In particular, Microsoft has always been great about providing tools for no to low cost at the entry level, to get you (or more likely your company) hooked into the ecosystem. Not making a criticism, they have made some great stuff over the years (see the Visual Studio ecosystem for example).
The other angle is providing these tools, which can be complex to install/configure/manage, as a service offering via a subscription as part of the Azure platform. Recently MS has been hiring every superstar/rockstar evangelist/advocate/architect/engineer/etc to help design/build/promote/advocate for Azure that they can find (See Jessie Frazelle, @catie, and a ton of key people in the Golang world). Microsoft isn't just coming to play, they're playing to win.
In contrast this involves running a proprietary operating system, IDE, closed source, etc... Quite the contrary to anything that could be considered democratic.
Then, ML is all about volume. Open a spreadsheet with more than 10000 rows in Excel and see it squirm in pain.
This is exactly the problem -- what you're describing is not easy for anyone outside of tech to do. If you want to, say, run a simple text classification task and have thousands of labels, this is way overkill. Machine learning has the opportunity to become a common place utility for automating repetitive tasks, and the barrier to entry does not need to be learning Tensorflow, Docker, and Jupyter.