Do Not Merge_Sample Pull request_MCP Example

.github/workflows/sdk-assets-sdkmcpsample-sdkmcpsample.yml

@@ -0,0 +1,94 @@
+# This code is autogenerated.
+# Code is generated by running custom script: python3 readme.py
+# Any manual changes to this file may cause incorrect behavior.
+# Any manual changes will be overwritten if the code is regenerated.
+
+name: sdk-assets-sdkmcpsample-sdkmcpsample
+# This file is created by sdk/python/readme.py.
+# Please do not edit directly.
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "4 15/12 * * *"
+  pull_request:
+    branches:
+      - main
+    paths:
+      - sdk/python/assets/sdkmcpsample/**
+      - .github/workflows/sdk-assets-sdkmcpsample-sdkmcpsample.yml
+      - sdk/python/dev-requirements.txt
+      - infra/bootstrapping/**
+      - sdk/python/setup.sh
+
+permissions:
+  id-token: write
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - name: check out repo
+      uses: actions/checkout@v2
+    - name: setup python
+      uses: actions/setup-python@v2
+      with:
+        python-version: "3.10"
+    - name: pip install notebook reqs
+      run: pip install -r sdk/python/dev-requirements.txt
+    - name: azure login
+      uses: azure/login@v1
+      with:
+        client-id: ${{ secrets.OIDC_AZURE_CLIENT_ID }}
+        tenant-id: ${{ secrets.OIDC_AZURE_TENANT_ID }}
+        subscription-id: ${{ secrets.OIDC_AZURE_SUBSCRIPTION_ID }}
+    - name: bootstrap resources
+      run: |
+          echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}';
+          bash bootstrap.sh
+      working-directory: infra/bootstrapping
+      continue-on-error: false
+    - name: setup SDK
+      run: |
+          source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh";
+          bash setup.sh
+      working-directory: sdk/python
+      continue-on-error: true
+    - name: validate readme
+      run: |
+          python check-readme.py "${{ github.workspace }}/sdk/python/assets/sdkmcpsample"
+      working-directory: infra/bootstrapping
+      continue-on-error: false
+    - name: setup-cli
+      run: |
+          source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh";
+          bash setup.sh
+      working-directory: cli
+      continue-on-error: true
+    - name: Eagerly cache access tokens for required scopes
+      run: |
+          # Workaround for azure-cli's lack of support for ID token refresh
+          # Taken from: https://github.com/Azure/login/issues/372#issuecomment-2056289617
+
+          # Management
+          az account get-access-token --scope https://management.azure.com/.default --output none
+          # ML
+          az account get-access-token --scope https://ml.azure.com/.default --output none
+    - name: run assets/sdkmcpsample/sdkmcpsample.ipynb
+      run: |
+          source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh";
+          bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json";
+          bash "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh" replace_template_values "sdkmcpsample.ipynb";
+          [ -f "../../.azureml/config" ] && cat "../../.azureml/config";
+          papermill -k python sdkmcpsample.ipynb sdkmcpsample.output.ipynb
+      working-directory: sdk/python/assets/sdkmcpsample
+    - name: upload notebook's working folder as an artifact
+      if: ${{ always() }}
+      uses: ./.github/actions/upload-artifact
+      with:
+        name: sdkmcpsample
+        path: sdk/python/assets/sdkmcpsample

sdk/python/README.md

@@ -43,6 +43,7 @@ Test Status is for branch - **_main_**
 |assets|data|[working_with_mltable](assets/data/working_with_mltable.ipynb)|Read, write and register a data asset|[![working_with_mltable](https://github.com/Azure/azureml-examples/actions/workflows/sdk-assets-data-working_with_mltable.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-assets-data-working_with_mltable.yml)|
 |assets|environment|[environment](assets/environment/environment.ipynb)|Create custom environments from docker and/or conda YAML|[![environment](https://github.com/Azure/azureml-examples/actions/workflows/sdk-assets-environment-environment.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-assets-environment-environment.yml)|
 |assets|model|[model](assets/model/model.ipynb)|Create model from local files, cloud files, Runs|[![model](https://github.com/Azure/azureml-examples/actions/workflows/sdk-assets-model-model.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-assets-model-model.yml)|
+|assets|sdkmcpsample|[sdkmcpsample](assets/sdkmcpsample/sdkmcpsample.ipynb)|Advanced Azure ML SDK v2 sample with MCP integration, comprehensive analytics, and enterprise-grade ML workflows|[![sdkmcpsample](https://github.com/Azure/azureml-examples/actions/workflows/sdk-assets-sdkmcpsample-sdkmcpsample.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-assets-sdkmcpsample-sdkmcpsample.yml)|
 |basic-installation|basic-installation|[basic-installation](basic-installation/basic-installation.ipynb)|Install and do a basic sanity of azure-ai-ml by listing the workspaces available in the resource group.||
 |data-wrangling|interactive_data_wrangling.ipynb|[interactive_data_wrangling](data-wrangling/interactive_data_wrangling.ipynb)|*no description* - _This sample is excluded from automated tests_|[![interactive_data_wrangling](https://github.com/Azure/azureml-examples/actions/workflows/sdk-data-wrangling-interactive_data_wrangling.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-data-wrangling-interactive_data_wrangling.yml)|
 |endpoints|batch|[custom-output-batch](endpoints/batch/deploy-models/custom-outputs-parquet/custom-output-batch.ipynb)|*no description*|[![custom-output-batch](https://github.com/Azure/azureml-examples/actions/workflows/sdk-endpoints-batch-deploy-models-custom-outputs-parquet-custom-output-batch.yml/badge.svg?branch=main)](https://github.com/Azure/azureml-examples/actions/workflows/sdk-endpoints-batch-deploy-models-custom-outputs-parquet-custom-output-batch.yml)|

sdk/python/assets/sdkmcpsample/README.md

@@ -0,0 +1,256 @@
+# Azure ML SDK MCP Sample
+
+This folder contains an advanced sample notebook demonstrating comprehensive usage of Azure Machine Learning SDK v2 with a focus on MCP (Model Context Protocol) integration and enterprise-grade ML workflows.
+
+## Overview
+
+The `sdkmcpsample.ipynb` notebook provides an in-depth exploration of Azure ML SDK v2, covering advanced concepts and operations for experienced machine learning practitioners and MLOps engineers.
+
+## What You'll Learn
+
+- **Advanced Workspace Connection**: Robust authentication patterns with fallback mechanisms
+- **Comprehensive Asset Management**: In-depth analysis of data, model, environment, and component assets
+- **Advanced Compute Management**: Compute resource optimization and analysis
+- **Sophisticated Data Operations**: Multi-dataset management with rich metadata and comprehensive analysis
+- **Advanced Data Exploration**: Statistical analysis, visualizations, and data quality assessment
+- **Job and Experiment Analytics**: Comprehensive workflow analysis and performance metrics
+- **Model and Endpoint Management**: Advanced deployment strategies and endpoint health monitoring
+- **Workspace Health Monitoring**: Performance metrics, health scoring, and optimization recommendations
+
+## Prerequisites
+
+Before running this notebook, ensure you have:
+
+1. **Azure Subscription**: An active Azure subscription with a configured Azure ML workspace
+2. **Python Environment**: Python 3.8+ with the following packages:
+   ```bash
+   pip install azure-ai-ml azure-identity pandas scikit-learn matplotlib seaborn
+   ```
+3. **Authentication**: Proper credentials for accessing your Azure ML workspace
+4. **Permissions**: Appropriate RBAC permissions for asset management and compute operations
+5. **Basic ML Knowledge**: Understanding of machine learning concepts and workflows
+
+## Getting Started
+
+1. **Clone the Repository**:
+   ```bash
+   git clone https://github.com/Azure/azureml-examples
+   cd azureml-examples/sdk/python/assets/sdkmcpsample
+   ```
+
+2. **Set Up Environment**:
+   ```bash
+   # Create virtual environment
+   python -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+   # Install dependencies
+   pip install azure-ai-ml azure-identity pandas scikit-learn matplotlib seaborn
+   ```
+
+3. **Open the Notebook**:
+   - Open `sdkmcpsample.ipynb` in Jupyter Lab, Jupyter Notebook, or VS Code
+   - Alternatively, use Azure ML Studio notebooks for cloud execution
+
+4. **Configure Workspace Details**:
+   Update the workspace configuration in the notebook:
+   ```python
+   subscription_id = "<your-subscription-id>"
+   resource_group_name = "<your-resource-group>"
+   workspace_name = "<your-workspace-name>"
+   ```
+
+5. **Run the Notebook**:
+   Execute the cells sequentially to learn about advanced Azure ML SDK v2 capabilities
+
+## Notebook Structure
+
+### 1. Setup and Installation
+- Advanced package management and environment verification
+- Comprehensive import statements for ML workflows
+
+### 2. Advanced Workspace Connection
+- Robust authentication with multiple credential fallbacks
+- Connection validation and error handling
+
+### 3. Comprehensive Workspace Assets Analysis
+- Detailed analysis of all asset types with metadata
+- Asset categorization and distribution analysis
+
+### 4. Advanced Compute Resource Management
+- Compute resource optimization and scaling analysis
+- Performance metrics and cost optimization
+
+### 5. Advanced Data Asset Management
+- Multi-dataset registration with rich metadata
+- Data lineage and versioning strategies
+
+### 6. Advanced Data Exploration and Analysis
+- Statistical analysis with comprehensive visualizations
+- Data quality assessment and anomaly detection
+
+### 7. Advanced Job and Experiment Management
+- Workflow analysis and performance tracking
+- Experiment comparison and optimization insights
+
+### 8. Model and Endpoint Management
+- Advanced deployment strategies and patterns
+- Endpoint health monitoring and performance analysis
+
+### 9. Workspace Health and Performance Metrics
+- Comprehensive health scoring system
+- Performance optimization recommendations
+
+## Key Features
+
+### Advanced Analytics
+- **Statistical Analysis**: Comprehensive data profiling and quality assessment
+- **Visualization Suite**: Professional-grade charts and insights
+- **Performance Metrics**: Detailed workspace and resource utilization analysis
+
+### Enterprise Features
+- **Health Monitoring**: Automated workspace health scoring
+- **Optimization Recommendations**: AI-driven suggestions for improvement
+- **Cost Analysis**: Resource utilization and cost optimization insights
+
+### Production Readiness
+- **Error Handling**: Robust exception management and fallback strategies
+- **Scalability**: Patterns for large-scale ML operations
+- **Best Practices**: Enterprise-grade coding standards and patterns
+
+## Sample Data
+
+The notebook works with multiple datasets for comprehensive demonstration:
+
+- **Titanic Dataset**: Classification problem for passenger survival prediction
+- **Diabetes Dataset**: Regression problem for medical outcome prediction
+- **Custom Data**: Extensible framework for your own datasets
+
+## Authentication Options
+
+The notebook supports multiple authentication methods with intelligent fallback:
+
+- **DefaultAzureCredential**: Automatic authentication in Azure environments
+- **InteractiveBrowserCredential**: Browser-based interactive authentication
+- **Service Principal**: Programmatic authentication for automated workflows
+- **Managed Identity**: Azure-managed identity for secure, passwordless authentication
+
+## Advanced Use Cases
+
+This sample is ideal for:
+
+- **MLOps Engineers**: Implementing enterprise-grade ML operations
+- **Data Scientists**: Advanced model development and experimentation
+- **Platform Teams**: Setting up and managing ML infrastructure
+- **Architects**: Designing scalable ML solutions
+- **DevOps Teams**: Integrating ML into CI/CD pipelines
+
+## Performance Optimization
+
+### Resource Management
+- Compute auto-scaling strategies
+- Data caching and optimization
+- Model serving optimization
+
+### Cost Optimization
+- Resource utilization monitoring
+- Automated cost alerts and budgeting
+- Right-sizing recommendations
+
+### Security Best Practices
+- RBAC implementation patterns
+- Network security configurations
+- Data encryption and compliance
+
+## Related Examples
+
+After completing this advanced sample, explore these related notebooks:
+
+- **[Basic SDK Sample](../sdksample/sdksample.ipynb)**: Foundational SDK concepts
+- **[MLOps Pipelines](../../../tutorials/mlops/)**: Production pipeline development
+- **[AutoML Integration](../../../tutorials/automl/)**: Automated machine learning
+- **[Distributed Training](../../../tutorials/distributed-training/)**: Large-scale model training
+- **[Model Deployment](../../endpoints/)**: Advanced deployment strategies
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Authentication Failures**:
+   ```bash
+   # Check Azure CLI login
+   az login
+   az account show
+
+   # Verify workspace access
+   az ml workspace show --name <workspace-name> --resource-group <resource-group>
+   ```
+
+2. **Package Dependencies**:
+   ```bash
+   # Update packages
+   pip install --upgrade azure-ai-ml azure-identity
+
+   # Check versions
+   pip list | grep azure
+   ```
+
+3. **Memory Issues with Large Datasets**:
+   - Use data streaming techniques
+   - Implement chunked processing
+   - Consider distributed computing options
+
+4. **Compute Resource Issues**:
+   - Verify compute quotas and limits
+   - Check resource availability in your region
+   - Review RBAC permissions for compute management
+
+### Performance Tuning
+
+1. **Data Loading Optimization**:
+   - Use Azure ML data streams for large datasets
+   - Implement data caching strategies
+   - Consider data preprocessing pipelines
+
+2. **Compute Optimization**:
+   - Right-size compute instances
+   - Use spot instances for cost savings
+   - Implement auto-scaling policies
+
+3. **Network Optimization**:
+   - Use VNet integration for security
+   - Optimize data transfer patterns
+   - Consider regional data placement
+
+### Getting Help
+
+- [Azure ML Documentation](https://docs.microsoft.com/azure/machine-learning/)
+- [SDK v2 Migration Guide](https://docs.microsoft.com/azure/machine-learning/how-to-migrate-from-v1)
+- [Azure ML Community](https://techcommunity.microsoft.com/t5/azure-ai/ct-p/Azure-AI)
+- [GitHub Issues](https://github.com/Azure/azureml-examples/issues)
+- [Stack Overflow](https://stackoverflow.com/questions/tagged/azure-machine-learning)
+
+## Contributing
+
+We welcome contributions to improve this advanced sample! Please see the [CONTRIBUTING.md](../../../../CONTRIBUTING.md) file for guidelines on:
+
+- Code standards and best practices
+- Testing requirements
+- Documentation standards
+- Pull request process
+
+## Security
+
+For security-related concerns, please review our [SECURITY.md](../../../../SECURITY.md) file and follow responsible disclosure practices.
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](../../../../LICENSE) file for details.
+
+## Changelog
+
+### Version 1.0
+- Initial release with comprehensive Azure ML SDK v2 coverage
+- Advanced analytics and visualization capabilities
+- Enterprise-grade health monitoring and optimization
+- Production-ready error handling and best practices