Master distributed computing at the network edge. Learn to build IoT sensor networks, deploy machine learning models on edge devices, and architect systems that process data locally for real-time response with minimal latency.
Start here to understand edge computing from the ground up—designed for non-engineers, designers, makers, students, and product managers.
Learn the basics of distributed computing, why edge computing matters, and how it complements cloud infrastructure.
Understand low latency, bandwidth efficiency, privacy, reliability, and why these properties make edge computing essential.
Explore edge devices, gateways, servers, communication protocols, and how they work together in a distributed system.
See edge computing in action—from autonomous vehicles to smart cities, healthcare, retail, and industrial IoT.
See edge computing in action—from autonomous vehicles to smart cities.
Infrastructure Gap: In India, "Edge" often means Offline-First. Unlike the West, we can't assume constant backhaul. Designing for the Indian Edge means designing for intermittency.
Curated academic and industry resources on edge computing fundamentals, IoT architecture, distributed systems, and real-world deployments.
Core concepts, architectural patterns, and strategic understanding of when to use edge computing versus cloud-only approaches.
Connecting physical devices with distributed sensors, communication protocols (MQTT, LoRaWAN), and network architectures.
System-level thinking: orchestration, synchronization, fault tolerance, CAP theorem, and edge-cloud data consistency.
Running ML models on edge devices: TinyML, model optimization, quantization, YOLO deployment, and federated learning.
Case studies from smart cities, retail, healthcare, autonomous vehicles, and 5G MEC implementations.
Interactive learning system with 60 flashcards covering edge concepts, IoT fundamentals, distributed architecture, and edge AI.
Edge computing, fog computing, cloudlet, MEC, latency reduction, bandwidth optimization, and offline operation.
Sensors, actuators, MQTT, CoAP, LoRaWAN, NB-IoT, digital twins, OTA updates, and IoT gateways.
CAP theorem, eventual consistency, replication, load balancing, failover, and edge-cloud synchronization.
Model quantization, pruning, TinyML, federated learning, Edge TPU, and hybrid inference patterns.
Smart cities, Industry 4.0, autonomous vehicles, retail analytics, healthcare monitoring, and CDNs.
Hardware, platforms, and tools for building edge computing systems—from Raspberry Pi to NVIDIA Jetson to Edge Impulse.
Raspberry Pi, NVIDIA Jetson, Google Coral, Arduino/ESP32—complete comparison with prices, ML performance, and use cases.
Edge Impulse (highly recommended), TensorFlow Lite, Roboflow, NVIDIA TAO—end-to-end edge ML workflows.
YOLO v5-v10, MobileNet, EfficientNet, MediaPipe, and 2023-2024 models (FastViT, RT-DETR) optimized for edge.
AWS IoT Greengrass, Azure IoT Edge, Google Cloud IoT, Balena fleet management, and Node-RED visual programming.
Hands-on IoT projects, architecture exercises, team scenarios, and production system case studies.
Build multi-node sensor networks, deploy edge AI object detection, create edge-to-cloud pipelines, and train TinyML models.
Cloud vs edge decision frameworks, network topology design, and cost analysis for edge deployments.
Smart building IoT design sprints, industrial edge implementation planning, and deployment logistics workshops.
Analyze AWS Greengrass, Tesla FSD Computer, Google Nest, and Siemens MindSphere production deployments.