SKYMIRROR ACADEMY
Full-Stack AI Engineer: Enterprise AI Application Development
24-Week Intensive Program

COURSE OVERVIEW
Duration: 24 weeks (6 months)
Format: Hybrid (online theory + hands-on projects)
Prerequisites: Basic computer literacy, willingness to learn programming
Target Audience: Aspiring AI engineers, career changers, professionals seeking AI integration skills

LEARNING PATH STRUCTURE

PHASE 1: WEB DEVELOPMENT FOUNDATIONS (Weeks 0-6)

Module 0: Orientation & Setup (Week 0)
Goal: Establish development environment and course foundation
Topics:
- Development environment setup (Node.js, VS Code, Git, Docker)
- Course overview and mastery-based progression rules
- Career goal setting and AI industry overview
- GitHub workflow and project management

Module 1: Web Development Foundations (Weeks 1-2)
Goal: Master modern web development fundamentals
Topics:
- HTML5 semantic structure and accessibility
- Modern CSS with Flexbox/Grid and responsive design
- JavaScript ES6+ fundamentals and async programming
- DOM manipulation, events, and API integration
Project: Interactive web application with API integration

Module 2: Modern Frontend with React (Weeks 3-4)
Goal: Build dynamic user interfaces with React
Topics:
- React components, hooks, and functional programming
- TypeScript integration and type safety
- State management with useState and useEffect
- Component lifecycle and performance optimization
Project: React-based dashboard with real-time data

Module 3: Advanced React & State Management (Weeks 5-6)
Goal: Implement complex state management and routing
Topics:
- Context API and Redux Toolkit
- React Router and navigation
- Form handling and validation
- Testing with Jest and React Testing Library
Project: Multi-page React application with authentication

PHASE 2: BACKEND & DATABASE SYSTEMS (Weeks 7-10)

Module 4: Backend Development with Node.js (Weeks 7-8)
Goal: Create robust server-side applications
Topics:
- Express.js server architecture and TypeScript
- RESTful API design and middleware
- JWT authentication and authorization
- MongoDB integration with Mongoose ODM
Project: AI-powered chat application backend

Module 5: Database Design & Management (Weeks 9-10)
Goal: Master database systems for AI applications
Topics:
- PostgreSQL advanced SQL and schema design
- NoSQL with MongoDB and Redis caching
- Vector databases for AI embeddings (Pinecone)
- Multi-database architecture and optimization
Project: Multi-database system with vector search

PHASE 3: AI/ML INTEGRATION (Weeks 11-16)

Module 6: AI/ML Fundamentals (Weeks 11-12)
Goal: Understand machine learning and AI integration
Topics:
- Machine learning concepts and Python integration
- Data preprocessing with pandas and numpy
- OpenAI API integration and prompt engineering
- Hugging Face transformers and model selection
Project: Machine learning data pipeline

Module 7: AI Integration & Advanced Applications (Weeks 13-14)
Goal: Build real-time AI applications
Topics:
- Real-time AI with WebSockets and streaming
- Multimodal AI (text, image, voice) integration
- AI model versioning and A/B testing
- Production AI monitoring and performance optimization
Project: Multimodal AI application with streaming

Module 8: Advanced AI Systems (Weeks 15-16)
Goal: Implement enterprise-grade AI systems
Topics:
- Retrieval-Augmented Generation (RAG) systems
- Document processing pipelines and vector search
- AI agents with multi-tool capabilities
- Enterprise AI architecture and compliance
Project: RAG-based knowledge system

PHASE 4: PRODUCTION & DEPLOYMENT (Weeks 17-20)

Module 9: Testing & Deployment (Weeks 17-18)
Goal: Deploy AI applications to production
Topics:
- AI testing strategies and performance testing
- Docker containerization and multi-stage builds
- Kubernetes deployment and orchestration
- CI/CD pipelines with GitHub Actions
Project: Containerized AI microservices

Module 10: Performance & Scaling (Weeks 19-20)
Goal: Optimize AI applications for scale
Topics:
- Advanced caching strategies for AI responses
- Load balancing and horizontal scaling
- Performance monitoring and analytics
- Cost optimization for AI APIs
Project: Scalable AI microservices architecture

PHASE 5: CAPSTONE PROJECT (Weeks 21-24)

Module 11: Capstone Project (Weeks 21-24)
Goal: Deliver enterprise-grade AI application
Capstone Requirements:
- Enterprise-grade AI application development
- Multi-tenant architecture and security
- Production deployment and monitoring
- Professional portfolio and career preparation

ASSESSMENT STRATEGY
- Assignments: 60% (hands-on projects and coding)
- Quizzes: 20% (knowledge checks and assessments)
- Participation: 10% (discussions and peer reviews)
- Capstone Project: 10% (final portfolio and presentation)
- Progression Requirement: 80% minimum score to advance

REQUIRED TOOLS & SOFTWARE
Development Environment:
- Node.js (v18+) and npm/yarn
- Visual Studio Code with AI extensions
- Python and Jupyter support
- Docker Desktop for containerization
- Git and GitHub account

AI Services & APIs:
- OpenAI API account and credits
- Hugging Face account for model access
- Pinecone vector database account
- MongoDB Atlas cloud database
- Redis Cloud for caching

Cloud Platforms:
- AWS/GCP/Azure account for deployment
- Kubernetes cluster access
- GitHub Actions for CI/CD
- Monitoring tools: Prometheus, Grafana

CAREER OUTCOMES
Upon completion, graduates will be able to:
- Design and develop full-stack AI applications
- Integrate multiple AI models and services
- Deploy scalable AI systems to production
- Optimize AI applications for performance and cost
- Build enterprise-grade AI solutions with security and compliance

STUDENT SUPPORT
- Weekly live Q&A sessions
- Industry professional mentors
- Peer learning groups
- Portfolio development guidance
- Career counseling and job placement assistance

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