Building a CI/CD Pipeline for On-Device AI Models

Why Do AI Teams Need On-Device CI/CD?

Most AI teams have CI/CD for their code — linters, unit tests, integration tests. But the model itself? It gets tested once on a developer's machine (usually a cloud GPU) and then shipped to production on hardware that behaves completely differently.

This creates a blind spot. A model change that improves accuracy by 0.5% in the cloud might increase latency by 200% on a Snapdragon 8 Gen 3 because a new operator falls back to the CPU. Without on-device testing in CI, you only discover this after deployment.

What Are the Components of an On-Device AI CI Pipeline?

An on-device AI CI pipeline has three components:

1. Trigger: A GitHub Action (or equivalent) that fires on every PR that modifies model files or training code.
2. Orchestrator: A service that compiles the model for target devices, dispatches it to a device fleet, and collects results. EdgeGate handles this via the Qualcomm AI Hub API.
3. Gate: A pass/fail decision based on thresholds you define — inference time and peak memory.

How Do You Define Quality Gates for Edge AI?

Before writing any YAML, decide what "good enough" looks like. Common gates include:

How Do You Create a Pipeline in EdgeGate?

In the EdgeGate dashboard, create a pipeline that specifies:

• Target devices (e.g., Snapdragon 8 Gen 3, Snapdragon 7+ Gen 2)
• Quality gates from the table above
• Number of warmup iterations to exclude (avoids cold-start noise)
• Number of benchmark iterations for statistical confidence

How Do You Add EdgeGate to GitHub Actions?

Add a workflow file to your repository. The workflow authenticates with EdgeGate using HMAC-SHA256, triggers a run with your pipeline and model artifact IDs, and polls for results.

name: EdgeGate Performance Test
on:
  pull_request:
    paths: ['models/**', 'training/**']

jobs:
  edge-test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run EdgeGate Pipeline
        env:
          WORKSPACE_ID: ${{ secrets.EDGEGATE_WORKSPACE_ID }}
          API_SECRET: ${{ secrets.EDGEGATE_API_SECRET }}
          PIPELINE_ID: ${{ secrets.EDGEGATE_PIPELINE_ID }}
          MODEL_ID: ${{ secrets.EDGEGATE_MODEL_ARTIFACT_ID }}
        run: |
          # Authentication + trigger run
          # (see docs/integration for full script)
          curl -s "$API_URL/v1/ci/github/run" \
            -X POST -H "Content-Type: application/json" \
            -H "X-EdgeGate-Workspace: $WORKSPACE_ID" \
            -H "X-EdgeGate-Timestamp: $TIMESTAMP" \
            -H "X-EdgeGate-Nonce: $NONCE" \
            -H "X-EdgeGate-Signature: $SIGNATURE" \
            -d '{"pipeline_id":"'$PIPELINE_ID'",
                 "model_artifact_id":"'$MODEL_ID'",
                 "commit_sha":"'${{ github.sha }}'",
                 "branch":"'${{ github.head_ref }}'",
                 "pull_request":'${{ github.event.number }}'}'

What Do the Results Look Like in Your PR?

When the pipeline completes, results appear as a GitHub PR check. You get a summary table with pass/fail status for each gate, per-device breakdowns, and a link to the full evidence bundle in the EdgeGate dashboard.

If any gate fails, the check blocks the merge. Your team can see exactly which metric regressed, on which device, and by how much. No more "it worked in the cloud" arguments in code review.

What Advanced Features Can You Add?

Once the basic pipeline is running, you can extend it:

• Multi-device matrix: Test across 5 different Snapdragon chipsets simultaneously.
• Flake detection: EdgeGate automatically flags results with high variance and re-runs them.
• Trend tracking: Monitor performance over time to catch gradual regressions.
• Evidence bundles: Ed25519-signed proof that your model passed validation on real hardware.