4 ms·
On the other hand, if ML gives you a 0.5% disadvantage, that too is a non-trival amount of losses on a national scale. Because they compared against baseline ra
by fao_ 10y ago
On the other hand, if ML gives you a 0.5% disadvantage, that too is a non-trival amount of losses on a national scale. Because they compared against baseline rather than against another data science team (or both), it's difficult to know if ML actually brings an advantage here.
- phkahler 10y agoCan't we assume they already have a data science team? Optimizing the grid is their job already right? From what I've seen in several places though is companies tend not to listen to their own people, but are willing to make changes based on the advice of hired "experts". So the experts will come in and ask everyone what the challenges are and what they might do to improve. Then the "experts" will pick several low hanging fruit and find a few things that cross internal boundaries (the harder things to change) and make recommendations. Upper management will go with it and improvements will be made. I suppose the way to sum it up is that they bring objectivity to the table.