Edge AI Feasibility Project Template

Purpose

Use this template to design a focused feasibility study that answers whether edge AI (on-device inference) can meet a specific shopfloor need—reducing latency, improving availability, protecting data, or lowering operational cost—without becoming a costly or mis-scoped experiment.

Quick guidance

Keep the scope small and measurable. Run a staged experiment (lab bench → pilot cell → production pilot). Instrument everything and define clear decision gates and rollback triggers before you deploy models to devices.

Template sections and prompts

1. Project hypothesis

State a concise, testable hypothesis. Example: "Running model X on-device will reduce anomaly detection end-to-end latency from 700 ms to under 100 ms and maintain detection precision within 3% of the cloud baseline."

2. Business outcome and success criteria

• Primary business metric(s) (OEE, mean time to detect, scrap reduction, safety event reduction).
• Quantitative success thresholds (e.g., latency < 100 ms; availability > 99.5%; model precision loss < 5%; cost per inference < $0.0005).
• Minimum viable result that justifies scaling.

3. Required signals and sampling

List sensors and data sources required, data formats, sampling rates, and example volumes.

• Examples: camera 30 FPS 640x480 JPEG (~50 KB/frame), vibration sensor 4 kHz, encoder pulses 1 kHz.
• Calculate data volume: sample_rate × bytes_per_sample × active_seconds_per_day × retention_days.

4. Latency targets and measurement method

Define what you mean by latency (inference-only vs end-to-end including preprocessing, decision, and actuation) and how you will measure it (timestamp ingress, inference start, inference end, actuation). Provide measurement scripts or tools.

5. Hardware constraints

List candidate devices, CPU/GPU/TPU availability, memory, storage, network connectivity, power and thermal limits, and expected lifecycle. Note whether devices support model acceleration libraries (e.g., ONNX runtime, TensorRT, EdgeTPU runtime).

6. Data volume & retention

Estimate data storage needs for training, validation, and logs. Decide where training will occur (cloud, on-prem) and whether data leaves the site. Document retention and anonymization needs.

7. Safety, compliance & governance

• Identify safety-critical decisions the model may influence and required fail-safes.
• Data governance: PII, IP, regulatory constraints, and where model artifacts are stored.
• Model governance: versioning, rollback plan, audit trail, and explainability requirements.

8. Evaluation metrics

Choose both technical and operational metrics:

• Technical: inference latency (ms), model accuracy/precision/recall or mAP, model size (MB), memory pressure, CPU/GPU utilization, energy per inference (J).
• Operational: availability (%), percent of decisions made locally, false alarm rate, time-to-detect, cost per inference, maintenance events triggered by false positives.
• Comparison baseline: cloud inference or current manual process.

9. Test plan & phases

1. Discovery & data collection: confirm signals, label sample data, run baseline cloud inference.
2. Bench testing: run model on representative hardware in lab, measure inference time, memory, thermal behavior.
3. Pilot cell (limited production): small number of devices in real operation, close monitoring for X weeks.
4. Production pilot & scale decision: expand if success criteria met; otherwise stop or iterate.

10. Rollback triggers and mitigation

Specify automatic and manual rollback triggers. Example triggers:

• Model precision drops > 5% vs baseline for 24 hours.
• Average end-to-end latency exceeds target by > 20% for the past 1,000 inferences.
• Device CPU temperature > safe threshold for more than 30 minutes or reboots occur > 2 times/day.

Mitigations: revert to cloud inference, disable automation outputs, switch to safe fallback logic, and notify stakeholders.

11. Roles, stakeholders & approvals

List experiment owner, data engineer, ML engineer, OT/controls engineer, plant manager, safety officer, and model governance approver. Define who can stop the trial.

12. Resources, costs & timeline

Estimate staff effort, hardware procurement, cloud training costs, pilot deployment costs, and an expected timeline (discovery 1–2 weeks, bench 2–4 weeks, pilot 4–8 weeks).

13. Deliverables & decision gate

• Deliverables: dataset snapshots, model artifacts, bench test report, pilot telemetry, incident log, final evaluation against success criteria.
• Decision gate: Go/Iterate/Stop based on whether success criteria are met and risks are acceptable.

14. Appendix — example checklist

• Is end-to-end latency defined and instrumented? Yes/No
• Are baseline cloud metrics collected? Yes/No
• Is rollback automation implemented and tested? Yes/No
• Is a data retention and governance plan approved? Yes/No

Notes on common failure modes

Watch for hidden costs (per-device provisioning, field updates), insufficiently representative training data, thermal throttling on device, and underestimating operational monitoring needs. Keep the experiment constrained and insist on measurable gates.

Suggested next step (capability opportunity)

Turn this template into an interactive project intake + results form so teams capture consistent experiment metadata, signals, metrics, and endpoint measurements. Structured submissions enable cross-project comparison and organizational learning.