Yes, I think as long as you shuffle the data and stick to your workflow, the final results should be comparable, i.e. the model should converge to the same final accuracy (+/- a small difference).
You might have some use cases where you need to restore exactly the same data ordering etc., which is more tricky as explained before. Usually these steps are necessary to debug some issues and your “standard” training shouldn’t depend on a specific seed or ordering of the shuffled data.