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X-Edge AI

Operating Best Practices

Field-tested guidance for keeping a Xisom box healthy in production. Each section links to the reference page with the full detail. Sections marked Placeholder are being finalized with field data — treat them as direction, not settled numbers.

The operating loop

Section titled “The operating loop”
flowchart LR
  P["Prepare<br/>modelctl"] --> D["Deploy<br/>upload + pair + activate"]
  D --> M["Monitor<br/>latency · throughput · EP"]
  M -->|"drift / new data"| P
  M -->|"version up"| D

Model preparation

Section titled “Model preparation”
  • Validate against the box’s ONNX Runtime version before shipping — pin --target-ort so a too-new opset fails on the workstation, not on the floor.
  • Quantize deliberately. Dynamic INT8 makes models smaller and usually faster, at some accuracy cost — re-validate the quantized model and check prediction quality before it goes live.
  • Pin reproducible bundles (--timestamp) so the artifact on the box matches what you reviewed. See Preparing Models.

Datasource sizing

Section titled “Datasource sizing”
  • Match window size × feature count between model and datasource — the platform rejects a mismatched pairing. Record these two numbers with every model.
  • Prefer OPC-UA subscriptions over polling where the server supports them.
  • Use CSV replay to validate a model against recorded data before connecting a live plant source. See Input Datasources.

Monitoring & alerting

Section titled “Monitoring & alerting”
  • Watch p95/p99 latency, not just the average — tail latency is where edge inference degrades first.
  • An execution-provider fallback to CPU is a regression signal — if a box that should run on TensorRT/CUDA reports CPU, investigate before throughput drops.
  • Live metrics retain for 3 days — export anything you need for longer-term trending. See Monitoring.

Deployment & versioning

Section titled “Deployment & versioning”
  • Keep frontend and backend versions in lockstep — a drift triggers the version-mismatch banner. See Versions & Updates.
  • Stage on the Docker lab before promoting a model or release to a production box. See Docker Lab.

Security

Section titled “Security”
  • Apply least privilege — reserve admin accounts for actions that need them (output test writes, key management); operators sign in as operators.
  • Review the audit trail periodically — it records who accessed the system and when.
  • Terminate TLS at the reverse proxy in front of the dashboard and API, and use authenticated brokers / secured OPC-UA policies for datasources. See Security.

Next steps

Section titled “Next steps”
Preparing Models Convert, validate, quantize, package.
Monitoring Read the inference dashboard.
Security Access control, hardening, audit.