An Azure service that provides natural language capabilities including sentiment analysis, entity extraction, and automated question answering.
The described CLU evaluation failures with Http client call failed, reasonPhrase: Unauthorized across regions and subscriptions are consistent with a platform-side issue in the evaluation pipeline rather than with training itself, especially since:
- Training jobs complete successfully.
- The same data worked recently.
- The error occurs quickly and consistently across regions/subscriptions and training modes.
From the available information, there is no documented client-side configuration or workaround in the CLU training/evaluation flow that addresses this specific “Unauthorized” evaluation failure. The CLU documentation only describes the normal behavior:
- After training completes successfully, evaluation is automatically triggered using the testing set.
- Only successfully completed jobs create a model; failed jobs do not produce a valid evaluation result.
Given that:
- Retries in different regions and subscriptions already failed.
- Training is succeeding, which indicates that project configuration and data are valid.
The recommended actions are:
- Avoid relying on evaluation until the issue is resolved, and do not assume evaluation failures reflect problems in the training data or configuration.
- If deployment without evaluation is acceptable for the scenario, deploy the trained model and validate it with a small, controlled set of test utterances in a non-production environment.
- Open a support ticket with Azure support, providing:
- Subscription IDs and regions tested.
- Approximate timestamps and job IDs of failing evaluation runs.
- Confirmation that training succeeds but evaluation fails within ~10–12 seconds with
Unauthorized.
There is no documented client-side certificate rotation or configuration step for CLU evaluation analogous to the AKS or VM certificate issues in the context, so remediation must be handled by the service team.
Separately, when integrating CLU models into applications, design the client experience so that:
- Users confirm actions before they are processed.
- There is always a correction path (for example, the ability to undo or adjust an action if the model output is wrong).
These patterns help mitigate risk if a model must be deployed before full automated evaluation is available.
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