Before deploying a machine learning model in the wild, it can be important to ensure that it properly integrates with the infrastructure built around it. After evaluating the model in isolation (equivalent to unit testing), trying various probable use cases can allow you to detect issues in the pipeline you developed to put your model in production.
These integration tests can be conducted by you, a member of your team or even by the organization of a private beta release
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