Update deploy.md

Author: pareenaverma

content/learning-paths/servers-and-cloud-computing/onnx-on-azure/deploy.md

@@ -8,7 +8,7 @@ layout: learningpathall
 
 
 ## ONNX Installation on Azure Ubuntu Pro 24.04 LTS
-Install Python, create a virtual environment, and use pip to install ONNX, ONNX Runtime, and dependencies. Verify the setup and validate a sample ONNX model like SqueezeNet.
+To work with ONNX models on Azure, you will need a clean Python environment with the required packages. The following steps install Python, set up a virtual environment, and prepare for ONNX model execution using ONNX Runtime.
 
 ### Install Python and Virtual Environment:
 
@@ -26,41 +26,46 @@ source onnx-env/bin/activate
 
 ### Install ONNX and Required Libraries:
 
+Upgrade pip and install ONNX with its runtime and supporting libraries:
 ```console
 pip install --upgrade pip
 pip install onnx onnxruntime fastapi uvicorn numpy
 ```
 This installs ONNX libraries along with FastAPI (web serving) and NumPy (for input tensor generation).
 
 ### Validate ONNX and ONNX Runtime:
-Create **version.py** as below:
+Once the libraries are installed, you should verify that both ONNX and ONNX Runtime are correctly set up on your VM.
 
+Create a file named `version.py` with the following code:
 ```python
 import onnx  
 import onnxruntime 
 
 print("ONNX version:", onnx.__version__)  
 print("ONNX Runtime version:", onnxruntime.__version__)  
 ```
-Now, run version.py: 
+Run the script: 
 
 ```console
 python3 version.py
 ```
-You should see an output similar to:
+You should see output similar to:
 ```output
 ONNX version: 1.19.0
 ONNX Runtime version: 1.23.0
 ```
-### Download and Validate ONNX Model - SqueezeNet:
-SqueezeNet is a lightweight convolutional neural network (CNN) architecture designed to achieve comparable accuracy to AlexNet, but with fewer parameters and smaller model size. 
+With this validation, you have confirmed that ONNX and ONNX Runtime are installed and ready on your Azure Cobalt 100 VM. This is the foundation for running inference workloads and serving ONNX models.
 
+### Download and Validate ONNX Model - SqueezeNet:
+SqueezeNet is a lightweight convolutional neural network (CNN) architecture designed to provide accuracy close to AlexNet while using 50x fewer parameters and a much smaller model size. This makes it well-suited for benchmarking ONNX Runtime. 
+Download the quantized model:
 ```console
 wget https://github.com/onnx/models/raw/main/validated/vision/classification/squeezenet/model/squeezenet1.0-12-int8.onnx -O squeezenet-int8.onnx
 ```
 #### Validate the model: 
 
-Create a **vaildation.py** file with the code below for validation for ONNX model:
+After downloading the SqueezeNet ONNX model, the next step is to confirm that it is structurally valid and compliant with the ONNX specification. ONNX provides a built-in checker utility that verifies the graph, operators, and metadata.
+Create a file named `validation.py` with the following code:
 
 ```python
 import onnx
@@ -69,10 +74,16 @@ model = onnx.load("squeezenet-int8.onnx")
 onnx.checker.check_model(model)
 print("✅ Model is valid!")
 ```
-You should see an output similar to:
+Run the script:
+
+```bash
+python3 validation.py
+```
+
+You should see output similar to:
 ```output
 ✅ Model is valid!
 ```
-This downloads a quantized (INT8) classification model, and validates its structure using ONNX’s built-in checker. 
+With this validation, you have confirmed that the quantized SqueezeNet model is valid and ONNX-compliant. The next step is to run inference with ONNX Runtime and to benchmark performance.
 
 ONNX installation and model validation are complete. You can now proceed with the baseline testing.