You have developed a deep learning model for a recommendation system. You want to evaluate the performance of the model using A/B testing. What is…

A/B testing is a statistical method used for conducting controlled experiments to compare two versions (A and B) of a single variable. In the context of deep learning models, it involves deploying a new model (version B, the "challenger") alongside the existing model (version A, the "control") in a live production environment. A random subset of users is directed to each version, and key performance indicators (KPIs) such as click-through rates, conversion rates, or user engagement are measured. This controlled comparison allows data scientists to statistically determine if the new model provides a significant performance improvement over the old one before a full rollout.

B. A/B testing is a quantitative, data-driven methodology; it does not formally integrate qualitative designer commentary into its process.

C. While a successful A/B test can indicate robustness, its primary purpose is comparative performance evaluation, not the direct testing or ensuring of robustness.

D. Latency can be one of many metrics collected during an A/B test, but the fundamental rationale is the overall comparative performance, not just latency.

1. Huyen, C. (2022). Designing Machine Learning Systems. O'Reilly Media.

Chapter 9, Section: "A/B Testing": The text explicitly defines A/B testing as a method to "compare two or more variants... to see which one performs better on a specific metric." It emphasizes its role in making data-driven decisions about deploying new models by comparing their real-world performance against a control version.

2. Stanford University. (2021). CS 329S: Machine Learning Systems Design, Lecture 13: MLOps & Tooling.

Slide 21, "Online Evaluation": The lecture material describes A/B testing as a primary method for online evaluation. It states, "A/B testing: show some users the old model and some users the new model and see which one performs better." This directly supports the rationale of controlled comparison to identify the superior version.

3. Google Cloud. (2023). Architecture for MLOps using TFX, Kubeflow Pipelines, and Cloud Build.

Section: "Model deployment": The documentation describes A/B testing as a deployment pattern where you "deploy the new model alongside the previous model" and "route a certain amount of traffic to the new model." The goal is to "compare the performance of the two models" and decide whether to roll out the new model completely, directly aligning with the concept of a controlled comparison.