Seasoned Software Architect | Senior Software Engineer | Logistics | Power Distribution & Smart Metering | Collaboration with Reliance IBM | TCS
🚀 Cracking AI & Data Science Interviews Topic: Edge AI – Bringing Intelligence to Devices Quick Insight: Edge AI enables machine learning models to run directly on devices like smartphones, IoT sensors, and industrial machines, reducing latency, bandwidth use, and dependency on cloud infrastructure. --- 1️⃣ Core Concepts: On-Device Inference: Perform predictions locally without sending data to the cloud. Model Compression: Techniques like pruning, quantization, and knowledge distillation to make models lightweight. Energy Efficiency: Optimizing computation for low-power devices. Real-Time Processing: Immediate responses for time-critical applications. --- 2️⃣ Key Techniques & Tools: TensorFlow Lite / TensorRT / ONNX Runtime for deploying optimized models. Pruning & Quantization: Reduce model size while maintaining accuracy. Federated Learning: Train models across devices without centralizing data. Edge Accelerators: GPUs, TPUs, and NPUs designed for edge computation. --- 3️⃣ Use Cases: ✅ Smartphones: Voice assistants, camera enhancements. ✅ IoT & Industrial Automation: Predictive maintenance, anomaly detection. ✅ Autonomous Vehicles: Real-time object detection and navigation. ✅ Healthcare Devices: Portable diagnostic tools, patient monitoring. ✅ Retail: On-device customer behavior analysis and personalized recommendations. --- 4️⃣ Interview Questions to Expect: “What are the challenges of deploying AI on edge devices?” “Explain model compression techniques and their trade-offs.” “How does federated learning work?” “Why is Edge AI important for real-time applications?” “Give examples of Edge AI in industry or consumer devices.” --- 🔥 Trending Insight: With the rise of IoT and 5G, Edge AI is becoming crucial for privacy-preserving, low-latency, and scalable AI applications across industries. #EdgeAI #FederatedLearning #IoT #MachineLearning #DeepLearning #AI #InterviewPrep
