Drift can indicate both normal behavior and potential bugs within machine learning systems, as real-world changes impact data features. Factors such as shifting consumer preferences, competitive dynamics, and unpredictable elements like weather can all contribute to this evolution. It's crucial to adapt data strategies to reflect these changes, ensuring that models remain relevant and effective.
