[D] How do you deal with covariate shift and concept drift in production?

• river

  17 4,754 9.2 Python

  🌊 Online machine learning in Python

  

  Have a look at here: https://github.com/online-ml/river

• alibi-detect

  9 2,074 7.6 Python

  Algorithms for outlier, adversarial and drift detection

  

  I work in this area and also contribute to outlier/drift detection library https://github.com/SeldonIO/alibi-detect. To tackle this type of problem, I would strongly encourage following a more principled, fundamentally (statistically) sound approach. So for instance measuring metrics such as the KL-divergence (or many other f-divergences) will not be that informative since it has a lot of undesirable properties for the problem at hand (in order to be informative requires already overlapping distributions P and Q, it is asymmetric, not a real distance metric, will not scale well with data dimensionality etc). So you should probably look at Integral Probability Metrics (IPMs) such as the Maximum Mean Discrepancy (MMD) instead which have much nicer behaviour to monitor drift. I highly recommend the Interpretable Comparison of Distributions and Models NeurIPS workshop talks for more in-depth background.

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