What is data drift and why is it important?
Data drift refers to the change in the statistical properties of a model's input data over time, which can affect its performance.
How can I detect data drift in my models?
You can detect data drift using techniques like monitoring model performance, statistical tests, and visualizations of input data distributions.
What are the consequences of ignoring data drift?
Ignoring data drift can lead to decreased model accuracy, poor decision-making, and ultimately, business losses.
How often should I check for data drift?
It's recommended to check for data drift regularly, especially after significant changes in data sources or model updates.
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