An optimal flow for model evaluation and retraining

Hi everyone,

I am working on a model flow and looking to add in some automated evaluation and retraining. I've built a functional flow,. but I feel like it could be slicker, does anyone have any tips on how I could improve it?

My flow looks like this:

Steps are as follows:

1. Read training data in from Google BigQuery (Training).
2. Send this data to Google Cloud Storage (because connecting the BigQuery dataset directly for modelling is painfully slow!).
3. Split the training data so I have a holdout set for later evaluation.
4. Train my model on the training data.
5. Evaluate my model on the holdout dataset, from my earlier split.
6. Run checks on the model evaluation AUC and kick off a model retrain if below a certain threshold.
7. Read in my prediction data (as above), and generate predictions.

I want to predict every day, but I don't want to train my model every day.

In this current flow I need to load in all my Training data in order to split it out and evaluate my model, is this normal? In this way, if I wanted to evaluate my model every day I would have to load in and split my full training dataset, again, is this normal?

Thanks for your help,

Ben