I'm just curious about real ML projects on production.
I was wondering what is the way to go to retrain your models when you get new data? for example, let's suppose you've built a model with 2023 January data. When February's data comes in, do you have to either retrain the model with full available data (January + February) or just retrain it with new data (February)?
In the first approach, the problem I see is that it wouldn't scale well with the size of the data.
How do you decide that your historical data is no longer useful to feed your model's training?