What you say re saving all data is the ideal. I'd add a couple caveats, one is that in many fields you often get lots of redundant data that adds nothing to tra

What you say re saving all data is the ideal. I'd add a couple caveats, one is that in many fields you often get lots of redundant data that adds nothing to training (for example if an image classifier looking for some rare class you can be drowning in images of the majority class). Or you can just have lots of data that is unambiguously and correctly classified- some kind of active learning can tell you what is worth keeping. The other is that for various reasons the customer doesnt want to share their data (or at least have sharing built into the inference system) so even if you'd like to have everything they record, it's just not available. Obviously something to discourage but it seems common