Model Retraining and accuracy

Question

Hi,
My team needs to deal with a scenario where we will get data samples (training data) almost every day and we need to train a model using these data samples
We will be storing these data samples in some storage.
Once we reach a specific number of these , we will train an ML.net Model from these.

Now, these data samples will keep coming , so we may need retrain the model again to accommodate the incremental data samples

Questions regarding this:

1. After we train the ML.Net model first time, is it ok to discard the original data samples that we had?
   (we will save on space and since data will keep coming continuously we cannot keep it forever)
   Can we assume that the ML.Net model has all the required information if we have to retrain it.
   2. When we retrain the existing ML.Net model , is there a risk of losing accuracy?

Thanks