Adaptable filtering for edge-based deep learning models

Techniques for utilizing adaptable filters for edge-based deep learning models are described. Filters may be utilized by an edge electronic device to filter elements of an input data stream so that only a subset of the elements are used as inputs to a machine learning model run by the electronic device, enabling successful operation despite the input data stream potentially being generated at a higher rate than a rate in which the ML model can be executed. The filter can be a differential-type filter that generates difference representations between consecutive elements of the data stream to determine which elements are to be passed on for the ML model, a "smart" filter such as a neural network trained using outputs from the ML model allowing the filter to "learn" which elements are the most likely to be of value to be passed on, or a combination of both.