What is the purpose of A/B testing in MLOps?

What is the purpose of A/B testing in MLOps?

Step 1: Understand that A/B testing is a method used to compare two different versions of something.
Step 2: In MLOps, this 'something' refers to two versions of a machine learning model.
Step 3: Create two versions of the model: Version A and Version B.
Step 4: Deploy both versions in a real-world environment where they can be tested.
Step 5: Split the audience or data so that half of the users see Version A and the other half see Version B.
Step 6: Collect data on how each version performs based on specific metrics (like accuracy, user engagement, etc.).
Step 7: Analyze the results to see which version performed better.
Step 8: Use the findings to decide which model to keep or improve.

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