E.A.7 Deployment Integrated Project

This project is not about training the biggest model. It is about proving that you can turn a model into a small, measurable, deployable system.

Build a simple project story:

Lightweight image classification service with local inference, batching, metrics, and an edge-device readiness check.

What You Need

• Python 3.10+
• No external packages
• One small model idea, real or simulated
• One target device, such as a laptop CPU, Raspberry Pi, Jetson, or cloud CPU instance

Delivery Checklist

Your final project should show:

1. Target device and engine choice
2. Input and output examples
3. Baseline vs optimized metrics
4. Serving or batch-processing flow
5. Known failure cases
6. Reproduction commands

Run A Project Readiness Score

Create deployment_project_check.py:

project = {
    "name": "lightweight-image-classifier",
    "target_device": "edge-c",
    "engine": "ONNX Runtime",
    "baseline": {"latency_ms": 120, "memory_mb": 820, "accuracy": 0.904},
    "optimized": {"latency_ms": 68, "memory_mb": 430, "accuracy": 0.899},
    "evidence": ["README.md", "metrics.csv", "failure_cases.md"],
}

checks = {
    "latency_under_80": project["optimized"]["latency_ms"] < 80,
    "memory_under_512": project["optimized"]["memory_mb"] < 512,
    "accuracy_drop_ok": project["baseline"]["accuracy"] - project["optimized"]["accuracy"] <= 0.01,
    "has_failure_cases": "failure_cases.md" in project["evidence"],
}

for name, passed in checks.items():
    print(name, passed)

release_candidate = all(checks.values())
print("release_candidate:", release_candidate)
print("evidence_files:", project["evidence"])

Run it:

python deployment_project_check.py

Expected output:

latency_under_80 True
memory_under_512 True
accuracy_drop_ok True
has_failure_cases True
release_candidate: True
evidence_files: ['README.md', 'metrics.csv', 'failure_cases.md']

This is the shape of a presentable deployment project: not just code, but evidence.

How To Present The Project

Use this order:

1. Problem: what needs to run, where, and why.
2. Constraints: memory, latency, hardware, offline requirement.
3. Design: model format, engine, serving path.
4. Evidence: before/after metrics and failure cases.
5. Trade-off: what you did not optimize yet and why.

Common Mistakes

• Showing only a demo interface and no metrics.
• Optimizing latency but hiding the accuracy drop.
• Claiming edge readiness without a memory or long-running test.
• Making the project too broad, such as cloud, mobile, and edge all at once.

Practice

Add a second target device and rerun the readiness checks. Then write three README lines that explain why the chosen device and engine are reasonable.