The DeepMind datacenter project was very interesting, but a lot of the ML people I spoke to were quite dubious about how much of it was genuinely down to new AI/neural networks, and how much of it was Google PR to justify how much they spent on DeepMind. > DeepMind trained a neural network to more accurately predict future cooling requirements, in turn reducing the power usage of the cooling system by 40 percent. But…
So yes, many applications of ML are something "a regular data science team could do." However, isn't the main benefit of ML removing the need for a team of data scientists? Of course you will need some data scientists / ML engineers to support the ML product itself, but ultimately the gains will be realized from the ML doing the work that traditionally required a team of data scientists.