These are problems of third parties, not Nvidia.

Every game that runs on Linux gets full graphics acceleration.

As for Windows, PyTorch AMP allows for mixed precision training on native windows through conda, no apex needed. And of course, the same APIs from Nvidia are available on both.

You could always dual boot.

The lifecycle diagram is legit.

As for the vendors, Nvidia is just promoting their own services or their partners.

But there aren't any established players yet. It's still a nascent field.

It's a bit of both. Well, it's definitely an advertising piece but parts of it are somewhat accurate about MLOps.

The purpose of that step is exactly the opposite: to use data that represents (as much as possible) the full generality of the space you seek to model. And on a more practical level, there are datasets that can't be used for commercial purposes but are useful for research (a well known but not the best example would be ImageNet).

"Data Fixes" might also correct for bias. Data often contains errors and inconsistencies, especially if it's the output of some other automation.

"Select the right data" just means don't try to do something silly like predict 2021 housing prices in Ann Arbor using historical data for Pittsburgh from 1980-2007.