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Maintaining ML models is like doing laundry -- it never ends! @juliasilge with the harsh realities of data science life.
ML has both software AND statistical properties. Failures in statistical performance can be *silent*.
Data drift (inputs changing) and concept drift (feature vs outcome relationships changing) are sneaky sources of changes over time.
Watch out - deploying a model may cause drift via feedback loops!
The slides are so good: https://juliasilge.github.io/ml-maintenance-2023/#/title-slide
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