Advanced

Over-the-Air Model Updates

Implement reliable OTA model update pipelines with A/B testing, automatic rollback, delta updates, and staged fleet-wide rollouts.

OTA Update Architecture

  1. Model Registry

    Central repository that stores versioned model artifacts with metadata including accuracy metrics, target hardware, and compatibility information.

  2. Update Server

    Manages update scheduling, device targeting, and rollout orchestration. Tracks which devices have which model versions.

  3. Device Agent

    Runs on each edge device. Checks for updates, downloads new models, validates integrity, and performs the model swap.

  4. Rollback Manager

    Monitors model performance after update. Automatically reverts to the previous version if accuracy drops or errors exceed thresholds.

A/B Testing on Edge

Deploy new models to a subset of your fleet first. Compare accuracy, latency, and error rates between the control group (old model) and test group (new model) before full rollout.

YAML - Staged Rollout Config
rollout:
  model_version: "detector-v3.2"
  stages:
    - name: canary
      target_percent: 5
      duration_hours: 24
      success_criteria:
        accuracy_drop_max: 0.02
        error_rate_max: 0.001
        latency_p99_max_ms: 50
    - name: early_adopters
      target_percent: 25
      duration_hours: 48
    - name: general
      target_percent: 100
  rollback:
    auto_rollback: true
    health_check_interval_sec: 300

Delta Updates

Instead of transferring the entire model on every update, delta updates compute and transmit only the differences between model versions. This can reduce update size by 80-95% for incremental model improvements, critical for devices on metered cellular connections.

Update Safety Patterns

🔄

Dual-Bank Storage

Keep two model slots on device. Update the inactive slot while the active model continues serving. Atomic swap only after validation passes.

✅

Integrity Verification

Sign model artifacts and verify signatures on device before loading. Prevents corrupted or tampered models from being activated.

🚨

Health Monitoring

Run a validation dataset on the new model after deployment. Auto-rollback if accuracy, latency, or error metrics fall outside defined thresholds.

✅
Best practice: Never update all devices simultaneously. Use staged rollouts starting with 5% of your fleet. Monitor for 24 hours before expanding to 25%, then 100%. Always maintain the ability to rollback to the previous model version within seconds.