Ah, I know this one! Splitting the data is part of the data preparation stage in machine learning. It's important to have a separate test set to evaluate the model's performance.
I've got a strategy in mind. I think the Cloud Function approach (option D) might be the best way to handle the preprocessing in a scalable and efficient way.
Okay, I've got this. The key is to balance the need for redundancy and failover with the cost of running multiple instances. I think a hybrid approach with one on-premises instance and one cloud instance could be a good compromise.
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