A/B testing model versions in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is A/B testing in the context of model versions?

A/B testing is a method to compare two versions of a model by running them simultaneously on different user groups to see which performs better.

beginner

Why do we use A/B testing for model versions?

To safely evaluate a new model's performance against the current one without affecting all users, ensuring improvements before full deployment.

beginner

What is a key metric in A/B testing model versions?

A key metric is a measurable value like accuracy, click-through rate, or error rate used to compare model performance.

intermediate

How do you split traffic in A/B testing for model versions?

Traffic is split by directing a percentage of users to each model version, often 50/50 or weighted based on risk tolerance.

intermediate

What is a common risk when running A/B tests on model versions?

The new model might perform worse, affecting user experience for the group exposed to it, so monitoring is essential.

What does A/B testing help you decide in model deployment?

A Which model version performs better in real use

B How to write code faster

C How to reduce model size

D How to train models without data

A/B testing compares model versions to find which performs better with real users.

In A/B testing, how is user traffic usually divided?

A All users to one model

B No users see the new model

C Only new users to new model

D Random split between model versions

Traffic is split randomly or weighted to compare models fairly.

Which metric is NOT typically used in A/B testing model versions?

A Model file size

B Click-through rate

C Accuracy

D Error rate

Model file size is not a performance metric for A/B testing outcomes.

What should you do if the new model performs worse in A/B testing?

A Immediately replace the old model

B Stop the test and investigate

C Ignore results and continue

D Increase traffic to the new model

If performance drops, stop and analyze to avoid harming users.

What is a benefit of A/B testing model versions?

A Train models faster

B Deploy models without any monitoring

C Test models safely with partial user exposure

D Avoid collecting user data

A/B testing allows safe evaluation by exposing only part of users to the new model.

Explain how A/B testing helps in deploying new model versions safely.

Think about how you can test something new without affecting everyone.

Describe the steps to set up an A/B test for two model versions.

Consider what you need before, during, and after the test.