Train and publish a custom neural machine translation model with Azure AI Translator's Custom Translator and call it with a category ID
domain: learn.microsoft.com · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Create a Custom Translator workspace and project for the target language pair, then create or join a workspace tied to a Translator resource
Upload aligned parallel documents (or sentence pairs) as training, tuning, and testing sets covering the target domain/terminology
Train a model from the uploaded documents; training is an asynchronous job that can take from under an hour to many hours depending on data volume
Once trained, review BLEU score and test output, then publish the model to obtain its Category ID (a concatenation of workspace ID, project label, and category code)
Call the standard text translation endpoint with the category parameter set to that Category ID to route requests through the custom model
Known gotchas
Very small training sets are typically rejected or produce a model with no measurable improvement over the baseline generic model — a meaningful volume of clean, aligned in-domain data is needed
Publishing/training are long-running async operations with no fixed SLA on completion time; poll workspace/project status rather than assuming a fixed duration
Microsoft has been folding Translator (including Custom Translator) into the broader Foundry Tools branding/portal, so screenshots and exact portal navigation in older tutorials may not match the current console
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