Update background.md

Author: pareenaverma

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

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-## Cobalt 100 Arm-based processor
+## Azure Cobalt 100 Arm-based processor
 
 Azure’s Cobalt 100 is built on Microsoft's first-generation, in-house Arm-based processor: the Cobalt 100. Designed entirely by Microsoft and based on Arm’s Neoverse N2 architecture, this 64-bit CPU delivers improved performance and energy efficiency across a broad spectrum of cloud-native, scale-out Linux workloads. These include web and application servers, data analytics, open-source databases, caching systems, and more. Running at 3.4 GHz, the Cobalt 100 processor allocates a dedicated physical core for each vCPU, ensuring consistent and predictable performance. 
 
@@ -16,6 +16,6 @@ To learn more about Cobalt 100, refer to the blog [Announcing the preview of new
 ONNX (Open Neural Network Exchange) is an open-source format designed for representing machine learning models.  
 It provides interoperability between different deep learning frameworks, enabling models trained in one framework (such as PyTorch or TensorFlow) to be deployed and run in another.  
 
-ONNX models are serialized into a standardized format that can be executed by the **ONNX Runtime**, a high-performance inference engine optimized for CPU, GPU, and specialized hardware accelerators. This separation of model training and inference allows developers to build flexible, portable, and production-ready AI workflows.  
+ONNX models are serialized into a standardized format that can be executed by the ONNX Runtime, a high-performance inference engine optimized for CPU, GPU, and specialized hardware accelerators. This separation of model training and inference allows developers to build flexible, portable, and production-ready AI workflows.  
 
 ONNX is widely used in cloud, edge, and mobile environments to deliver efficient and scalable inference for deep learning models. Learn more from the [ONNX official website](https://onnx.ai/) and the [ONNX Runtime documentation](https://onnxruntime.ai/docs/).