How to Detect Data Drift with Hypothesis Testing

Data drift is a concern to anyone with a machine learning model serving live predictions. The world changes, and as the consumers’ tastes or demographics shift, the model starts receiving feature values different from what it has seen in training, which may result in unexpected outputs. Detecting feature drift appears to be simple: we just need to decide whether the training and serving distributions of the feature in question are the same or not. There are statistical tests for this, right? Well, there are, but are you sure you are using them correctly? <a href="https://towardsdatascience.com/how-to-detect-data-drift-with-hypothesis-testing-1a3be3f8e625">Click Here</a>