Drift Monitoring is a process used in data science and machine learning to detect and measure changes or "drift" in the statistical properties of data over time. This is crucial because the algorithms used in machine learning models are often built on the assumption that the underlying data distribution is consistent. When the data drifts, it means the distribution or relationship between variables has changed, which can lead to a decrease in model accuracy or effectiveness.
How Data Monitoring works
Drift monitoring works by regularly comparing the statistical properties of incoming data against those of the data that a model was originally trained on. If significant differences are noticed, this suggests that drift has occurred. This can be measured in several ways, but generally involve some form of statistical test or distance measure.
There are two main types of data drift to monitor:
- Concept Drift: This occurs when the relationships between variables change over time. Even if the individual distributions of variables stay the same, a machine learning model might become less accurate if the way those variables interact changes.
- Data Drift: This occurs when the actual values of the data change over time, even if the relationships between variables remain the same.
Monitoring for these drifts can help in identifying when a model might need to be retrained or updated. This is an integral part of maintaining the accuracy and validity of predictive models over time.
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