Windows Admin Center and System Insights.
The AZ‑800 exam gets more into Windows Admin Center holistically,
Here,
we're concerned with a specific capability that we use WAC
to manage and that is a Windows feature,
a Windows server feature called system insights.
You should be aware walking into AZ‑801 that system insights is not
a cloud service, and this comes as a surprise,
I think,
initially because we started this course looking at all the different
Microsoft 365 cloud services that we can leverage,
including their artificial intelligence capabilities,
to help in our hybrid cloud administration,
but no, this one runs entirely on Windows server locally,
and what it offers you is predictive analytics with no cloud dependency.
Now what does that mean?
Well,
the short Cliffs Notes explanation here is that system insights takes
advantage of some artificial intelligence actually.
There are some machine learning models integrated into system insights,
these are called capabilities, that can look at your data historically.
You'll want to have it run for a while before it can
start to make good predictions, but it can give you good predictions.
You're going to be running out of space on your disks pretty soon.
It looks like your CPU is going to need an upgrade.
It looks like your disk maybe reaching end of life.
These are some of the business use cases.
Now depending upon your sample time if you're doing
per second per minute per hour,
you want to think about the footprint that system insights will have.
You can selectively enable or disable capabilities,
and again, you can adjust the data collection aggressiveness.
You get 1 year of data retention here.
You configure it with Windows Admin Center,
like I mentioned before,
and the built‑in capabilities are on CPU and network capacity forecasting,
and then for storage, storage and volume consumption forecasting,
and these capabilities, the framework and API is extensible.
So it's likely over time we'll see more and more providers support on where
you can load and look at different aspects of your system,
and again,
taking advantage of machine learning where it's able to take
new data that's arising from your system,
compare it to the model that it's developed or it's been
trained to develop over time in the past,
and make a prediction on that given subsystem.
That's the value prop. Let's take a look at a demo now.