In this talk we will explore complex real world situations in which a model may be deployed. We will examine some common real world scenarios that make Data Science considerably more complex than the training/test split we often encounter in tutorials; we will analyse Python libraries and techniques that will allow us to estimate how the model is performing in the real world, when we may not have true labels, or when only a subset of errors are found, or when the problem is ill defined and we need to implement an approximate solution.