Data Drift
Data drift occurs when the statistical properties of the incoming data to an AI model change over time in ways that degrade model performance. This can happen due to evolving user behavior, changes in sensor calibration, or shifts in demographics. Unlike concept drift, where the relationship between inputs and outputs changes, data drift specifically refers to the input data's characteristics themselves changing, making previously learned patterns less relevant.
Imagine a weather prediction model trained only on summer data suddenly trying to predict winter storms; the input weather patterns have changed significantly.
Understanding and detecting data drift is crucial for maintaining the long-term accuracy and effectiveness of deployed AI models in dynamic environments.
Learn one new AI thing every day.
Daily Deck sends you seven plain-English cards like this every morning. Free.
Start free