Microsoft's "EdgeAI for Beginners":Learn How to Run AI Models Locally on Devices (github.com)

Microsoft published "EdgeAI for Beginners," a hands-on, multi-language course and open-source GitHub repo (github.com/microsoft/edgeai-for-beginners) that teaches developers how to run AI models locally on edge devices. The curriculum spans fundamentals to production: small language model (SLM) foundations, hardware-aware optimization, real-time privacy-preserving inference, SLMOps, multi-agent systems, and deployment samples (Windows 11 app, Jetson/mobile targets, RAG pipelines). The full workshop suite includes 50+ examples, Jupyter notebooks, validation scripts, and a Foundry Local toolkit—total learning time ranges from focused 20–30 hour paths up to 36–45 hours for the full program. Microsoft also provides community support via the Azure AI Foundry Discord. Technically, the course emphasizes SLMs and edge-tailored optimization: families like Phi, Qwen, Gemma and distilled variants (e.g., Mistral-7B) with quantization and compression workflows that claim up to ~85% speed boosts and ~75% size reductions. Tooling coverage includes llama.cpp, Microsoft Olive, OpenVINO and Apple MLX, plus production patterns for model routing, benchmarking, streaming chat, local RAG, and multi-agent orchestration. This lowers barriers for privacy-sensitive, low-latency, and cost-efficient on-device AI—making it easier for ML engineers and edge architects to prototype, benchmark, and deploy real-world edge-first systems.