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What is 'data drift' in the context of deployed models?

Practice Questions

Q1
What is 'data drift' in the context of deployed models?
  1. Changes in the model architecture
  2. Changes in the data distribution over time
  3. Changes in the model's hyperparameters
  4. Changes in the evaluation metrics

Questions & Step-by-Step Solutions

What is 'data drift' in the context of deployed models?
  • Step 1: Understand that models are trained on data to make predictions.
  • Step 2: Realize that the data used for training can change over time.
  • Step 3: Know that when the new data is different from the training data, it is called 'data drift'.
  • Step 4: Recognize that data drift can lead to the model making less accurate predictions.
  • Step 5: Be aware that monitoring for data drift is important to maintain model performance.
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