Can we prevent concept and data drift?

Data & Data Governance Category
Input PhaseModel PhaseOutput PhaseDeploy PhaseMonitor Phase
Can we prevent concept and data drift?
  • Data drift weakens performance because the model receives data on which it hasn’t been trained. It causes changes in the statistical properties of the input data distribution (e.g., feature distributions shift over time).
  • With Concept drift, the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways causing accuracy issues. It causes changes in the relationship between input features and the target variable (e.g., customer behavior changes over time, impacting a predictive model).

If you answered No then you are at risk

If you are not sure, then you might be at risk too

Recommendations

  • Implement robust monitoring tools to detect data and concept drift, and establish governance policies for regular data validation and model retraining.
  • Select an appropriate drift detection algorithm and apply it separately to labels, model’s predictions and data features.