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 …

Assume I have a model which predicts the outcome of the number of icecreams sold in a store. The model is trained on data for the last 5 years while keeping the last year as a validation set and has produced very good results. We now put the model into production such that the CFO can create an estimate for the upcoming year's budget. The CFO now look at the prediction for May, say 2000 ice creams, and thinks "Ooh... …

I'm trying to predict a continuous target in an industrial context. The problem I'm facing is that the some of the predictors have changed over time, for example the pressure in the machine was increased. This influenced some of the other predictors, but hasn't influenced my target. As an example (in R formula notation): $ Y \sim U_1$ The target depends on some unobservable variable $ X_j ~ \sim U_1 + X_i$ One of my observed variables depends on the …

I have been reading up on detecting data drift and concept drift, I have found this library but it seems all the methods here detect concept drift and take input as if the prediction was correct or not. (Requiring ground truth) is this the correct assumption? Then I stumbled on Kullback-Leibler Divergence and JS-Divergence. Can I use these methods to detect data drift in real time? (ex: request comes into my models API and then the prediction is made. I …