E.A C++ and Model Deployment Roadmap

Use this elective when a Python model already works, but latency, memory, packaging, or serving cost becomes the real problem.

See the Deployment Path First

The core question is simple: can you turn model output into a fast, measurable, deployable inference path?

Run the Smallest C++ Inference Step

Create demo.cpp:

#include <iostream>
#include <vector>

int main() {
    std::vector<float> logits = {1.2f, 0.3f, 2.1f};
    int best_index = 0;

    for (int i = 1; i < static_cast<int>(logits.size()); ++i) {
        if (logits[i] > logits[best_index]) {
            best_index = i;
        }
    }

    std::cout << "best_class=" << best_index << "\n";
    std::cout << "score=" << logits[best_index] << "\n";
    return 0;
}

Run it:

c++ -std=c++17 demo.cpp -o demo
./demo

Expected output:

best_class=2
score=2.1

This is the smallest deployment habit: input tensor-like values, compute a decision, print a reproducible result.

Learn in This Order

Step	Lesson	Practice Output
1	E.A.1 C++ Basics	Compile and run a tiny inference helper
2	E.A.2 Advanced C++	Explain ownership, RAII, and safe resource release
3	E.A.3 Optimization	Compare latency, memory, and accuracy trade-offs
4	E.A.4 Inference Engines	Pick an engine based on hardware and model format
5	E.A.5 Edge Deployment	Name edge constraints and export a checklist
6	E.A.6 Model Serving	Design versioned serving with metrics
7	E.A.7 Project	Deliver a small deployment evidence pack

Pass Check

You pass this module when you can compile one C++ example, explain the deployment trade-off, record latency or memory evidence, and connect the result to the Elective Hands-on Workshop.