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In the context of model deployment, what does 'model drift' refer to?

Practice Questions

Q1
In the context of model deployment, what does 'model drift' refer to?
  1. Changes in the model architecture
  2. Changes in the underlying data distribution
  3. Changes in the model's hyperparameters
  4. Changes in the deployment environment

Questions & Step-by-Step Solutions

In the context of model deployment, what does 'model drift' refer to?
  • Step 1: Understand that a model is a tool that makes predictions based on data.
  • Step 2: Know that the model was trained on a specific set of data to learn patterns.
  • Step 3: Realize that over time, the data the model sees in the real world can change.
  • Step 4: Recognize that these changes in data are called 'model drift'.
  • Step 5: Understand that model drift can cause the model to make less accurate predictions.
  • Step 6: Conclude that monitoring and updating the model is important to maintain its performance.
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