What is the difference between data drift and model drift?
Data drift refers to changes in the input data distribution over time, while model drift occurs when the model's performance degrades due to these changes.
Can you give me an example of data drift?
Sure! An example of data drift is when a retail model trained on historical sales data starts to perform poorly due to changes in consumer behavior.
How can I detect data drift?
You can detect data drift by monitoring statistical properties of the input data, such as mean, variance, and distribution, and comparing them to the training data.
What are some techniques to handle model drift?
Techniques to handle model drift include retraining the model with new data, using ensemble methods, and implementing continuous monitoring.
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