor and a system memory storing a video asset classification software code. The hardware processor executes the video asset classification software code to receive video clips depicting video assets and each including images and annotation metadata, and to preliminarily classify the images with one or more of the video assets to produce image clusters. The hardware processor further executes the video asset classification software code to identify key features data corresponding respectively to each image cluster, to segregate the image clusters into image super-clusters based on the key feature data, and to uniquely identify each of at least some of the image super-clusters with one of the video assets.

Abstract: An embodiment provides a monopod jib for cameras, including: a pole; a multi-axis gimbal disposed at one end of the pole; and a user interface comprising: a first user interface element having a first plurality of camera controls; and a second user interface element having a second plurality of camera controls. Other embodiments are shown and described.