Update GKE multi-arch Axion learning path content and structure

- Revise titles and weights for clarity and organization
- Enhance descriptions for target audience and learning objectives
- Add new background section detailing migration process and benefits

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/_index.md

@@ -1,24 +1,20 @@
 ---
-title: From x86 to Arm on GKE - Build, Deploy, and Migrate with Google Axion
-
-draft: true
-cascade:
-    draft: true
+title: Migrate x86 workloads to Arm on GKE with Google Axion
 
 minutes_to_complete: 90
 
-who_is_this_for: This is an advanced topic for cloud, platform, and site reliability engineers operating Kubernetes on Google Cloud who need a prescriptive path to build multi-architecture images and migrate services from x86 to Arm using Google Axion processors.
+who_is_this_for: This is an advanced topic for cloud, platform, and site reliability engineers who operate Kubernetes on Google Cloud and need to build multi-architecture images and migrate services from x86 to Arm using Google Axion processors.
 
 learning_objectives:
     - Prepare Dockerfiles for multi-architecture builds by adding arm64 support
-    - Create a dual-architecture GKE standard cluster with two node pools, amd64 and arm64
-    - Build and publish multi-architecture images to Artifact Registry using Docker Buildx without using QEMU to emulate Arm instructions
-    - Deploy a Kubernetes application amd64 first, then migrate to arm64 using Kustomize overlays and progressive rollout
-    - Optionally automate builds and rollouts with Cloud Build and Skaffold
+    - Create a dual-architecture GKE standard cluster with amd64 and arm64 node pools 
+    - Build and publish multi-architecture images to Artifact Registry using Docker Buildx
+    - Deploy a Kubernetes application on amd64, then migrate to arm64 using Kustomize overlays
+    - Automate builds and rollouts with Cloud Build and Skaffold
 
 prerequisites:
     - A [Google Cloud account](https://console.cloud.google.com/) with billing enabled
-    - A local Linux or macOS computer or Cloud Shell access with Docker, Kubernetes CLI (kubectl), Google Cloud CLI (gcloud), and Git installed
+    - A local Linux or macOS computer with Docker, Kubernetes CLI (kubectl), Google Cloud CLI (gcloud), and Git installed, or access to Google Cloud Shell
     - Basic familiarity with Docker, Kubernetes, and gcloud
 
 author: 
@@ -44,7 +40,7 @@ further_reading:
         link: https://cloud.google.com/kubernetes-engine/docs
         type: documentation
     - resource:
-        title: Create Arm-based clusters and node pools 
+        title: Create standard clusters and node pools with Arm nodes 
         link: https://cloud.google.com/kubernetes-engine/docs/how-to/create-arm-clusters-nodes
         type: documentation
 

content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/background.md

@@ -0,0 +1,48 @@
+---
+# User change
+title: "Overview and Environment Setup"
+
+weight: 2 # 1 is first, 2 is second, etc.
+
+# Do not modify these elements
+layout: "learningpathall"
+---
+
+This Learning Path demonstrates how to migrate a real microservices application from x86 to Arm (amd64 to arm64) on GKE using multi-architecture container images. The sample application is Google's Online Boutique, a polyglot microservices system that mirrors production architectures and ships with Dockerfiles. It's a realistic, real-world scenario, and the migration can be done with no major code changes.
+
+## Why Google Axion processors for GKE?
+
+Google Axion processors bring modern Arm-based compute to GKE, delivering strong price-performance and energy efficiency for cloud-native, scale-out services. With multi-architecture images and mixed node pools, services can migrate from x86 to Arm gradually, with no major code changes.
+
+### What is Google Axion?
+
+[Google Axion](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu) is Google Cloud's Arm-based CPU family built on Arm Neoverse, designed for general-purpose, cloud-native services and CPU-based AI. Typical workloads include web apps and web servers, containerized microservices, open-source databases, in-memory caches, data analytics, media processing, and CPU-based AI inference and data processing. On GKE, Axion powers the C4A and N4A VM families and is paired with Google's Titanium offloads to free CPU cycles for application work.
+
+### Why migrate to Arm on GKE?
+
+There are three factors motivating the move to Google Axion processors:
+
+- **Price-performance:** run more workload per dollar for scale-out services  
+- **Energy efficiency:** reduce power usage for always-on microservices
+- **Compatibility:** containerized apps typically migrate with build/deploy changes, and don't require code rewrites
+
+### About the Online Boutique sample application
+
+[Online Boutique](https://github.com/GoogleCloudPlatform/microservices-demo) is a polyglot microservices storefront, complete with shopping cart, checkout, catalog, ads, and recommendations. It's implemented in Go, Java, Python, .NET, and Node.js, with ready-to-use Dockerfiles and Kubernetes manifests. It's a realistic example for demonstrating an x86 to Arm migration with minimal code changes.
+
+### Multi-architecture on GKE (pragmatic path)
+
+This Learning Path presents a pragmatic migration approach that builds both amd64 and arm64 images using Docker Buildx with a Kubernetes driver, where builds run natively inside BuildKit pods on your GKE node pools without requiring QEMU emulation. You'll add an Arm node pool alongside existing x86 nodes, then use node selectors and affinity rules to control placement and migrate safely, service by service.
+
+### How this Learning Path demonstrates the migration
+
+You'll migrate the Online Boutique application from x86 to Arm using a practical, low-risk approach that leverages multi-architecture container images and mixed node pools. This allows you to validate each service on Arm before fully committing to the migration, ensuring compatibility and performance meet your requirements.
+
+The steps below outline the migration process: 
+
+1. Open Google Cloud Shell and set the environment variables.
+2. Enable required APIs, create an Artifact Registry repository, and authenticate Docker.
+3. Create a GKE Standard cluster with an amd64 node pool and add an arm64 (Axion-based C4A) node pool.
+4. Create a Buildx (Kubernetes driver) builder that targets both pools, then build and push multi-architecture images (amd64 and arm64) natively via BuildKit pods.
+5. Deploy to amd64 first (Kustomize overlay), validate, then migrate to arm64 (overlay) and verify.
+6. Automate builds and rollouts with Cloud Build and Skaffold.

content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/cloud-build.md

@@ -1,6 +1,6 @@
 ---
 title: Automate builds and rollout with Cloud Build and Skaffold
-weight: 6
+weight: 7
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall

content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/gke-build-push.md

@@ -1,6 +1,6 @@
 ---
 title: Provision a dual-architecture GKE cluster and publish images
-weight: 4
+weight: 5
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall

content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/gke-deploy.md

@@ -1,6 +1,6 @@
 ---
 title: Prepare manifests and deploy on GKE
-weight: 5
+weight: 6
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall

content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/multi-arch-images.md

@@ -1,6 +1,6 @@
 ---
 title: Create build-ready Dockerfiles for both architectures
-weight: 3
+weight: 4
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall

content/learning-paths/servers-and-cloud-computing/gke-multi-arch-axion/project-setup.md

@@ -2,7 +2,7 @@
 # User change
 title: "Overview and Environment Setup"
 
-weight: 2 # 1 is first, 2 is second, etc.
+weight: 3 # 1 is first, 2 is second, etc.
 
 # Do not modify these elements
 layout: "learningpathall"