3 Multi-armed bandits: Maximizing business metrics while experimenting
This chapter covers
- Defining the multi-armed bandit (MAB) problem
- Modifying A/B testing’s randomization procedure
- Extending epsilon-greedy to simultaneously evaluate multiple system changes
- Evaluating system changes even more quickly with Thompson sampling
In the previous chapter, we learned how to use A/B testing to evaluate changes to the system your engineering team is building. Once the tooling is in place to run A/B tests, the team should see a steady increase in the quality of the system as new changes follow the engineering workflow: implement a change candidate, evaluate it offline, and evaluate it online with an A/B test.
As the use of A/B testing increases, you’ll ...
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