A successful machine learning system should be able to gauge its performance by testing different scenarios. This can lead to more innovations in the model design. For an ML system, “success” can be measured in numerous ways. Let’s take an example of an advertising platform that uses a machine-learning algorithm to display relevant ads to the user. The success of this system can be measured using the users’ engagement rate with the advertisement and the overall revenue generated by the system. Similarly, a search ranking system might take into account correctly ranked search results on SERP as a metric to claim to be a successful search engine. Let’s assume that the first version of the system (v0.1) has been created and deployed.