pipeline update-md-date

.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 }}

README.md

@@ -20,14 +20,28 @@ Last updated: 2025-02-21
 
 </details>
 
+## 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. |
+
 
 <div align="center">
   <img src="" alt="Centered Image" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/>
 </div>
 
 
-
-
 <div align="center">
   <h3 style="color: #4CAF50;">Total Visitors</h3>
   <img src="https://profile-counter.glitch.me/brown9804/count.svg" alt="Visitor Count" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/>