A/B testing has long been the cornerstone of experimentation in the software and machine learning domains. By comparing two versions of a webpage, application, feature, or algorithm, businesses can determine which version performs better based on predefined metrics of interest. However, as the complexity of business problems or experimentation grows, A/B testing can be a constraint in empirically evaluating successful development. Multi-armed bandits (MAB) is a powerful alternative that can scale complex experimentation in enterprises by dynamically balancing exploration and exploitation.

The Limitations of A/B Testing

While A/B testing is effective for simple experiments, it has several limitations: