What is a common method for monitoring a deployed machine learning model?

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Question: What is a common method for monitoring a deployed machine learning model?

Options:

Correct Answer: A/B testing

Solution:

A/B testing is commonly used to compare the performance of the deployed model against a baseline or alternative model in real-time.

What is a common method for monitoring a deployed machine learning model?

What is a common method for monitoring a deployed machine learning model?

Step 1: Understand that A/B testing is a method used to compare two versions of something.
Step 2: In the context of machine learning, one version is the deployed model (Model A) and the other is a baseline or alternative model (Model B).
Step 3: Randomly split your users or data into two groups: one group uses Model A and the other group uses Model B.
Step 4: Collect data on how each model performs based on specific metrics (like accuracy, response time, etc.).
Step 5: Analyze the results to see which model performs better in real-time.
Step 6: Use the insights gained from the A/B testing to make decisions about which model to keep or improve.

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