Data drift

Data drift is when live inputs start to look different from the training data, so the model's accuracy can quietly drop.

• Compare live feature statistics (mean, spread, categories) with training.
• Concept drift: the link between inputs and the answer itself changes.
• Alert on big shifts, then investigate and retrain if needed.

import numpy as np
train, live = np.array([30, 32, 28]), np.array([48, 52, 50])
print(round(float(live.mean() - train.mean()), 1), bool(abs(live.mean() - train.mean()) > 2 * train.std()))

20.0 True

Remember: Models age. Plan to monitor and retrain.

Common mistake: Deploying once and assuming the model stays accurate forever.