Edge Computing Fundamentals

Module 1: Introduction to Edge Computing

• Edge computing vs cloud computing: latency, bandwidth, and data sovereignty
• Edge computing architecture: devices, edge nodes, and cloud tiers
• Use cases: autonomous vehicles, smart manufacturing, video analytics, and 5G
• Edge computing platforms: AWS Greengrass, Azure IoT Edge, and Google Anthos

Module 2: AWS IoT Greengrass

• AWS Greengrass v2 architecture: core device, components, and cloud integration
• Deploying Greengrass on edge devices: Linux and containerised deployments
• Greengrass components: Lambda functions, Docker containers, and ML models
• Local messaging: MQTT bridge and inter-process communication

Module 3: Azure IoT Edge

• Azure IoT Edge architecture: IoT Edge runtime, modules, and IoT Hub
• Deploying custom modules: Docker containers as IoT Edge modules
• Edge routing: message routing between modules and to IoT Hub
• Azure Stream Analytics on Edge for local data processing

Module 4: Containerised Edge Workloads

• Docker on edge devices: deploying lightweight containers
• K3s and MicroK8s: lightweight Kubernetes for edge orchestration
• Helm charts for edge application deployment and management
• Multi-node edge clusters: managing distributed edge infrastructure

Module 5: Machine Learning at the Edge

• Edge ML frameworks: TensorFlow Lite and ONNX Runtime
• Model optimisation: quantisation and pruning for edge deployment
• NVIDIA Jetson: GPU-accelerated edge inference for computer vision
• AWS SageMaker Edge Manager and Azure ML edge deployment

Module 6: Edge Networking and Connectivity

• 5G and edge computing: MEC (Multi-access Edge Computing)
• Network protocols for edge: MQTT, CoAP, and OPC-UA
• SD-WAN for reliable and secure edge site connectivity
• Edge data synchronisation: managing intermittent connectivity

Module 7: Edge Security and Management

• Zero-trust security model for distributed edge environments
• Certificate management and device identity at scale
• Remote monitoring and OTA updates for edge deployments
• Observability: logging, metrics, and tracing at the edge

Module 8: Capstone Project and Assessment

• AWS Greengrass or Azure IoT Edge deployment on an edge device
• Containerised ML inference pipeline at the edge
• Edge security configuration and remote management setup
• Final assessment and course certification

Experience in the Industry Learn from edge computing architects who have designed distributed edge infrastructure for smart manufacturing plants, retail analytics deployments, and telecommunications edge computing platforms.

Backgrounds at the Top Our edge computing trainers have worked at cloud providers, industrial IoT companies, and systems integrators delivering edge solutions that bring compute closer to where data is generated.

Clear & Effective Teaching Edge computing architecture, AWS Greengrass, Azure IoT Edge, containerised edge workloads, ML at the edge, 5G MEC, edge security, and remote management are taught with real edge deployment examples.

Hands-On Learning Focus Students deploy AWS Greengrass or Azure IoT Edge, containerise workloads, run ML inference at the edge, configure edge security, and set up remote monitoring through structured hands-on lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest AWS Greengrass and Azure IoT Edge releases, NVIDIA Jetson platform updates, emerging 5G edge use cases, and evolving edge computing architecture best practices.