Deployment Options

By Ashwin Venugopal -

 





Deployment Options

Each project can produce numerous models and deployments, each with a distinct name, thanks to Azure AI Language. Benefits include ability to:

Test two models side by side

Compare how the split of datasets impact performance

Deploy multiple versions of your model

During deployment you can choose the name for the deployed model, which can then be selected when submitting a classification task.

Using REST API

CLI development of Azure AI is made possible by the REST API for the Azure AI Language service. Language projects in the same manner as Language Studio offers a project-building user interface. The lab for this module delves deeper into Language Studio.

Pattern of Using API

For the majority of calls, the Azure AI Language service's API runs asynchronously. In each stage, we first send a request to the service and then follow up with a call to find out the status or outcome. With each request, a header is required to authenticate your request.

Submit Initial Request

The URL to submit the request to varies on which step you are on, but all are prefixed with the endpoint provided by your Azure AI Language resource.

Get Training Status

Use the URL from the request response's header to send a GET request that includes our Azure AI Language service key for authentication in order to obtain the training status. It may take some time to train a model, so keep checking back at this status URL until the response status indicates that the model was successfully trained. You can examine, confirm, and use your model after the training is complete.

Consuming a Deployed Model

The same procedure as described above applies when using the model to classify text, a POST request is used to submit the job, and a GET request is used to receive the results.

Submit Text For Classification

To use your model, submit a POST to the analyze endpoint at the following URL:

<Endpoint>/language/analyze-text/jobs?api-version=<API-VERSION>

Look for the operation-location value in the response headers, which will look something like the following URL:

 <Endpoint>/language/analyze-text/jobs/?api-version=<API-VERSION>

This URL is used to get your task results.

Get Classification Results

Submit a GET request to the endpoint from the previous request, with the same header for authentication. The classification result will be within the items array's results object, for each document submitted.

Conclusion

We have successfully learnt about all the deployment options.









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