Improve clarity and consistency in documentation for ONNX Runtime testing and Azure VM creation.

Author: madeline-underwood

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

@@ -36,14 +36,13 @@ python3 baseline.py
 You should see output similar to:
 ```output
 Inference time: 0.0026061534881591797
-```
 {{% notice Note %}}
-Inference time is the amount of time it takes for a trained machine learning model to make a prediction (produce output) after receiving input data.
+Inference time is how long it takes for a trained machine learning model to make a prediction after it receives input data.
 
-The input tensor has the shape `(1, 3, 224, 224)`, which means:
-- 1: batch size (number of images processed at once)
-- 3: color channels (RGB)
-- 224 x 224: image resolution (common for models like SqueezeNet)
+The input tensor shape `(1, 3, 224, 224)` means:
+- `1`: One image is processed at a time (batch size)
+- `3`: Three color channels (red, green, blue)
+- `224 x 224`: Each image is 224 pixels wide and 224 pixels tall (standard for SqueezeNet)
 {{% /notice %}}
 
 This indicates the model successfully executed a single forward pass through the SqueezeNet INT8 ONNX model and returned results.

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

@@ -11,7 +11,7 @@ Now that you have validated ONNX Runtime with Python-based timing (for example,
 
 This approach helps you evaluate ONNX Runtime efficiency on Azure Arm64-based Cobalt 100 instances and compare results with other architectures if needed.
 
-You are ready to run benchmarks — a key skill for optimizing real-world deployments.
+You are ready to run benchmarks, which is a key skill for optimizing real-world deployments.
 
 
 ## Run the performance tests using onnxruntime_perf_test
@@ -35,7 +35,7 @@ You should see output similar to:
 ```output
 libprotoc 3.21.12
 ```
-### Build ONNX Runtime from source
+## Build ONNX Runtime from source
 
 The benchmarking tool `onnxruntime_perf_test` isn’t available as a pre-built binary for any platform, so you will need to build it from source. This process can take up to 40 minutes.
 
@@ -65,12 +65,12 @@ Now that you have built the benchmarking tool, you can run inference benchmarks
 
 Breakdown of the flags:
 
-- `-e cpu`: Use the CPU execution provider.
-- `-r 100`: Run 100 inference passes for statistical reliability.
-- `-m times`: Run in “repeat N times” mode for latency-focused measurement.
-- `-s`: Print summary statistics after the run.
-- `-Z`: Disable memory arena for more consistent timing.
-- `-I ../squeezenet-int8.onnx`: Path to your ONNX model file.
+- `-e cpu`: use the CPU execution provider.
+- `-r 100`: run 100 inference passes for statistical reliability.
+- `-m times`: run in “repeat N times” mode for latency-focused measurement.
+- `-s`: print summary statistics after the run.
+- `-Z`: disable memory arena for more consistent timing.
+- `-I ../squeezenet-int8.onnx`: path to your ONNX model file.
 
 You should see output with latency and throughput statistics. If you encounter build errors, check that you have enough memory (at least 8 GB recommended) and all dependencies are installed. For missing dependencies, review the installation steps above.
 

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

@@ -39,15 +39,15 @@ Creating a virtual machine based on Azure Cobalt 100 is no different from creati
 
 ![Azure portal VM creation — Azure Cobalt 100 Arm64 virtual machine (D4ps_v6) alt-text#center](images/instance1.png "Allow inbound port rules")
 
-- Click on the **Review + Create** tab and review the configuration for your virtual machine. It should look like the following:
+Click on the **Review + Create** tab and review the configuration for your virtual machine. It should look like the following:
 
 ![Azure portal VM creation — Azure Cobalt 100 Arm64 virtual machine (D4ps_v6) alt-text#center](images/ubuntu-pro.png "Review and Create an Azure Cobalt 100 Arm64 VM")
 
-- When you are confident about your selection, click on the **Create** button, and click on the **Download Private key and Create Resources** button.
+When you are confident about your selection, click on the **Create** button, and click on the **Download Private key and Create Resources** button.
 
 ![Azure portal VM creation — Azure Cobalt 100 Arm64 virtual machine (D4ps_v6) alt-text#center](images/instance4.png "Download Private key and Create Resources")
 
-- Your virtual machine should be ready and running within a few minutes. You can SSH into the virtual machine using the private key, along with the Public IP details.
+Your virtual machine should be ready and running within a few minutes. You can SSH into the virtual machine using the private key, along with the Public IP details.
 
 You should see your VM listed as **Running** in the Azure portal. If you have trouble connecting, double-check your SSH key and ensure the correct ports are open. If the VM creation fails, check your Azure quota, region availability, or try a different VM size.