Edge AI for Beginners

EdgeAI for Beginners Follow these steps to get started using these resources: 🌐 Multi-Language Support Supported via GitHub Action (Automated & Always Up-to-Date) Arabic | Bengali | Bulgarian | Burmese (Myanmar) | Chinese (Simplified) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Macau) | Chinese (Traditional, Taiwan) | Croatian | Czech | Danish | Dutch | Estonian | Finnish | French | German | Greek | Hebrew | Hindi | Hungarian | Indonesian | Italian | Japanese | Korean | Lithuanian | Malay | Marathi | Nepali | Norwegian | Persian (Farsi) | Polish | Portuguese (Brazil) | Portuguese (Portugal) | Punjabi (Gurmukhi) | Romanian | Russian | Serbian (Cyrillic) | Slovak | Slovenian | Spanish | Swahili | Swedish | Tagalog (Filipino) | Tamil | Thai | Turkish | Ukrainian | Urdu | Vietnamese If you wish to have additional translations languages supported are listed here Introduction 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. What You'll Master This course takes you from fundamental concepts to production-ready implementations, covering: Small Language Models (SLMs) optimized for edge deployment optimized for edge deployment Hardware-aware optimization across diverse platforms across diverse platforms Real-time inference with privacy-preserving capabilities with privacy-preserving capabilities Production deployment strategies for enterprise applications Why EdgeAI Matters Edge AI represents a paradigm shift that addresses critical modern challenges: Privacy & Security : Process sensitive data locally without cloud exposure : Process sensitive data locally without cloud exposure Real-time Performance : Eliminate network latency for time-critical applications : Eliminate network latency for time-critical applications Cost Efficiency : Reduce bandwidth and cloud computing expenses : Reduce bandwidth and cloud computing expenses Resilient Operations : Maintain functionality during network outages : Maintain functionality during network outages Regulatory Compliance: Meet data sovereignty requirements Edge AI 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. Core Principles: On-device inference : AI models run on edge devices (phones, routers, microcontrollers, industrial PCs) : AI models run on edge devices (phones, routers, microcontrollers, industrial PCs) Offline capability : Functions without persistent internet connectivity : Functions without persistent internet connectivity Low latency : Immediate responses suited for real-time systems : Immediate responses suited for real-time systems Data sovereignty: Keeps sensitive data local, improving security and compliance Small Language Models (SLMs) 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 : Efficient use of limited edge device memory Lower compute demand : Optimized for CPU and edge GPU performance : 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 : IoT devices and industrial controllers Mobile devices : Smartphones and tablets with offline capabilities : Smartphones and tablets with offline capabilities IoT Devices : Sensors and smart devices with limited resources : Sensors and smart devices with limited resources Edge servers : Local processing units with limited GPU resources : Local processing units with limited GPU resources Personal Computers: Desktop and laptop deployment scenarios Course Modules & Navigation 🏭 Module 08: Sample Applications 🎓 Workshop: Hands-On Learning Path Comprehensive hands-on workshop materials with production-ready implementations: Workshop Guide - Complete learning objectives, outcomes, and resource navigation - Complete learning objectives, outcomes, and resource navigation Python Samples (6 sessions) - Updated with best practices, error handling, and comprehensive documentation (6 sessions) - Updated with best practices, error handling, and comprehensive documentation Jupyter Notebooks (8 interactive) - Step-by-step tutorials with benchmarks and performance monitoring (8 interactive) - Step-by-step tutorials with benchmarks and performance monitoring Session Guides - Detailed markdown guides for each workshop session - 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 📊 Learning Path Summary Total Duration : 36-45 hours : 36-45 hours Beginner Path : Modules 01-02 (7-9 hours) : Modules 01-02 (7-9 hours) Intermediate Path : Modules 03-04 (9-11 hours) : Modules 03-04 (9-11 hours) Advanced Path : Modules 05-07 (12-15 hours) : Modules 05-07 (12-15 hours) Expert Path: Module 08 (8-10 hours) What You'll Build 🎯 Core Competencies Edge AI Architecture : Design local-first AI systems with cloud integration : Design local-first AI systems with cloud integration Model Optimization : Quantize and compress models for edge deployment (85% speed boost, 75% size reduction) : Quantize and compress models for edge deployment (85% speed boost, 75% size reduction) Multi-Platform Deployment : Windows, mobile, embedded, and cloud-edge hybrid systems : Windows, mobile, embedded, and cloud-edge hybrid systems Production Operations: Monitoring, scaling, and maintaining edge AI in production 🏗️ Practical Projects Foundry Local Chat Apps : Windows 11 native application with model switching : Windows 11 native application with model switching Multi-Agent Systems : Coordinator with specialist agents for complex workflows : Coordinator with specialist agents for complex workflows RAG Applications : Local document processing with vector search : Local document processing with vector search Model Routers : Intelligent selection between models based on task analysis : Intelligent selection between models based on task analysis API Frameworks : Production-ready clients with streaming and health monitoring : Production-ready clients with streaming and health monitoring Cross-Platform Tools: LangChain/Semantic Kernel integration patterns 🏢 Industry Applications Manufacturing • Healthcare • Autonomous Vehicles • Smart Cities • Mobile Apps Quick Start 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. Career Impact 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 Repository Structure 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 Course Highlights ✅ 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. Other Courses Our team produces other courses! Check out: Getting Help If you get stuck or have any questions about building AI apps, join: If you have product feedback or errors while building visit: