ONNX specialists for your dedicated team.

Where ONNX fits in a long-term product team.

Separating training from serving is the architectural value. A model can be trained in PyTorch and served through a runtime optimised for inference, which is often faster, has fewer dependencies and runs on platforms the training framework doesn't support.

Export is not always clean. Models using unsupported operators, dynamic control flow or custom layers may fail to export or export with subtly different behaviour. Verifying numerical equivalence between the original and the converted model is a necessary step, not a formality.

What your dedicated team does with ONNX.

The deployment benefits are real where the target is constrained. Edge devices, mobile and environments where installing a full training framework is impractical are all cases where a portable format is what makes deployment possible at all.

Establishing the target constraints before choosing the training approach is basic sequencing, part of the scoping described in how an assignment runs.

Common ONNX use cases.

• Training and serving decoupled A model trained in one framework and served through an optimised runtime.
• Equivalence verified after export Numerical output compared, because conversion can change behaviour subtly.
• Deployment to constrained targets Edge and mobile environments where a full framework cannot be installed.

Adding ONNX skills to your team.

Model portability work is assigned inside AI capacity, with post-export equivalence testing treated as a required step.

At Azendo, ONNX work sits with a dedicated AI engineering team. You can add ONNX capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.