  Data pipelines are an essential component for end-to-end solutions that take
machine learning algorithms to production. Engineering data pipelines for
video-sequences poses several challenges including isolation of key-frames from
video sequences that are high quality and represent significant variations in
the scene. Manual isolation of such quality key-frames can take hours of
sifting through hours worth of video data. In this work, we present a data
pipeline framework that can automate this process of manual frame sifting in
video sequences by controlling the fraction of frames that can be removed based
on image quality and content type. Additionally, the frames that are retained
can be automatically tagged per sequence, thereby simplifying the process of
automated data retrieval for future ML model deployments. We analyze the
performance of the proposed video-data pipeline for versioned deployment and
monitoring for object detection algorithms that are trained on outdoor
autonomous driving video sequences. The proposed video-data pipeline can retain
anywhere between 0.1-20% of the all input frames that are representative of
high image quality and high variations in content. This frame selection,
automated scene tagging followed by model verification can be completed in
under 30 seconds for 22 video-sequences under analysis in this work. Thus, the
proposed framework can be scaled to additional video-sequence data sets for
automating ML versioned deployments.
