Google Professional Machine Learning Engineer Question 315
Select 4Google Cloud PlatformYou are deploying a machine learning model to production on Google Cloud and want to evaluate the performance of two model versions (Version A and Version B) using A/B testing. The goal is to identify which version performs better on key business metrics, such as customer conversion rates. Which of the following steps should you take to properly conduct the A/B test?
- A
Split incoming user traffic between Version A and Version B randomly to ensure unbiased comparisons.
- B
Use Cloud Monitoring to log predictions and business metric data for both model versions.
- C
Deploy both models to a Vertex AI endpoint and use traffic splitting to route a fixed percentage of requests to each model.
- D
Analyze the results of the A/B test after a few requests to determine which model is better.
- E
Ensure that the evaluation metric aligns with your business goal, such as using conversion rates for an e-commerce platform.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
A/B testing involves comparing two model versions in a controlled manner to evaluate their performance on specific metrics. In this scenario, splitting traffic randomly, using Cloud Monitoring for logging and analysis, deploying models effectively using Vertex AI traffic splitting, and ensuring the metric aligns with business goals are crucial steps. Avoid drawing conclusions prematurely, as statistical significance requires sufficient data.
- A. Correct.
Correct. Randomly splitting user traffic ensures that the comparison between the two versions is unbiased and that external factors affecting performance are evenly distributed.
- B. Correct.
Correct. Cloud Monitoring allows you to collect and analyze performance metrics and logs for both model versions, which is crucial for assessing their impact on business metrics.
- C. Correct.
Correct. Vertex AI endpoints support traffic splitting, which is ideal for deploying multiple model versions and directing specific portions of traffic to each model for A/B testing.
- D. Incorrect.
Incorrect. Analyzing results after only a few requests can lead to unreliable conclusions. Sufficient traffic and statistical significance are necessary to determine which model is better.
- E. Correct.
Correct. Aligning the evaluation metric with the business goal ensures that the A/B test evaluates the models based on their real-world impact on the business.