🚀 MLOps Mastery: Building Enterprise LLM Systems on Azure Databricks with Azure AI and GitHub Platform
This playbook provides a comprehensive, implementation-focused guide for developing, deploying, and maintaining Large Language Model (LLM) systems on Azure Databricks. It covers everything from initial environment setup to advanced monitoring techniques, with practical examples that apply across industries.
- Introduction
- Repository Structure
- Environment Setup
- LLM Development Workflows
- GitHub Platform Integration
- Testing and Validation
- Deployment Options
- Monitoring and Observability
- Governance and Maintenance
- Industry Use Cases
- Common Challenges and Solutions
- Advanced Topics
- Contributing
- Credits
- License
This playbook is designed for data engineers, ML engineers, and developers who want to implement enterprise-grade LLM systems using Azure Databricks, Azure AI Foundry, and GitHub. By following this guide, you can achieve:
- 70% reduction in time-to-market for LLM applications
- Enterprise-grade scalability and reliability
- Repeatable, automated MLOps processes
- Integrated governance and compliance
MLOps-Azure-Databricks-GitHub-Playbook/
├── .github/ # GitHub-specific files
│ ├── workflows/ # GitHub Actions workflow definitions
│ ├── ISSUE_TEMPLATE/ # Templates for issues
│ └── PULL_REQUEST_TEMPLATE.md # Template for pull requests
├── docs/ # Documentation files
│ ├── diagrams/ # Architecture and workflow diagrams
│ ├── images/ # Screenshots and images
│ ├── use-cases/ # Detailed use case documentation
│ └── glossary.md # Terminology glossary
├── infrastructure/ # Infrastructure as Code
│ ├── terraform/ # Terraform configuration for Azure resources
│ └── databricks/ # Databricks workspace configuration
├── notebooks/ # Example Databricks notebooks
│ ├── development/ # Development-focused notebooks
│ ├── deployment/ # Deployment-focused notebooks
│ └── monitoring/ # Monitoring-focused notebooks
├── src/ # Source code for MLOps components
│ ├── data_processing/ # Code for data ingestion and processing
│ ├── model_training/ # Code for model training and fine-tuning
│ ├── evaluation/ # Code for model evaluation
│ ├── deployment/ # Code for model deployment
│ └── monitoring/ # Code for monitoring and observability
├── tests/ # Test code and fixtures
│ ├── unit/ # Unit tests
│ └── integration/ # Integration tests
├── config/ # Configuration files
│ ├── development/ # Development environment configs
│ ├── staging/ # Staging environment configs
│ └── production/ # Production environment configs
├── scripts/ # Utility scripts
│ ├── setup/ # Environment setup scripts
│ └── utils/ # Utility functions
├── .gitignore # Git ignore file
├── requirements.txt # Python dependencies
├── LICENSE # License information
└── README.md # This file
- Azure subscription with Databricks workspace access
- GitHub account with appropriate permissions
- Python 3.8+ installed locally
- Azure CLI installed
- Clone this repository
- Set up your Azure Databricks workspace following Environment Setup
- Configure GitHub integration as described in GitHub Integration
- Explore the example notebooks in the
notebooks/directory
| Section | Estimated Time to Complete |
|---|---|
| Environment Setup | 2-3 hours |
| LLM Development Workflow | 4-6 hours |
| GitHub Integration | 1-2 hours |
| Deployment Configuration | 3-4 hours |
| Full End-to-End Implementation | 2-3 days |
Each section of this playbook includes:
- Step-by-step implementation instructions
- Code examples with comments
- Architecture diagrams
- Configuration templates
- Troubleshooting tips
Contributions are welcome! Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.
This MLOps Azure Databricks GitHub Playbook was developed by @paulanunes85.
This project is licensed under the MIT License - see the LICENSE file for details.