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Minimum operational requirements

• Hardware: CPU with SIMD support or low-power GPU; 4+ GB RAM (edge) / 16+ GB (server).
• Software: Linux (Ubuntu 20.04+), Python 3.9+, required libs (NumPy, PyTorch/TensorFlow runtime matching ai variant).
• Network: optional: <100 ms latency for remote model calls.

Purpose & use cases

• Use as an AI-optimized module/component in inference pipelines.
• Use cases: low-latency edge inference, higher-accuracy classification, quality-sensitive production systems.

B. First‑Boot & Network Configuration

1. Power on; the device will boot to the Setup Wizard on its local IP (default 192.168.100.10).
2. Connect a laptop to the same LAN, navigate to the IP in a browser, and follow the wizard:
   • Set a static IP or enable DHCP.
   • Choose Wi‑Fi 6 SSID/password (if using wireless).
   • Create an admin account (strong password, 2‑FA recommended).

4️⃣ Setting Up the Device (Step‑by‑Step)

Extra-quality checklist (to achieve “extra quality”)

1. Input validation: strict schema checks, normalization, and sanitization.
2. Preprocessing: deterministic transforms, fixed random seeds.
3. Calibration: run calibration dataset to tune thresholds and confidence scaling.
4. Ensemble/aggregation: where possible, use small ensembles or test-time augmentation to boost accuracy.
5. Quantization-aware training / Mixed precision: preserve accuracy while improving efficiency.
6. Robustness tests: adversarial/noise augmentation, out-of-distribution detection.
7. Monitoring: real-time drift detection, latency and error-rate alerts.
8. CI/CD for models: automated unit, integration, and regression tests with performance budgets.
9. Documentation & versioning: explicit changelogs tied to mr015811 serial and min-specs.
10. Security & access control: signed artifacts, integrity checks.