Make intelligence useful where data is created
The AIoT Center of Excellence explores how artificial intelligence and connected systems work together in practice. Join the conversation on data quality, model deployment and the operational decisions needed to move AIoT beyond a demonstration.
Executive Summary
The AIoT CoE brings OT/IT, data/ML, product and ecosystem teams together to move AI and IoT from pilots to reliable operations. Its work spans edge and cloud deployment, reference patterns, evaluation and governance for machine learning, vision, forecasting and bounded agents.
Mission and Purpose:
Members share cross-sector use cases, data and domain-knowledge lessons, and evidence from deploying analytics on streaming signals and at the edge. The aim is to move from reactive decisions toward useful predictive and prescriptive action.
Who We Serve
For OT/IT architects, edge and ML engineers, operators and product owners, security and compliance teams, executives, and technology, standards and academic partners.
Charter & Objectives
The charter targets faster pilot-to-production cycles; reliable, offline-capable edge behavior; models and hardware sized to SLA and cost; open interfaces; role-based skills; and safety through policy gates, human approval, rollback and audit trails.
Focus Pillars
- Edge ML lifecycle: version datasets and models, monitor drift, and plan reproducible builds and rollback.
- Computer vision: apply low-latency inspection and safety analysis with privacy controls, calibration and error targets.
- Time-series: use telemetry for anomaly detection, forecasting and operator-ready alerts.
- Optimization: test schedules, routes and setpoints against real constraints before action.
- Digital twins: simulate changes with live data contracts and validation checks.
- Bounded agents: triage and propose actions only within policy, identity, human-approval and audit controls.
Reference Architecture
Architecture: trusted devices and sensors feed segmented wired or wireless networks. Edge runtimes handle adapters, processing and offline operation; data platforms manage streams, history, lineage and models. Enterprise applications turn results into workflows, with policy, evaluation, human approval, rollback and audit across every layer.
Deliverables
Publish reference architectures with design and BOM guidance, policy-to-evidence packs, and practical masterclasses on edge ML, vision, time-series, optimization and governance.
Cadence & Calendar
Monthly 60–90 minute working sessions support design reviews and decisions. Quarterly 2–3 hour showcases share outcomes; IoT Slam anchors deeper discussions and masterclasses.
Participation Levels
Members learn and review; contributors bring use cases and artifacts; workstream leads complete deliverables; chairs and steering members set direction and uphold governance.
How to Get Involved
• Join the AIoTCoE and select a workstream: https://iotcommunity.net/join-us/
• Sponsor a workstream or propose a pilot: sponsors [at] iotcommunity [dot] net
How We Succeed
Success means reusable learning through the knowledge platform, a CoE approach across transportation, manufacturing, industrial maintenance, healthcare and energy, and live conferences with on-demand reach. The goal is measurable AIoT outcomes backed by governance and evidence.
A question worth exploring
What must be measured, maintained and controlled for an AI model to remain useful in a changing operational environment?
Bring your perspective
Bring a use case, an evaluation approach or an experience from deployment. Help others understand the data, infrastructure and organizational choices behind the result.