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Cloud-to-Edge Model Deployment for Low-Latency IoT Applications Using AWS IoT Greengrass V2

Cloud-to-Edge Model

Cloud-to-Edge Model Deployment for Low-Latency IoT Applications Using AWS

  • Use Case: Enable real-time machine learning inference on edge devices (e.g., sensors, gateways, cameras) for IoT applications such as predictive maintenance, industrial automation, and smart monitoring where low latency and offline capability are crucial.

Objective

  • Deploy pre-trained ML models from the cloud to edge devices.

    Enable edge devices to make autonomous decisions without relying on continuous cloud connectivity.

    Ensure low-latency inference and reduce bandwidth costs by processing data locally.

    Securely manage, update, and monitor ML models and components on edge devices.

Project Description

  • This project focuses on deploying and managing machine learning models across edge devices using AWS IoT Greengrass V2. The workflow begins with training ML models in the cloud using Amazon SageMaker or custom frameworks.Once trained, the models are packaged and pushed to edge devices via Greengrass components.

    Edge devices then run the ML inference locally using pre-installed runtime environments (like TensorFlow Lite, PyTorch Mobile, or custom Python code). The results can be logged, acted upon immediately (e.g., trigger an alert), or selectively sent to the cloud for long-term storage or dashboards.

    The setup supports remote updates, device health monitoring, secure communication, and offline operation. Cloud-to-edge synchronization ensures that models and logic are consistently updated across distributed environments.
    • Key Technologies & AWS Services :
    Category Service / Technology Purpose
    Cloud Services Amazon SageMaker Train and build ML models in the cloud
    Cloud Services AWS Lambda Preprocess data and prepare deployment artifacts
    Cloud Services Amazon S3 Store trained models and deployment files
    Cloud Services Amazon CloudWatch Monitor health, logs, and performance of edge devices
    Cloud Services AWS IoT Core Enable secure, reliable messaging and connectivity with IoT devices
    Cloud Services AWS IAM Role-based access control and secure permission management
    Cloud Services Amazon SNS / EventBridge Send notifications or trigger events based on predefined conditions
    Edge Services AWS IoT Greengrass V2 Deploy ML models and logic to edge devices
    Edge Services ML Inference Runtime (e.g., TensorFlow Lite, ONNX) Run predictions locally on the edge device
    Edge Services Local MQTT Broker Enable local messaging between applications on the same edge device
    Edge Services Greengrass Components Modular applications/scripts managed and deployed to edge via Greengrass