Setup: I have a couple of binary classification models based on Logistic Regression and Gradient Boosted Trees. Currently I train the model offline and use it to predict the class of incoming data.
Problem: One of my features is order_count i.e. number of purchases made by the user. So if the order_count of a user changes (i.e. he/she purchases new products) it will not be reflected in my classification model until I retrain it. There are 1000s of such dynamically changing features.
Current solution: One option is to retrain the model say every day or every week with the updated dataset.
Question: Is there any alternative to this method of retraining at regular intervals? Is there a way to update the model with the modified training samples in real time? For example, the moment a user purchases a new product, the model should re-train itself based on this updated data in real time. Or is there some completely different approach that people use in such a situation?