############ 12.0 Project ############ =================== Learning Objectives =================== By the end of this capstone project, you will be able to: - **Code** a complete Python application that integrates with a Large Language Model (LLM) service - **Integrate** all DevOps concepts learned throughout the course into a real-world application - **Design** and implement a complete CI/CD pipeline for a cloud-native application - **Deploy** applications to production using containers, Kubernetes, and cloud platforms - **Apply** infrastructure as code principles for reproducible and scalable deployments - **Implement** monitoring, logging, and security best practices for production systems - **Demonstrate** collaborative development workflows using Git, code reviews, and documentation - **Troubleshoot** and optimize real-world deployment issues and performance bottlenecks - **Present** technical solutions effectively to stakeholders and receive constructive feedback ================================================================================ Build and deploy a complete Python application with LLM integration in the cloud ================================================================================ =============== Project purpose =============== .. note:: This project is optional! If you choose to do this project you will supplement the theoretical knowledge gained during the course with practical experience that will help you a lot in understanding how and when you can apply the knowledge learned in this course. ===================== Project Prerequisites ===================== This capstone project integrates all the DevOps concepts you've learned throughout the course. The project is structured as progressive tasks that will give you insight into real-world DevOps workflows. **Course Foundation Required:** Before starting this project, you should have completed the course modules covering: - Linux system administration and scripting - Git version control and collaboration workflows - Python programming and application development - Containerization with Docker - Kubernetes orchestration and deployment - CI/CD pipeline design and implementation - Infrastructure as Code (Terraform/Ansible) - Cloud platforms and services - Monitoring, logging, and security practices **Why This Project Matters:** DevOps work focuses on helping development teams be more productive through automation, reliable infrastructure, and streamlined processes. This project will make you a well-rounded developer who understands the complete software delivery lifecycle. ================ Project Overview ================ .. note:: **This project is optional but highly recommended!** This capstone project allows you to apply the theoretical knowledge gained during the course in a practical, real-world scenario. You'll build a complete application that demonstrates your mastery of DevOps principles and practices. **What You'll Build:** You need to create a complete Python application (or challenge yourself with Go, Ruby, JavaScript, or Scala) that integrates with a Large Language Model (LLM) service of your choice. **Project Scope:** Building an LLM-integrated microservice involves multiple phases: 1. **Planning & Setup**: Environment configuration and project structure 2. **Development**: Application logic and LLM service integration 3. **Testing**: Automated testing and quality assurance 4. **Deployment**: Containerization and cloud deployment 5. **Operations**: Monitoring, maintenance, and documentation **LLM Service Options:** You can choose from any of the following LLM services (or others): - **OpenAI**: https://platform.openai.com/docs/api-reference - **Anthropic Claude**: https://docs.anthropic.com/claude/reference/getting-started - **Google Gemini**: https://ai.google.dev/docs - **Hugging Face Transformers**: https://huggingface.co/docs/transformers/ **Application Ideas:** If you need inspiration, consider building one of these applications: - **Conversational Chatbot**: Web-based chat interface with context memory - **Document Analysis Tool**: Upload and analyze documents with AI insights - **Code Assistant**: Help developers with code review, generation, or debugging - **Content Generator**: Create blogs, articles, or creative writing with AI assistance - **Q&A System**: Domain-specific question answering (e.g., technical support, education) - **Translation Service**: Language translation with additional context or features - **Custom Solution**: Any other LLM-powered application that solves a real problem **Quality Standards:** Your application must be production-ready with proper architecture, comprehensive error handling, structured logging, and an intuitive user interface. **Learning Path:** This project builds upon everything you've learned: Linux server management, dependency automation, application development, architecture design, testing, packaging, and deployment across Linux, Docker, Kubernetes, and cloud platforms. ================== Timeline & Reviews ================== **Project Timeline:** - **Start**: Any time during the course - **Completion**: By the end of the course - **Peer Review**: Required before instructor review - **Final Review**: Schedule with course instructors **Review Process:** Before scheduling your final instructor review, you must: 1. Complete all project requirements (see checklist below) 2. Obtain at least one peer review from a fellow student 3. Address any feedback from the peer review 4. Prepare your project demonstration and documentation ============================== Project Requirements Checklist ============================== Use this checklist to track your progress and ensure you meet all requirements before your final review. ======================== Application Architecture ======================== *Core application components and design* 1. **Web Application**: Build a complete web application with both frontend and backend components - **Frontend**: Interactive user interface (web-based) - **Backend**: RESTful API or similar service architecture - **Database**: Optional but recommended for data persistence - **Authentication**: User login/authentication system required 2. **LLM Integration**: Successfully integrate with at least one Large Language Model service - Choose from OpenAI, Anthropic Claude, Google Gemini, or Hugging Face - Implement proper API handling and error management - Include rate limiting and cost management considerations 3. **Internet Accessibility**: Application must be publicly accessible - Deploy to a cloud platform (AWS, Azure, GCP, or similar) - Configure HTTPS/SSL certificates for secure communication - Implement proper domain and DNS configuration ===================== Development & Testing ===================== *Code quality and development workflow practices* 4. **Code Quality**: Follow professional development standards - Use proper code structure and design patterns - Implement comprehensive error handling and logging - Include unit tests and integration tests - Follow coding standards and best practices for chosen language 5. **Version Control**: Demonstrate professional Git workflows - Use meaningful commit messages and proper branching strategy - Implement code review process with pull requests - Maintain clean project history and documentation ======================= Deployment & Operations ======================= *Infrastructure, containerization, and automation* 6. **Containerization**: Package application using Docker - Create optimized Dockerfiles for each component - Use multi-stage builds where appropriate - Implement proper container security practices 7. **Orchestration**: Deploy to Kubernetes cluster - Create Kubernetes manifests or Helm charts - Implement proper resource limits and requests - Configure services, ingress, and persistent volumes as needed 8. **Infrastructure as Code**: Automate infrastructure provisioning - Use Terraform, Ansible, or similar IaC tools - Create reproducible and version-controlled infrastructure - Document infrastructure dependencies and requirements 9. **CI/CD Pipeline**: Implement automated build and deployment - Automated testing on code commits - Automated building and packaging of applications - Automated deployment to staging and production environments - Include rollback capabilities for failed deployments ===================== Monitoring & Security ===================== *Production readiness and operational excellence* 10. **Observability**: Implement comprehensive monitoring - Application logging with structured logs - Health checks and readiness probes - Performance metrics and monitoring dashboards - Alerting for critical issues 11. **Security**: Follow security best practices - Secure secret management (no hardcoded secrets) - Vulnerability scanning in CI/CD pipeline - Implement proper authentication and authorization - Follow OWASP security guidelines ============================ Documentation & Presentation ============================ *Communication and knowledge sharing* 12. **Documentation**: Create comprehensive project documentation - Architecture overview and design decisions - Setup and deployment instructions - API documentation and user guides - Troubleshooting and maintenance procedures 13. **Project Presentation**: Prepare final project demonstration - Live demo of working application - Explanation of technical choices and trade-offs - Discussion of challenges faced and solutions implemented - Q&A session with technical reviewers ============ Success Tips ============ **Start Early**: Begin planning and setting up your development environment as soon as possible. **Iterate Frequently**: Build your application incrementally, testing each component as you go. **Document Everything**: Keep detailed notes of your decisions, challenges, and solutions. **Ask for Help**: Use the course community and instructors when you get stuck. **Focus on Learning**: The goal is to demonstrate your understanding of DevOps concepts, not to build the perfect application.