Of all the top Kaggle competition solutions I've looked at, very few could not be done on a laptop. I can think of only one exception and he was using one compu

Of all the top Kaggle competition solutions I've looked at, very few could not be done on a laptop. I can think of only one exception and he was using one computer with 100GB+ RAM. Doubling your data will likely only get you maybe 10% more accurate than the last increment. This seems like a lot until you realize that you went from a RMSE of 0.87567 to 0.87566. This doesn't make any material difference to your bottom line other than it takes more compute resources and more complexity in terms of coding. In this sense it may be actually costing you more to maintain a larger model and also the results may be less clear. I would encourage ML practitioners to try the following exercise: half a problem's data and re-train your models. I think you'll find that your training will typically be much faster and the difference in the performance will be minimal.