Production Checklists, Architecture Diagrams, and Troubleshooting Playbooks

Why production artifacts matter for Edge AI

In Edge AI, “production” is not just shipping a model file. It is the repeatable ability to build, test, release, observe, and recover across many device variants, network conditions, and user environments. Three artifacts make this repeatability practical: production checklists (to prevent omissions), architecture diagrams (to align teams and surface hidden dependencies), and troubleshooting playbooks (to reduce time-to-recovery when something breaks). This chapter focuses on how to create these artifacts so they are actionable for engineers and operators working with on-device inference systems.

Production checklists: turning “tribal knowledge” into gates

A production checklist is a set of verifiable items that must be satisfied before a release is allowed to proceed. The goal is not bureaucracy; it is to convert common failure patterns into explicit gates. In Edge AI, checklists should be split by lifecycle stage (build-time, release-time, and post-release) and by responsibility (ML, mobile/firmware, backend, security, QA). Each item must be phrased so it can be answered with evidence: a link to a test report, a CI job, a configuration diff, or a signed approval.

Checklist design principles

Use “binary” language: pass/fail, present/absent, measured/not measured. Avoid vague items like “model looks good.” Prefer “Top-1 accuracy on evaluation set X is at least Y” or “Memory peak measured on device class A is below B MB.” Keep the checklist short enough to be used, but complete enough to block known bad releases. When an incident happens, add a new checklist item only if it would have prevented or detected the issue earlier.

Release readiness checklist (template)

The following template is intentionally practical. Adapt thresholds and device classes to your product, but keep the structure stable so teams learn it.

• Artifact integrity: Model file hash recorded; runtime package version pinned; build is reproducible from a tagged commit.
• Compatibility matrix: Verified on each supported device class and OS/firmware version; known unsupported variants explicitly blocked via feature flags.
• Configuration freeze: Inference settings (input size, normalization, thresholds, label map, post-processing) are versioned and bundled; no “remote tweak” without audit trail.
• Resource budgets: Peak RAM, steady-state CPU/NPU utilization, and storage footprint measured on representative devices; results attached.
• Thermal and long-run stability: Soak test completed (e.g., 2–8 hours) with no memory growth, no watchdog resets, no thermal throttling beyond acceptable limits.
• Offline behavior: Device behavior verified under airplane mode / captive portal / intermittent connectivity; no blocking calls on critical paths.
• Failure handling: Corrupt model file, missing permissions, unavailable camera/mic/sensor, and low-storage scenarios tested; app degrades gracefully.
• Logging and diagnostics: On-device logs include model version, runtime version, device class, and key performance counters; log volume bounded.
• Security review: Model and config signing verified; rollback protection and update authenticity checks validated; secrets not embedded in binaries.
• Rollout plan: Staged rollout configured; canary cohort defined; rollback procedure rehearsed; success metrics and alert thresholds set.
• Support readiness: Playbook links available; on-call rotation aware; customer support has a symptom-to-action guide.

Step-by-step: building a checklist gate in CI/CD

To make the checklist real, implement it as an automated gate with a small number of manual sign-offs. A practical approach is to represent checklist items as machine-readable “release evidence” generated by CI jobs.

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• Step 1: Define evidence artifacts: Decide what files prove readiness (e.g., benchmark.json, compatibility.csv, signing_report.txt, soak_test.log).
• Step 2: Add CI jobs that produce evidence: For each device class, run a benchmark suite and export metrics; run unit tests for pre/post-processing; run packaging/signing verification.
• Step 3: Create a release manifest: Generate a single manifest (release_manifest.json) that includes model hash, config version, runtime version, and links to evidence artifacts.
• Step 4: Enforce thresholds: Add a “policy check” job that reads the manifest and fails if any metric is out of bounds or missing.
• Step 5: Require minimal human approvals: Keep manual steps limited to risk-based approvals (e.g., security sign-off when cryptographic changes occur).

{ "model_sha256": "...", "runtime": "tflite-2.15", "devices_tested": ["A", "B"], "benchmarks": { "A": {"p95_ms": 18.2, "peak_ram_mb": 62.1}, "B": {"p95_ms": 24.7, "peak_ram_mb": 71.4} }, "signing": {"verified": true}, "soak_test_hours": 6 }

Architecture diagrams: make the invisible visible

Architecture diagrams are not decoration; they are operational tools. In Edge AI, failures often occur at boundaries: sensor input, pre-processing, model invocation, post-processing, storage, networking, and update mechanisms. A good diagram makes boundaries explicit, shows ownership, and highlights what happens when components fail. The most useful diagrams are “living” documents tied to the repository and updated as part of change reviews.

Three diagram types you should maintain

Maintain three complementary views. Each answers a different question and prevents a different class of mistakes.

• Context diagram: What external systems and actors interact with the device? Useful for privacy/security reviews and integration planning.
• Container/component diagram: What are the major software components on-device and off-device, and how do they communicate? Useful for performance and reliability analysis.
• Sequence diagram for critical flows: What happens step-by-step during inference, update, and error handling? Useful for troubleshooting and regression prevention.

What to annotate on Edge AI diagrams

Edge AI diagrams should include annotations that are often omitted in typical web diagrams. Add: data types (raw frames, embeddings, events), data retention (in-memory only vs persisted), trust boundaries (secure enclave, OS sandbox), and failure modes (what happens if the model fails to load, if permissions are denied, if storage is full). Also annotate versioning points: where model/config versions are read, cached, and reported.

Example: component diagram (textual)

Even if you use a diagramming tool, keep a textual representation in the repo so it can be reviewed in code. The following is a structured outline you can convert into a diagram.

• Device
  • Sensor Interface (camera/mic/IMU) → Frame/Chunk Buffer
  • Pre-processing Module (resize, normalize, feature extraction)
  • Inference Runtime Wrapper (loads model, allocates tensors, invokes)
  • Post-processing Module (thresholding, smoothing, NMS, business rules)
  • Event Store (bounded queue; optional persistence)
  • Diagnostics (metrics, logs, crash reports)
  • Update Agent (downloads signed packages; verifies; activates)
• Backend (optional)
  • Release Service (serves signed model/config packages)
  • Telemetry Ingest (receives aggregated metrics/events)
  • Feature Flag Service (controls rollout cohorts)

Step-by-step: creating a diagram that survives production

Many diagrams become stale because they are created once and forgotten. Use a process that forces updates.

• Step 1: Choose a single source of truth: Store diagrams as text (e.g., Mermaid) or as versioned files in the repo.
• Step 2: Add “diagram impact” to PR templates: Any change to data flow, storage, permissions, or update logic must include a diagram update or an explicit “no change” justification.
• Step 3: Review diagrams like code: Require reviewers from at least two roles (e.g., ML + mobile/firmware) for changes touching inference paths.
• Step 4: Link diagrams to runbooks: Each box in the diagram should map to an owner and to troubleshooting steps.

Troubleshooting playbooks: reduce MTTR on device fleets

A troubleshooting playbook (or runbook) is a structured set of diagnostic steps for a known symptom. In Edge AI, the same symptom can have multiple causes: a performance regression might be a model change, a pre-processing bug, a runtime update, an OS permission change, or a device-specific driver issue. Playbooks help responders avoid guesswork by prescribing what to check first, what evidence to collect, and what mitigations are safe.

Playbook structure that works in practice

Use a consistent structure so responders can scan quickly under pressure.

• Symptom: What the user or monitoring system observes (e.g., “inference latency spikes,” “no detections,” “app crash on startup”).
• Impact: What functionality is degraded and what user segments are affected.
• Immediate mitigations: Safe actions to reduce harm (disable feature flag, rollback model, reduce sampling rate).
• Diagnostics: Ordered checks with commands, log keys, and expected ranges.
• Root cause candidates: Common causes mapped to confirming signals.
• Resolution: Permanent fix steps and validation tests.
• Post-incident actions: Add checklist items, tests, or alerts to prevent recurrence.

Playbook 1: “Model fails to load” on some devices

Symptom: Feature silently disables, or logs show “failed to allocate tensors” / “invalid model.” Immediate mitigation: Roll back to last known good model for affected cohort; disable rollout for device class.

Diagnostics (step-by-step):

• Step 1: Confirm versions: Collect model hash, config version, runtime version, OS version, device model identifier from logs.
• Step 2: Check signature/verification: Verify that signature validation succeeded; look for “signature mismatch” or “certificate expired.”
• Step 3: Check file integrity: Compare downloaded file size and hash to release manifest; look for partial downloads or storage write failures.
• Step 4: Check memory allocation: Inspect peak RAM at load time; confirm whether failure correlates with low-memory devices or background pressure.
• Step 5: Check operator support: If runtime reports unsupported ops, compare model build settings to runtime capabilities for that device class.

Root cause candidates: corrupted download, signing key rotation issues, model built with unsupported operators, insufficient contiguous memory, wrong config pointing to incompatible model. Resolution: fix packaging pipeline, adjust model build target, add device-class gating, or reduce model memory footprint; add a preflight “load test” per device class in CI.

Playbook 2: “Accuracy drop” reported after release

Symptom: Users report misclassifications; business metrics degrade; no obvious crashes. Immediate mitigation: Roll back model/config for impacted cohort; if safe, increase threshold to reduce false positives while investigating.

Diagnostics (step-by-step):

• Step 1: Verify config parity: Confirm label map, thresholds, and post-processing parameters match the intended release; check for stale cached config.
• Step 2: Check input pipeline changes: Confirm sensor format, orientation, sample rate, or normalization constants did not change in the app/firmware release.
• Step 3: Segment by device class: Determine whether degradation is isolated to certain chipsets, camera modules, or OS versions.
• Step 4: Reproduce with a golden test set: Run a small on-device “golden” dataset (stored as test assets) through the full pipeline and compare outputs to expected ranges.
• Step 5: Inspect post-processing: Validate thresholding, smoothing windows, and any business rules; many “accuracy” regressions are post-processing regressions.

Root cause candidates: mismatched label ordering, wrong normalization, changed sensor orientation, post-processing bug, config rollout mismatch. Resolution: fix config versioning and caching, add end-to-end golden tests, and add a checklist item requiring a pipeline checksum (pre-processing + post-processing) to match the release manifest.

Playbook 3: “Latency spikes and UI jank” on mid-tier devices

Symptom: P95 inference time increases; UI frame drops; battery complaints. Immediate mitigation: Reduce inference frequency via remote config; disable concurrent workloads; roll back if regression is severe.

Diagnostics (step-by-step):

• Step 1: Identify contention: Check whether inference runs on the UI thread or competes with camera capture; confirm thread priorities and scheduling.
• Step 2: Compare warm vs cold runs: Determine whether spikes occur during model load, first inference, or steady state.
• Step 3: Check memory pressure: Look for GC spikes, allocation churn, or buffer reallocation; confirm tensor buffers are reused.
• Step 4: Verify hardware path: Confirm the intended accelerator delegate/provider is actually active on affected devices; detect silent fallback to CPU.
• Step 5: Correlate with thermal state: Check whether throttling coincides with spikes; confirm long-run behavior under typical ambient conditions.

Root cause candidates: thread misuse, buffer churn, delegate fallback, thermal throttling, background services contention. Resolution: move inference off UI thread, implement buffer pools, enforce delegate selection with explicit checks, and add a checklist item requiring a “delegate active” metric per device class.

Playbook 4: “Update succeeded but behavior didn’t change”

Symptom: Device reports it updated, but outputs match old model; support sees inconsistent versions. Immediate mitigation: Pause rollout; force revalidation of active model/config on startup; if needed, trigger re-download.

Diagnostics (step-by-step):

• Step 1: Confirm active vs downloaded versions: Log both “downloaded package version” and “active loaded model hash.” Many systems download but fail to activate.
• Step 2: Check activation conditions: Verify required conditions (battery level, Wi-Fi only, idle state) were met; confirm activation scheduling.
• Step 3: Check rollback logic: Determine whether the system auto-rolled back due to load failure or health checks.
• Step 4: Check caching layers: Ensure the runtime wrapper is not holding an old model in memory; confirm restart/reload behavior.
• Step 5: Validate manifest consistency: Confirm the config points to the correct model and that both are from the same release bundle.

Root cause candidates: activation bug, health-check rollback, stale in-memory model, mismatched model/config bundle. Resolution: implement explicit “active model hash” reporting, add activation state machine tests, and add a checklist item requiring a simulated update + activation test in CI.

Operational tooling that makes checklists and playbooks usable

These artifacts become far more effective when paired with lightweight tooling. The key is to make evidence collection and symptom triage fast, consistent, and safe on user devices.

Golden diagnostic bundle

Create a small “diagnostic bundle” that can be shipped in debug builds or enabled for internal cohorts: a set of known inputs (images, audio clips, sensor traces) and expected output summaries. The playbook can instruct responders to run the bundle and compare results. Keep it small, versioned, and representative of common edge cases (low light, noise, motion blur, different accents, etc.).

Standard log keys and metric names

Playbooks depend on consistent observability. Standardize a minimal set of keys across platforms: model_hash, config_version, runtime_version, delegate_active, load_time_ms, p50_ms, p95_ms, peak_ram_mb, dropped_frames, thermal_state, and error_code. Make these keys part of the checklist so releases cannot ship without them.

Incident tags that map to owners

In architecture diagrams, each component should have an owner. In playbooks, each error code or symptom should map to an owner group (mobile, firmware, ML, backend). This prevents “ping-pong” during incidents and clarifies who can approve mitigations like disabling a feature or rolling back a package.

Putting it together: a practical workflow for teams

The three artifacts reinforce each other. Architecture diagrams define components and boundaries; checklists enforce that each boundary is tested and observable; playbooks use the same component names and log keys to guide responders. A practical workflow is to treat them as part of the release deliverable: every release includes an updated diagram (if data flow changed), an updated checklist evidence manifest, and playbook updates for any new error codes or new device classes.

Step-by-step: integrating artifacts into a release cycle

• Step 1: Before implementation: Update the architecture diagram for the proposed change; identify new boundaries and failure modes.
• Step 2: During implementation: Add log keys/metrics for new components; add tests that will later become checklist evidence.
• Step 3: Before rollout: Generate the release manifest with evidence; run the checklist gate; ensure playbooks mention any new symptoms or error codes.
• Step 4: During rollout: Use playbooks for any anomalies; record what evidence was missing and convert that into new checklist items.