How We Scaled a Manufacturing AI Vision Pipeline

Challenge

• Computer vision models drifted between plants because lighting, camera angle, and line speed varied by shift.
• Inference workloads were tightly coupled to line controllers, causing downtime when model updates failed.
• Security and governance controls for training data, model artifacts, and edge deployment were inconsistent.

Our Process

1. 1. Threat Modeling

   We mapped model supply-chain risks, camera ingestion trust zones, and attack paths from edge devices into cloud control surfaces.

   

2. 2. Architecture Redesign

   We introduced a decoupled edge-to-cloud architecture with signed model distribution, event buffering, and plant-level failover.

   

3. 3. Implementation

   Secure model loading and last-known-good rollback patterns were implemented in the .NET edge runtime to reduce line stoppage risk.

   public sealed class SignedModelLoader : IModelLoader
   {
       private readonly IArtifactVerifier _verifier;
       private readonly IInferenceRuntime _runtime;
   
       public async Task<ModelHandle> LoadAsync(ModelArtifact artifact, CancellationToken ct)
       {
           var signatureOk = await _verifier.VerifyAsync(artifact, ct);
           if (!signatureOk)
           {
               throw new SecurityException("Model signature verification failed.");
           }
   
           var handle = await _runtime.LoadAsync(artifact.Path, ct);
           return handle.WithFallback("last-known-good");
       }
   }

4. 4. Testing

   CI included data contracts, model regression checks, edge chaos tests, and infrastructure security gates for every release.

   suite: vision-pipeline-validation
   stages:
     - name: data-contract-tests
       command: pytest tests/contracts --maxfail=1
     - name: model-regression
       command: python tests/regression/run.py --dataset smoke_v3
     - name: edge-chaos-tests
       command: dotnet test tests/EdgeRuntime.Tests --filter Category=Chaos
     - name: security-gates
       command: trivy fs --exit-code 1 .

Results

Team

Led by Feroze Basha, .NET Security Specialist, in partnership with AI platform and MLOps engineers.