This video is a complete guide on Edge Devices, covering interview questions, real-world examples, architectures, security, and AI/ML deployment. It’s designed for embedded engineers, IoT developers, and AI at the edge enthusiasts 🚀
🔹 What you’ll learn in this video: 1️⃣ Fundamentals
What is an Edge Device?
Differences between Edge, IoT, and Embedded Devices
Advantages of Edge Computing vs Cloud-only solutions
Real-world examples of edge devices
2️⃣ Technical / Architecture Questions
Key hardware components of edge devices
Operating systems used (Linux, RTOS, lightweight OS) and why
AI model optimization techniques (compression, quantization, pruning, lightweight frameworks)
4️⃣ Scenario-Based / Design Questions
Designing a video surveillance system with edge devices
Synchronizing data between multiple edge devices and cloud
Behavior of edge devices when cloud connectivity is lost
Using edge devices to reduce bandwidth & latency in industrial IoT
5️⃣ TinyML & STM32 MCU Deployment
What is TinyML and why it matters
Step-by-step process: Data collection → Preprocessing → Model training → Optimization → Deployment (STM32Cube.AI / TensorFlow Lite Micro) → Real-time inference → Testing
Practical STM32 example for running ML models on microcontrollers
6️⃣ Career Guidance
Step-by-step guide for embedded/software engineers starting with edge device development
🎯 Why watch this video?
Covers interview-ready questions & answers in detail
Blends theory + practical real-world applications
Explains TinyML deployment workflow with STM32 example
Helps engineers master Edge + IoT + Embedded + AI at the edge
👉 Perfect for job interviews, hands-on learning, and real-world projects in Edge AI & IoT!
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