Tensorflow data validation offers the capability of checking data skew and drift and the documentation also mention that "Setting the correct distance is typically an iterative process requiring domain knowledge and experimentation" How can one specify an initial reasonable jensen_shannon_divergence threshold (or infinity_norm one for categorical features)? Is there some python package/utility/code that one can leverage on a given data set feature to compute a reasonable threshold? If not, what is recommended to proper conduct the experiment and find the …

For a give set of audio files collected from an industrial process via a microphone, I have extracted suitable features and fed them into a neural network for training a binary classifier as depicted below. The model has been performing quite well on an unseen data. I am at the stage of developing a sub-product to monitor data drift forecasting the inevitable i.e. data changes (namely microphone position changes, product materials changes and produces a distinct signal, background noise prevail …