Edge AI for Industrial IoT

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The full contents of Edge AI for Industrial IoT, slide by slide. Read it here, or use the viewer above for the designed version.

1. 02

   The Business Question

   • AI investments are expanding, but many industrial use cases need decisions in milliseconds, not seconds
   • Cloud-only architectures were built for batch analytics, not line-speed control
   • This briefing frames when edge AI is the right architecture, not just a trend to chase
   • Goal: align OT and IT leadership on where edge AI fits our roadmap
   • Decision points at the end: pilot scope, ownership, and budget ask
2. 03

   Why Cloud-Only AI Breaks Down on the Plant Floor

   • Round-trip latency to a cloud region can exceed the tolerance window for real-time control loops or line-speed inspection
   • High-resolution camera and sensor streams consume significant bandwidth if sent continuously off-site
   • Many plant and remote sites have inconsistent or metered connectivity, especially outside major hubs
   • A network outage should not mean a safety system or quality gate goes blind
   • Cloud remains valuable for training, aggregation, and fleet-wide analytics — the question is where inference should run
3. 04

   What "Edge AI" Actually Means

   • Inference — the act of running a trained model on new data — executes on local hardware near the sensor or machine
   • Model training typically still happens in the cloud or a data center; the trained model is deployed down to the edge
   • Edge devices range from ruggedized industrial PCs to purpose-built AI accelerator boards
   • Local inference means decisions happen in milliseconds, without a round trip to the internet
   • Edge AI is a deployment pattern, not a different category of algorithm — the same model families apply
4. 05

   Where It Applies: Visual Quality Inspection

   • Camera-based defect detection at line speed, where cloud latency would force the line to slow down or buffer
   • Local inference keeps inspection synchronized with conveyor or robotic cycle times
   • Flags parts for rework or rejection in real time rather than after the fact
   • Reduces the volume of image data that needs to leave the site — only exceptions or summaries are sent upstream
   • Works alongside, not instead of, existing manual inspection during initial rollout
5. 06

   Where It Applies: Predictive Maintenance & Safety Monitoring

   • Local anomaly detection on vibration, temperature, or acoustic sensors flags early signs of equipment degradation
   • Edge inference avoids streaming continuous raw sensor data to the cloud for every asset
   • Worker safety monitoring (PPE compliance, restricted-zone intrusion) requires immediate local alerting, not delayed cloud analysis
   • Alerts can trigger local actions — a machine stop, an audible warning — without waiting on network round trips
   • These use cases share a pattern: local speed and privacy first, cloud aggregation second
6. 07

   Edge Hardware Options and Tradeoffs

   • Industrial PCs: flexible, familiar to IT, moderate compute — good fit for lower-throughput inference
   • Edge GPUs: higher compute for demanding vision workloads, higher power and cooling requirements
   • Dedicated AI accelerators (purpose-built inference chips): efficient and compact, but narrower software compatibility
   • Tradeoffs to weigh: compute headroom, power/thermal limits in the plant environment, ruggedization, and vendor lock-in
   • Hardware choice should follow from the specific use case's latency and throughput needs, not be selected first
7. 08

   The OT/IT Convergence Challenge

   • Edge AI devices sit on the OT network but often need IT-style management, patching, and monitoring
   • Network segmentation must be preserved — edge devices should not become a bridge between OT and corporate IT networks
   • Each edge device is a new endpoint and a new attack surface; cybersecurity posture has to extend to the edge fleet
   • OT and IT teams need shared ownership of the edge layer, including clear incident-response responsibility
   • This is as much an organizational design question as a technical one
8. 09

   Deploying and Updating Models Across a Distributed Fleet

   • Models need a defined path from training to validation to staged rollout across sites
   • Fleet management tooling (device registry, version tracking, rollback capability) is a prerequisite, not an afterthought
   • Staged rollout — pilot site, then a wider wave — limits blast radius if a model update underperforms
   • Monitoring model performance in production (drift, accuracy degradation) needs to happen locally and be reported upstream
   • Update cadence should balance improvement velocity against the operational risk of change on a live production line
9. 10

   Illustrative Plant Deployment Example

   • Illustrative scenario, not a verified case study — presented to make the architecture concrete
   • A mid-size assembly plant deploys edge inspection cameras on two high-speed lines, plus vibration sensors on critical rotating equipment
   • Edge devices run inference locally; only flagged exceptions and summary metrics are sent to a central data platform
   • A local edge gateway continues operating quality checks and safety alerts even if the plant's internet link drops
   • Central IT retains visibility into fleet health and model versions without being on the control-loop critical path
10. 11

    Handling Offline and Degraded Connectivity

    • Edge inference should default to continuing operation locally when the network connection is lost or degraded
    • Defined fallback behavior is essential: what happens to alerts, logging, and control actions during an outage
    • Local buffering with later sync ensures data is not lost, only delayed, once connectivity returns
    • Failure modes should be tested deliberately, not discovered during an actual outage
    • This is a reliability requirement to design in from the start, not a feature to add later
11. 12

    Total Cost of Ownership: Edge vs. Cloud-Only

    • Edge adds upfront hardware and deployment cost per site, plus ongoing device maintenance
    • Cloud-only avoids hardware capex but can carry substantial data transfer and compute costs at scale, particularly for continuous video
    • Edge can reduce recurring bandwidth and cloud compute costs once deployed, though payback period varies by use case and site count
    • Total cost comparisons should be treated as directional planning inputs, not precise unnamed benchmarks, until validated against our own pilot data
    • The right architecture is often hybrid: edge for real-time inference, cloud for training, aggregation, and fleet-wide analytics
12. 13

    Integrating with SCADA and MES

    • Edge AI outputs need a defined interface into existing SCADA and MES systems, not a parallel reporting path
    • Common patterns include edge devices publishing alerts or tags that SCADA already knows how to consume
    • MES can log AI-driven quality or maintenance events alongside existing production and genealogy records
    • Integration should preserve existing operator workflows and alarm handling rather than introducing a second system to monitor
    • Early engagement with SCADA/MES vendors and integrators reduces integration risk and rework
13. 14

    Risk Summary Before We Proceed

    • Cybersecurity exposure grows with every new edge endpoint — a security review must be part of any pilot plan
    • Model performance can drift over time; ongoing monitoring is a requirement, not optional overhead
    • Cross-functional OT/IT ownership needs to be assigned before deployment, not resolved after an incident
    • Hardware and integration costs should be validated with a small pilot before any site-wide commitment
    • None of these risks are reasons to avoid edge AI — they are reasons to pilot deliberately
14. 15

    Recommended Next Steps

    • Select one line or one site for a scoped pilot — recommend starting with visual quality inspection given its clear success criteria
    • Form a joint OT/IT working group to own network segmentation, security review, and device management for the pilot
    • Define success metrics and a review checkpoint (target: 8-12 weeks) before any decision to scale
    • Engage existing SCADA/MES integration partners early to scope the data interface
    • The ask: approval to fund a single-site pilot and designate OT/IT co-leads within the next two weeks