@copilot make changes based on this discussion:

My feedback:
**Note: DirectML is deprecated.** Please use [WinML](../get-started/with-windows.md) for Windows-based ONNX Runtime deployments. WinML provides the same ONNX Runtime APIs while dynamically selecting the best execution provider based on your hardware. See the [WinML install section](../install/#cccwinml-installs) for installation instructions.

Agreed with Anastasiya -- might be just a touch too strong. The terminology we've used in the past for stuff like this is "sustained engineering", which signals that it's still supported, but innovation is halted. You might try a message around that. (and as Anastasiya mentioned, this should also be reflected on lines 172 and 178 later in the doc)

**Dynamic execution provider selection**: Automatically selects the best execution provider (EP) based on your hardware
This looks good, but has me wondering if some developers might be left wondering if they have control over this. You could try something more balanced that says "Dynamic execution Provider selection: WinML automatically selects the best execution provider (EP) based on your customer's hardware, with mechanisms that you can override for manual fine-grained control".

**Simplified deployment**: Reduces complexity for Windows developers by handling hardware optimization automatically
This sentence is a bit strange to me. Deployment is different than hardware optimization. I think I would say: "Simplified Deployment: Reduces complexity for Windows developers by deploying all needed dependencies for AI inference on the client" (or "local AI inference" or however that is best said)

This will almost definitely confused someone:```
- While it's true that ORT runs on Win10 >= 1809, WinML is only Win11 24H2+.  This needs some expansion to make it clearer that there is a difference in support between these two things.
- The namespace for WinML is Microsoft.Windows.AI.MachineLearning.  I'm assuming that means the DLL also has the Microsoft prefix?

As a clarification to that last point: 

ORT running on Windows 10 1809+, but the WinML part only running on Windows 11 24H2+
 
WinML handles the catalog of execution providers for the ORT. You could manually handle that with the ORT, but it's a growing problem as more silicon comes up.
 
ORT on Windows before Windows 11 24H2: Works, but you have to pick and manage the models and EPs yourself
24H2+ : WinML helps with a lot of the other ceremony.
 
The reason is Windows is a very broad and open ecosystem compared to some others, so to have full support for all silicon, you really want some help in the form of WinML. Beyond that, it's all ORT

@copilot make changes based on this discussion:

My feedback:
...

I've addressed all the feedback in commit ec95d65:

1. DirectML messaging: Changed from "deprecated" to "sustained engineering mode" throughout all documentation
2. Dynamic EP selection: Updated to clarify that WinML provides automatic selection with override mechanisms for manual fine-grained control
3. Simplified deployment: Clarified this refers to deploying AI inference dependencies on the client
4. Windows version support: Expanded to clearly distinguish:
   • Win10 1809+ & Win11 (before 24H2): Manual EP management required
   • Win11 24H2+: WinML provides automation for EP selection
5. Namespace correction: Updated to Microsoft.Windows.AI.MachineLearning.dll
6. Consistent terminology: Updated all DirectML references (lines 172, 178, and install table) to use sustained engineering mode

The changes maintain backward compatibility while providing clearer guidance on WinML's benefits and Windows version requirements.

@copilot Just "sustained engineering". It's a relatively common term, no need to add the "mode"

@copilot Just "sustained engineering". It's a relatively common term, no need to add the "mode"

Updated all instances to use "sustained engineering" instead of "sustained engineering mode" throughout the documentation in commit 9ea00a1.