How is drift detection implemented?
Drift detection is implemented by continuously comparing current input data and model predictions against the data from training. The goal is to identify significant changes in distributions, value ranges, or patterns.
Typically, statistical tests or thresholds are used to detect and flag deviations automatically. When a relevant change is identified, retraining can be triggered or a deeper analysis initiated. This prevents the model from silently losing quality over time.
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