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Model Drift
Model drift occurs when the performance of a deployed AI model degrades over time due to changes in the underlying data distribution it encounters in the real world. This can happen if the characteristics of the input data or the relationship between inputs and outputs shift. Monitoring for drift is essential to maintain model accuracy.
In plain terms
It's like a weather forecast model built for summer suddenly trying to predict winter storms without any updates, leading to inaccurate predictions.
Why it matters
Detecting and addressing model drift ensures that AI systems continue to provide accurate and useful insights even as their operational environment evolves.
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