Stage 5: Edge Deployment – Running AI on Digital View Boards
By this stage, we’ve gone from raw, unlabeled data to a fully optimized model. Now comes the payoff: deploying the model at the edge on a Digital View AI board with Pi CM5 and M.2 accelerator.
This is where AI leaves the lab and starts making real-time decisions in the field.
Why Edge Deployment?
Deploying AI on the edge, directly on the board, not in the cloud, delivers:
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Low Latency
Inference happens instantly, enabling safety-critical responses like detecting hazards on a jobsite. -
Bandwidth Efficiency
Only results (e.g., “forklift detected in zone A”) are transmitted, not raw video or sensor streams. -
Data Privacy
Sensitive footage, sound, or sensor readings never leave the site. -
Offline Reliability
Edge AI runs continuously, even without internet access.
How Deployment Works
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Load the Optimized Model
The model from Stage 4 is transferred to the Pi CM5 host environment. -
Accelerator Integration
The model is compiled and loaded into the chosen M.2 accelerator (Hailo, DeepX, Brainchip, or Encharge). -
Runtime Execution
The Pi CM5 orchestrates input (camera, audio, or sensors), runs inference on the accelerator, and outputs actionable results. -
Application Layer
Results are passed into workflows, triggering alerts, logging events, or feeding dashboards.
Example: Jobsite Safety Application
- Cameras feed video into the Digital View board.
- The optimized model detects workers, forklifts, and idle areas.
- Inference happens in milliseconds on the Hailo-8 accelerator.
- The board flags unsafe overlaps between people and moving machinery, sending alerts in real time without any cloud dependency.
The Role of Digital View Boards
The Pi CM5 provides flexible compute and connectivity, while the M.2 slot ensures compatibility with a range of accelerators. Together, they create a compact but powerful platform for edge AI deployment.
This makes Digital View boards not just development tools, but operational intelligence engines: hardware that can continuously run and deliver actionable insights in the field.
Closing Stage 5
Deployment marks the completion of the five-stage AI pipeline:
- Unsupervised Learning – finding patterns.
- Labeling & Dataset Creation – giving meaning.
- Supervised Training – teaching the model.
- Model Optimization – making it edge-ready.
- Edge Deployment – running in the real world.
But wait, there's more...
Iteration
In practice, deployment isn’t the end. Models must adapt as conditions change. That’s why the next step isn’t a stage so much as a cycle: Iteration, feeding field data back into the pipeline to refine and redeploy models over time.
Next Steps
Hopefully you are ready to embark on an AI development!
- Review the Digital View ALC-4096-AIH board
- Contact us to discuss your project
