Running Edge AI with AWS IoT Greengrass and SageMaker Neo

OneData Software offers an Edge AI solution that leverages AWS IoT Greengrass combined with SageMaker Neo to bring intelligent inference closer to devices. This enables organizations to run ML models locally on edge devices or gateways with optimized performance, even in environments with intermittent connectivity or bandwidth constraints. Their solution covers the full lifecycle such as model training, optimization, deployment, monitoring, updates, and security.

Core Functionalities

1. Model Training & Optimization Models are trained in the cloud (e.g. using AWS SageMaker), then optimized with SageMaker Neo to produce compiled, efficient versions that suit various edge hardware (e.g. ARM64, NVIDIA Jetson, embedded devices).

2. Deployment to Edge with Greengrass Using AWS IoT Greengrass, OneData packages and deploys the optimized model to edge devices or gateways. Greengrass also supports local inference, local messaging / communication, and management of the runtime environment.

3. Edge Inference & Real-Time Processing Edge devices can perform inference locally with minimal latency, handling tasks such as anomaly detection, predictive alerts, image or sensor processing, etc., without needing to always rely on the cloud.

4. Offline / Intermittent Connectivity Handling Greengrass allows devices to operate when disconnected from the cloud, buffering events, and synchronizing when connectivity is restored.

5. Secure Model Management & Update Workflow Ensuring that models are cryptographically signed, versioned, and securely transmitted. Using Greengrass’s security features (certificates, IAM policies), and OneData helps manage updates (OTA), monitor performance drift, and rollback if needed.

6. Monitoring, Logging, and Telemetry Edge devices send back telemetry about inference latency, errors, resource usage (CPU/GPU/Memory). It can be aggregated and visualized. Alerts if model performance degrades.

7. Scalability and Hardware/Device Diversity Support The solution supports different classes of edge devices from small sensors up to powerful gateways. It ensures that optimized models run efficiently across hardware types.

8. Integration with Cloud Analytics & Feedback Loop Insights from edge inference feed back into cloud analytics / ML retraining. Data from edge is used to further improve models, detect new patterns, etc.

Benefits • Reduced latency for mission-critical use cases (e.g. healthcare monitoring, industrial automation) by doing inference close to source. • Lower bandwidth usage and cost, because only necessary data or summaries are sent to the cloud. • Improved resilience: edge devices keep functioning even during connectivity outages. • Enhanced security by controlling what models are deployed, securing edge compute, performing signed updates. • Faster decision making and real-time response to local events.