diff --git a/.github/workflows/update-md-date.yml b/.github/workflows/update-md-date.yml
new file mode 100644
index 0000000..96dc9d7
--- /dev/null
+++ b/.github/workflows/update-md-date.yml
@@ -0,0 +1,41 @@
+name: Update Last Modified Date
+
+on:
+ pull_request:
+ branches:
+ - main
+
+permissions:
+ contents: write
+
+jobs:
+ update-date:
+ runs-on: ubuntu-latest
+
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+ with:
+ fetch-depth: 0
+
+ - name: Set up Python
+ uses: actions/setup-python@v4
+ with:
+ python-version: '3.x'
+
+ - name: Install dependencies
+ run: pip install python-dateutil
+
+ - name: Configure Git
+ run: |
+ git config --global user.email "github-actions[bot]@users.noreply.github.com"
+ git config --global user.name "github-actions[bot]"
+
+ - name: Update last modified date in Markdown files
+ run: python .github/workflows/update_date.py
+
+ - name: Commit changes
+ run: |
+ git add -A
+ git commit -m "Update last modified date in Markdown files" || echo "No changes to commit"
+ git push origin HEAD:${{ github.event.pull_request.head.ref }}
diff --git a/README.md b/README.md
index c5b1a47..279d557 100644
--- a/README.md
+++ b/README.md
@@ -20,14 +20,28 @@ Last updated: 2025-02-21
+## Workspace
+
+## Authoring
+
+## Assets
+
+## Manage
+
+| Compute Type | Description | Purpose | Ideal Use Cases | Key Features |
+|----------------------|-------------|---------|------------------|---------------|
+| **Compute Instances** | Preconfigured VMs (CPU/GPU) with tools like VS Code, JupyterLab, and RStudio. | Interactive development and experimentation. | Data exploration, model prototyping, debugging. | Auto-shutdown, integrated notebooks, preinstalled ML frameworks. |
+| **Compute Clusters** | Scalable clusters that auto-scale based on workload. Supports both CPU and GPU nodes. | Distributed training and batch inference. | Training large models, running parallel experiments. | Autoscaling, cost control, reusable across users. |
+| **Kubernetes Clusters** | Includes AKS and attached Kubernetes clusters for production-grade deployments. | Real-time inference and scalable deployment. | Hosting REST endpoints, high-availability inference. | Load balancing, autoscaling, secure networking. |
+| **Attached Compute** | External compute resources manually connected to Azure ML. | Leverage existing infrastructure. | Using Azure VMs, Databricks, or on-prem compute. | Flexibility, hybrid cloud support, reuse of existing resources. |
+| **Serverless Instances** | Lightweight, on-demand compute (e.g., Azure Container Instances). | Quick testing and low-scale inference. | Temporary model deployment, dev/test environments. | No infrastructure management, fast startup, cost-effective. |
+
-
-
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