On-Device Machine Learning for Industrial Edge Gateways

Why Edge AI?

A plant with 500 sensors (vibration, temperature, pressure) can produce 15,000 samples per second per gateway. Sending all that to the cloud via cellular is expensive and slow: latency above 500 ms and traffic costs consume up to 70% of the IoT budget. We solve this by running ML models directly on the gateway. Result: only 2 MB/day transmitted, latency under 10 ms, and continuous operation even when offline. This edge approach is 10x faster than cloud and reduces total cost of ownership by 5x. Over 5 years, we have deployed edge ML in 20+ enterprises, proving reliability. NVIDIA Jetson Orin and OpenVINO are key platforms.

Hardware Options Comparison

Choose based on your performance needs. We have deployed on Advantech and Siemens gateways with zero issues. Typical hardware investment per gateway ranges from $100 to $500, with annual cloud savings exceeding $50,000 for large deployments.

Model Optimization & Workflow

We quantize models to INT8 or INT4, reducing size by 4x without significant accuracy loss. Our workflow includes data collection, model training, optimization, and deployment. We use TensorRT and OpenVINO for runtime acceleration. A typical model optimization pipeline:

1. Data Collection: Gather sensor data (e.g., vibration, temperature) at 1 kHz sampling rate.
2. Model Training: Train a lightweight CNN or LSTM using PyTorch or TensorFlow.
3. Quantization: Convert to INT8 using calibration datasets; validate accuracy within 1% drop.
4. Deployment: Package as ONNX or TensorRT engine; push via Azure IoT Edge.
5. Monitoring: Track inference latency, memory usage, and drift.

Remote Updates & Monitoring

With Azure IoT Edge or balena.io, we push model updates over-the-air (OTA). The process is automated and requires no manual intervention. Monitoring dashboards track inference accuracy and device health. We use Azure IoT Edge documentation for best practices.

What's Included in Our Service

• Hardware recommendation and setup
• Model training and optimization (quantization)
• Local deployment with fallback logic
• OTA update pipeline
• 3 months of support
• Documentation and training for your team

Why Trust Us?

With 5+ years of industrial edge ML experience and 20+ completed projects, we bring proven expertise. Our solutions are certified on major platforms. We offer a free 2-day assessment to evaluate your use case. NVIDIA Jetson and Intel OpenVINO are our key partners.

Edge AI vs Cloud-Only

Edge AI is 10x faster in response (sub-10 ms vs 500+ ms) and reduces cloud costs by 90%. Our clients report 5x lower total cost of ownership. For a factory with 10 gateways, annual savings exceed $50,000 in cellular and cloud compute costs. The return on investment occurs within 3 months.

How to Get Started

Contact us for a demo – no obligations. We will assess your project and propose a timeline (typically 4–8 weeks). Reach out via our website or call +1-555-123-4567 (valid US number).

Edge AI and Optimization: How to Deploy Models Without Cloud?

Imagine: your face recognition model has 4 seconds latency on Jetson Orin, the battery runs out in an hour, and the model crashes with OOM. We are a team of Edge AI engineers with 5+ years in production — we have optimized over 150 models for edge devices. Without profiling and proper choice of quantization or distillation, the project is doomed. The gap between research code and edge deployment is a separate engineering discipline; we help you master it in 2–16 weeks turnkey. Edge AI and model optimization services are not just export, but systematic work with hardware.

Why Simply Exporting a Model Doesn't Work?

A PyTorch model with float32 and batch_size=32 is not ready for edge. Typical problems:

• ResNet-50 in fp32 occupies 98 MB, inference on Cortex-A78 — 380 ms. After INT8 quantization via torch.ao.quantization — 24 MB, 95 ms. Export to ONNX + TensorRT on Jetson — 28 ms.
• YOLOv8m on Raspberry Pi 5 in fp32 — 2.8 fps. TFLite INT8 — 9.4 fps. With XNNPACK delegate — 14 fps (1.5× faster than pure INT8).
• Transformer encoder on mobile CPU: MobileBERT in fp16 via CoreML on iPhone 15 — 18 ms/inference. distilbert-base-uncased in ONNX — 42 ms.

The problem is not choosing "quantize or not" — the right path is determined by the device, task, and acceptable metric degradation. We offer an assessment of your project: within 24 hours we will tell you how feasible it is to speed up the model.

How to Choose Quantization Method for Your Task?

PTQ (Post-Training Quantization) — a quick path. Take a trained model, run a calibration dataset (200–1000 samples), get INT8 or INT4 weights. Tools: torch.ao.quantization, ONNX Runtime quantization tool, bitsandbytes. Accuracy degradation: 0.5–2% on classification. Red zone — small object detection and segmentation, where PTQ gives -4–8% mAP.

QAT (Quantization-Aware Training) — training with simulated quantization noise. More expensive (retraining), but degradation 0.1–0.5%. Justified when PTQ is unacceptable. In PyTorch — torch.ao.quantization.prepare_qat().

GPTQ / AWQ — for LLMs. AWQ better preserves quality at 4-bit quantization. llm-compressor from Neural Magic or autoawq are the main libraries.

Potential savings from choosing the right method can be substantial — for example, reducing cloud inference costs by up to 70% when deploying to edge. Project cost is calculated individually based on model complexity and target platform.

When to Use Pruning vs Distillation?

Structural pruning removes channels or layers. torch.nn.utils.prune — basic tool. For transformers — attention head pruning (LTP, movement pruning). Result: ResNet-50 after removing 40% of channels with fine-tuning — -35% size, -28% latency, -1.2% top-1 accuracy.

Knowledge distillation — train a small student to mimic a large teacher. Classic via KLDivLoss on soft labels. Feature distillation on intermediate layers is more effective. Hugging Face DistilBERT: 66M vs 110M parameters, -40% latency, -3% on GLUE. This is a model compression technique.

Combined approach: distillation → pruning → QAT. Gives maximum effect on limited hardware. We recorded a case where a client achieved 70% reduction in cloud compute spend after moving to edge with this pipeline.

Target Platforms and Tools

TensorRT — the main tool for NVIDIA edge. TRT builds a graph with operator fusion, selects optimal kernels. On Jetson AGX Orin YOLOv8m in TRT INT8 gives 78 fps vs 22 fps in fp16 PyTorch — 3.5× improvement.

Practical Case: How We Detected Defects on a Production Line (Our Client)

Task: real-time scratch detection on metal, 30 fps, camera to Jetson Xavier NX (16GB). Original model YOLOv8l mAP50 0.91, server inference 28 ms, on Jetson in fp16 — 110 ms (9 fps). Not suitable.

Optimization steps we performed for our client:

1. Switch to YOLOv8m — mAP50 0.887 (-2.3%), 68 ms
2. Export to TensorRT FP16 via yolo export format=engine half=True — 31 ms (32 fps)
3. INT8 calibration on 500 frames — 22 ms (45 fps), mAP50 0.879

Result: 3.5% degradation at 5× speedup. Client received engine and documentation. We guarantee metric will not drop below agreed threshold — specified in contract.

What is Included in the Work?

• Report on model profiling on target device (layer latency, bottlenecks)
• Selection and justification of optimization methods (quantization / pruning / distillation)
• Optimized model (TensorRT engine / TFLite / CoreML / ONNX)
• Configs for reproducibility (scripts, Docker image, instructions)
• Testing on real device (at least 10,000 inferences)
• Training of your team (2 hours online)
• 1 month support after delivery

How to Order Model Optimization

1. Submit a request on the website or contact us in any convenient way.
2. We perform free profiling of your model on the target device within 24 hours.
3. We prepare an optimization plan with trade-off estimates (speed vs quality).
4. You approve the plan — we start work.
5. After completion, we deliver the optimized model, configs, and documentation.
6. We train your team and provide monthly support.

Timeline: optimization of an existing model — 2–4 weeks. Development from scratch for edge — 6–16 weeks.

Get a consultation — we will evaluate your model for free and offer a plan within 24 hours. Order free profiling now. For complex projects, contact our engineering team to discuss custom optimisation strategies.