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

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

Options:

Correct Answer: Changes in the underlying data distribution

Solution:

Model drift refers to changes in the underlying data distribution that can lead to a decrease in the model\'s performance over time.

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

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.

Model Drift – Model drift refers to changes in the underlying data distribution that can lead to a decrease in the model's performance over time.