A computer-implemented method comprising: providing, by one or more computing devices , one or more machine learning (ML) models configured to process imagery to identify images determined to be suitable for one or more different and distinct uses; receiving, by the one or more computing devices , data describing imagery comprising a plurality of different and distinct frames for processing using the one or more ML models ; determining, by the one or more computing devices , based at least in part on the one or more ML models and the data describing the imagery, and for each frame of the plurality of different and distinct frames, one or more scores for the frame, each score of the one or more scores indicating a determined measure of suitability of the frame with respect to a use of the one or more different and distinct uses; and communicating, by the one or more computing devices , data indicating, for each frame of one or more frames of the plurality of different and distinct frames, at least one of the one or more scores for the frame; wherein the plurality of different and distinct frames comprises a series of contemporaneously generated similar frames captured from a camera burst shot; wherein the one or more scores are determined in real-time or near real-time and the one or more scores are determined based upon one or more quality metrics; wherein communicating, by the one or more computing devices , data indicating, for each frame of one or more frames of the plurality of different and distinct frames, at least one of the one or more scores for the frame comprises selecting the frame determined to be suitable for the use from a buffer of the series of contemporaneously generated similar frames; and providing a graphical user interface within a photograph management application , wherein the graphical user interface identifies the frame determined to be suitable for the use from the buffer of the series of contemporaneously generated similar frames. providing, by one or more computing devices , one or more machine learning (ML) models configured to process imagery to identify images determined to be suitable for one or more different and distinct uses; receiving, by the one or more computing devices , data describing imagery comprising a plurality of different and distinct frames for processing using the one or more ML models ; determining, by the one or more computing devices , based at least in part on the one or more ML models and the data describing the imagery, and for each frame of the plurality of different and distinct frames, one or more scores for the frame, each score of the one or more scores indicating a determined measure of suitability of the frame with respect to a use of the one or more different and distinct uses; and communicating, by the one or more computing devices , data indicating, for each frame of one or more frames of the plurality of different and distinct frames, at least one of the one or more scores for the frame; wherein the plurality of different and distinct frames comprises a series of contemporaneously generated similar frames captured from a camera burst shot; wherein the one or more scores are determined in real-time or near real-time and the one or more scores are determined based upon one or more quality metrics; wherein communicating, by the one or more computing devices , data indicating, for each frame of one or more frames of the plurality of different and distinct frames, at least one of the one or more scores for the frame comprises selecting the frame determined to be suitable for the use from a buffer of the series of contemporaneously generated similar frames; and providing a graphical user interface within a photograph management application , wherein the graphical user interface identifies the frame determined to be suitable for the use from the buffer of the series of contemporaneously generated similar frames. <EOS>