Edge Inference Deployment
Models trained in the cloud, deployed back to constrained edge hardware for real-time local inference. AWS IoT Greengrass, NVIDIA Jetson, and industrial PCs — running locally where cloud latency isn't acceptable.
The Challenge
Operational environments that generate high-frequency video or sensor data face a structural problem with cloud-only AI architectures. Streaming raw video from dozens or hundreds of edge locations to the cloud is expensive, bandwidth-constrained, and introduces latency that makes real-time intervention impossible. Many high-value decisions — quality detection on a production line, anomaly detection at a point of transaction, equipment monitoring in a plant — need to happen in milliseconds, not seconds. At the same time, deploying and managing ML models across a distributed fleet of edge devices requires discipline: versioning, rollout controls, monitoring, and the ability to roll back a bad model update before it affects operations.
- • Cloud-only inference latency is too high for real-time operational interventions
- • Streaming high-resolution video or high-frequency sensor data to the cloud creates prohibitive bandwidth and storage costs
- • Edge devices have constrained compute — models must be optimized for the hardware they run on
- • Fleet management across dozens to hundreds of devices requires automated deployment and rollback
- • Shadow mode validation is essential before activating live interventions in production environments
Our Solution
We build the full loop: models trained in the cloud on historical operational data, optimized for edge hardware, deployed via AWS IoT Greengrass with automated rollout controls, and monitored for drift with results synced to the cloud. The architecture supports shadow mode validation — running models in the background against live data before activating any operational intervention.
- ✓ Model training and optimization in AWS SageMaker with TensorRT or ONNX export for edge hardware targets
- ✓ AWS IoT Greengrass V2 component model for packaging, versioning, and OTA deployment to edge device fleets
- ✓ Shadow mode deployment pipeline — models run silently against live data for validation before activation
- ✓ Local inference on NVIDIA Jetson, industrial PCs, or ARM-based edge hardware with GPU acceleration where available
- ✓ IoT Core MQTT telemetry for inference results, device health, and model performance metrics
- ✓ Centralized fleet monitoring with per-device model version tracking and automated rollback on performance degradation
Business Outcomes
- → Real-time inference at the edge eliminates cloud round-trip latency for time-sensitive operational decisions
- → Local processing reduces bandwidth consumption — only inference results and telemetry are sent to the cloud, not raw video or high-frequency sensor streams
- → Shadow mode deployment validates model performance against live operational data before any intervention is activated, reducing deployment risk
- → Automated OTA updates and rollback ensure the fleet stays current without requiring on-site visits
- → Full ownership of the Greengrass component packages, deployment pipelines, and monitoring infrastructure
Getting Started
- 01 Identify one to two high-value use cases where real-time edge inference would have meaningful operational impact
- 02 Inventory existing edge hardware and connectivity — or evaluate hardware options for the target environment
- 03 Assess available labeled training data for the target use case
- 04 Contact us to scope a focused edge AI readiness assessment
Ready to Get Started?
From predictive maintenance on grid infrastructure to renewable forecasting and upstream analytics, we scope engagements honestly and deliver systems your operations team can actually use.