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Welcome to EdgeAI for Beginners – your comprehensive journey into the transformative world of Edge Artificial Intelligence. This course bridges the gap between powerful AI capabilities and practical, real-world deployment on edge devices, empowering you to harness AI's potential directly where data is generated and decisions need to be made.
This course takes you from fundamental concepts to production-ready implementations, covering:
- Small Language Models (SLMs) optimized for edge deployment
- Hardware-aware optimization across diverse platforms
- Real-time inference with privacy-preserving capabilities
- Production deployment strategies for enterprise applications
Edge AI represents a paradigm shift that addresses critical modern challenges:
- Privacy & Security: Process sensitive data locally without cloud exposure
- Real-time Performance: Eliminate network latency for time-critical applications
- Cost Efficiency: Reduce bandwidth and cloud computing expenses
- Resilient Operations: Maintain functionality during network outages
- Regulatory Compliance: Meet data sovereignty requirements
Edge AI refers to running AI algorithms and language models locally on hardware, close to where data is generated without relying on cloud resources for inference. It reduces latency, enhances privacy, and enables real-time decision-making.
- On-device inference: AI models run on edge devices (phones, routers, microcontrollers, industrial PCs)
- Offline capability: Functions without persistent internet connectivity
- Low latency: Immediate responses suited for real-time systems
- Data sovereignty: Keeps sensitive data local, improving security and compliance
SLMs like Phi-4, Mistral-7B, and Gemma are optimized versions of larger LLMs—trained or distilled for:
- Reduced memory footprint: Efficient use of limited edge device memory
- Lower compute demand: Optimized for CPU and edge GPU performance
- Faster startup times: Quick initialization for responsive applications
They unlock powerful NLP capabilities while meeting the constraints of:
- Embedded systems: IoT devices and industrial controllers
- Mobile devices: Smartphones and tablets with offline capabilities
- IoT Devices: Sensors and smart devices with limited resources
- Edge servers: Local processing units with limited GPU resources
- Personal Computers: Desktop and laptop deployment scenarios
| Module | Topic | Focus Area | Key Content | Level | Duration |
|---|---|---|---|---|---|
| 📖 00 | Introduction to EdgeAI | Foundation & Context | EdgeAI Overview • Industry Applications • SLM Introduction • Learning Objectives | Beginner | 1-2 hrs |
| 📚 01 | EdgeAI Fundamentals | Cloud vs Edge AI comparison | EdgeAI Fundamentals • Real World Case Studies • Implementation Guide • Edge Deployment | Beginner | 3-4 hrs |
| 🧠 02 | SLM Model Foundations | Model families & architecture | Phi Family • Qwen Family • Gemma Family • BitNET • μModel • Phi-Silica | Beginner | 4-5 hrs |
| 🚀 03 | SLM Deployment Practice | Local & cloud deployment | Advanced Learning • Local Environment • Cloud Deployment | Intermediate | 4-5 hrs |
| ⚙️ 04 | Model Optimization Toolkit | Cross-platform optimization | Introduction • Llama.cpp • Microsoft Olive • OpenVINO • Apple MLX • Workflow Synthesis | Intermediate | 5-6 hrs |
| 🔧 05 | SLMOps Production | Production operations | SLMOps Introduction • Model Distillation • Fine-tuning • Production Deployment | Advanced | 5-6 hrs |
| 🤖 06 | AI Agents & Function Calling | Agent frameworks & MCP | Agent Introduction • Function Calling • Model Context Protocol | Advanced | 4-5 hrs |
| 💻 07 | Platform Implementation | Cross-platform samples | AI Toolkit • Foundry Local • Windows Development | Advanced | 3-4 hrs |
| 🏭 08 | Foundry Local Toolkit | Production-ready samples | Sample applications (see details below) | Expert | 8-10 hrs |
Comprehensive hands-on workshop materials with production-ready implementations:
- Workshop Guide - Complete learning objectives, outcomes, and resource navigation
- Python Samples (6 sessions) - Updated with best practices, error handling, and comprehensive documentation
- Jupyter Notebooks (8 interactive) - Step-by-step tutorials with benchmarks and performance monitoring
- Session Guides - Detailed markdown guides for each workshop session
- Validation Tools - Scripts to verify code quality and run smoke tests
What You'll Build:
- Local AI chat applications with streaming support
- RAG pipelines with quality evaluation (RAGAS)
- Multi-model benchmarking and comparison tools
- Multi-agent orchestration systems
- Intelligent model routing with task-based selection
- Total Duration: 36-45 hours
- Beginner Path: Modules 01-02 (7-9 hours)
- Intermediate Path: Modules 03-04 (9-11 hours)
- Advanced Path: Modules 05-07 (12-15 hours)
- Expert Path: Module 08 (8-10 hours)
- Edge AI Architecture: Design local-first AI systems with cloud integration
- Model Optimization: Quantize and compress models for edge deployment (85% speed boost, 75% size reduction)
- Multi-Platform Deployment: Windows, mobile, embedded, and cloud-edge hybrid systems
- Production Operations: Monitoring, scaling, and maintaining edge AI in production
- Foundry Local Chat Apps: Windows 11 native application with model switching
- Multi-Agent Systems: Coordinator with specialist agents for complex workflows
- RAG Applications: Local document processing with vector search
- Model Routers: Intelligent selection between models based on task analysis
- API Frameworks: Production-ready clients with streaming and health monitoring
- Cross-Platform Tools: LangChain/Semantic Kernel integration patterns
Manufacturing • Healthcare • Autonomous Vehicles • Smart Cities • Mobile Apps
Recommended Learning Path (20-30 hours total):
- 📖 Introduction (Introduction.md): EdgeAI foundation + industry context + learning framework
- 📚 Foundation (Modules 01-02): EdgeAI concepts + SLM model families
- ⚙️ Optimization (Modules 03-04): Deployment + quantization frameworks
- 🚀 Production (Modules 05-06): SLMOps + AI agents + function calling
- 💻 Implementation (Modules 07-08): Platform samples + Foundry Local toolkit
Each module includes theory, hands-on exercises, and production-ready code samples.
Technical Roles: EdgeAI Solutions Architect • ML Engineer (Edge) • IoT AI Developer • Mobile AI Developer
Industry Sectors: Manufacturing 4.0 • Healthcare Tech • Autonomous Systems • FinTech • Consumer Electronics
Portfolio Projects: Multi-agent systems • Production RAG apps • Cross-platform deployment • Performance optimization
edgeai-for-beginners/
├── 📖 introduction.md # Foundation: EdgeAI Overview & Learning Framework
├── 📚 Module01-04/ # Fundamentals → SLMs → Deployment → Optimization
├── 🔧 Module05-06/ # SLMOps → AI Agents → Function Calling
├── 💻 Module07/ # Platform Samples (VS Code, Windows, Jetson, Mobile)
├── 🏭 Module08/ # Foundry Local Toolkit + 10 Comprehensive Samples
│ ├── samples/01-06/ # Foundation: REST, SDK, RAG, Agents, Routing
│ └── samples/07-10/ # Advanced: API Client, Windows App, Enterprise Agents, Tools
├── 🌐 translations/ # Multi-language support (8+ languages)
└── 📋 STUDY_GUIDE.md # Structured learning paths & time allocation
✅ Progressive Learning: Theory → Practice → Production deployment
✅ Real Case Studies: Microsoft, Japan Airlines, enterprise implementations
✅ Hands-on Samples: 50+ examples, 10 comprehensive Foundry Local demos
✅ Performance Focus: 85% speed improvements, 75% size reductions
✅ Multi-Platform: Windows, mobile, embedded, cloud-edge hybrid
✅ Production Ready: Monitoring, scaling, security, compliance frameworks
📖 Study Guide Available: Structured 20-hour learning path with time allocation guidance and self-assessment tools.
EdgeAI represents the future of AI deployment: local-first, privacy-preserving, and efficient. Master these skills to build the next generation of intelligent applications.
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