Computer vision models can encounter unexpected data during operation, leading to issues such as data drift, where models may fail to perform as expected with new input data. This can happen, for instance, when a hospital changes its x-ray machine but continues using the same diagnostic model, or when an autonomous vehicle trained on European roads is deployed in an American city. To address these issues, it is crucial to implement out-of-distribution detection systems that identify suspicious or unknown inputs and allow the system to fail gracefully, thereby involving human intervention when necessary. This approach helps mitigate operational bias and prevent silent failures by keeping the data and models up-to-date and ensuring that mitigation strategies are in place. Out-of-distribution detection is a significant component of many learning systems, such as Generative Adversarial Networks, which use a discriminator network to identify suspicious images. Maintaining awareness of data drift and updating models is essential for the ongoing lifecycle of AI systems.