Model validation and drift

"Drift" is a term used in machine learning to describe how the performance of a machine learning model in production slowly gets worse over time. This can happen for a number of reasons, such as changes in the distribution of the input data over time or the relationship between the input (x) and the desired target (y) changing

Resources

• Understanding Data Drift and Model Drift: Drift Detection in Python
• Machine Learning Model Drift

Code

• #CODE Evidently - Evaluate and monitor ML models from validation to production
  • https://docs.evidentlyai.com/
• #CODE Frouros - Open-source Python library for drift detection in machine learning systems
  • https://frouros.readthedocs.io/en/latest/
• #CODE Alibi-detect - Algorithms for outlier, adversarial and drift detection

References

• #PAPER A survey on concept drift adaptation (Gama 2014)