AI & Edge Computing

Turn data at the edge into decisions deploy models where the work happens, govern them centrally, and prove ROI with real‑world latency, accuracy, and uptime.

AI at the edge demands ruggedised hardware, tight MLOps, and smart data pipelines. This hub explores model placement, inference at the edge, fleet‑wide updates, and how to secure, observe, and monetise AI outside the data centre.

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What you’ll learn

Model placement: when to run inference at the edge vs in the core.
Fleet MLOps: packaging, signatures, staged rollouts, and rollbacks.
Observability: the metrics that show real‑world performance and cost.
Safety and governance: data boundaries, model confidence, human‑in‑the‑loop.

Practical guides

Edge inference playbook

Decide what runs on the edge vs in the core so you cut latency without exploding costs.

What’s inside:

Placement rules by latency, privacy, and bandwidth.
Data pipeline: capture → pre‑process → on‑device inference → summarise to core.
Safety nets: confidence thresholds, drift detection, and a known‑good fallback model.

View the playbook

MLOps for fleets

Update models like software—safely, in stages, across thousands of devices.

What’s inside:

Signed model packages; staged rollouts 1% → 10% → 100%.
Telemetry to watch: accuracy deltas, update success, device health.
Rapid response: auto‑rollback and quarantine for failing devices.

See rollout steps

Observability at the edge

Decide what runs on the edge vs in the core so you cut latency without exploding costs.

What’s inside:

Metrics to track: inference p50/p95, success rate, CPU/battery, bandwidth.
Logging discipline: minimise PII, hash IDs, sample bursts.
Alerts that work: local watchdogs for instant action; central SLOs for trends.

Open the metrics checklist

FAQs

How do we choose edge vs core?

Run at the edge when milliseconds matter, privacy is strict, or links are unreliable; centralise heavy training and cross‑site analytics.

How do we secure edge AI?

Use signed model artifacts, device identity, least privilege, and zero‑trust networking; treat updates as change‑controlled releases.

What metrics prove ROI?

p95 latency, in‑the‑wild accuracy, cost per decision, update success rate, and device health.