A machine learning model can achieve 95% accuracy during development and still perform terribly after deployment. This is one of the frustrating realities of machine learning. You train the model, evaluate it on your test set, see impressive results, and think the hard part is over.
Source: [Dev.to](https://dev.to/opaul/why-your-machine-learning-model-performs-well-but-fails-in-production-3ac1)