The Deployment Gap Starts After Model Export

A PyTorch, ONNX, or TensorFlow model may work well during development. But embedded deployment changes the conditions. The question is no longer only whether the model can be converted. It is whether the generated C code can run under the constraints of the final target:

• limited memory
• predictable runtime behavior
• target-specific toolchains
• architecture-dependent performance
• integration into existing C environments

This is where simple conversion reaches its limits. Generated C code may still contain temporary buffers, inefficient data access, complex control flow, or missed opportunities for target-specific optimizations.

That is the step the emmtrix Edge AI Compiler is built for: moving from ML model input to target-aware C code generation at compiler level.

Between model export and embedded execution, this is where deployment work becomes concrete.