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Deploy a Custom sentiment analysis model

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Once you're satisfied with how your model performs, it's ready to be deployed and used to recognize entities in text. Deploying a model makes it available for use through the prediction API.

Prerequisites

Deploy model

After you've reviewed your model's performance and decided it can be used in your environment, you need to assign it to a deployment. Assigning the model to a deployment makes it available for use through the prediction API. It is recommended to create a deployment named production to which you assign the best model you have built so far and use it in your system. You can create another deployment called staging to which you can assign the model you're currently working on to be able to test it. You can have a maximum of 10 deployments in your project.

Submit deployment job

Submit a PUT request using the following URL, headers, and JSON body to submit a deployment job. Replace the placeholder values with your own values.

{Endpoint}/language/authoring/analyze-text/projects/{projectName}/deployments/{deploymentName}?api-version={API-VERSION}
Placeholder Value Example
{ENDPOINT} The endpoint for authenticating your API request. https://<your-custom-subdomain>.cognitiveservices.azure.com
{PROJECT-NAME} The name of your project. This value is case-sensitive. myProject
{DEPLOYMENT-NAME} The name of your deployment. This value is case-sensitive. staging
{API-VERSION} The version of the API you are calling. The value referenced here is for the latest version released. See Model lifecycle to learn more about other available API versions. 2023-04-15-preview

Headers

Use the following header to authenticate your request.

Key Value
Ocp-Apim-Subscription-Key The key to your resource. Used for authenticating your API requests.

Request body

Use the following JSON in the body of your request. Use the name of the model you to assign to the deployment.

{
  "trainedModelLabel": "{MODEL-NAME}"
}
Key Placeholder Value Example
trainedModelLabel {MODEL-NAME} The model name that will be assigned to your deployment. You can only assign successfully trained models. This value is case-sensitive. myModel

Once you send your API request, you’ll receive a 202 response indicating that the job was submitted correctly. In the response headers, extract the operation-location value. It will be formatted like this:

{ENDPOINT}/language/authoring/analyze-text/projects/{PROJECT-NAME}/deployments/{DEPLOYMENT-NAME}/jobs/{JOB-ID}?api-version={API-VERSION}

{JOB-ID} is used to identify your request, since this operation is asynchronous. You can use this URL to get the deployment status.

Get deployment job status

Use the following GET request to query the status of the deployment job. You can use the URL you received from the previous step, or replace the placeholder values with your own values.

{ENDPOINT}/language/authoring/analyze-text/projects/{PROJECT-NAME}/deployments/{DEPLOYMENT-NAME}/jobs/{JOB-ID}?api-version={API-VERSION}
Placeholder Value Example
{ENDPOINT} The endpoint for authenticating your API request. https://<your-custom-subdomain>.cognitiveservices.azure.com
{PROJECT-NAME} The name of your project. This value is case-sensitive. myProject
{DEPLOYMENT-NAME} The name of your deployment. This value is case-sensitive. staging
{JOB-ID} The ID for locating your model's training status. This is in the location header value you received in the previous step. xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxxx
{API-VERSION} The version of the API you're calling. The value referenced here is for the latest version released. See Model lifecycle to learn more about other available API versions. 2023-04-15-preview

Headers

Use the following header to authenticate your request.

Key Value
Ocp-Apim-Subscription-Key The key to your resource. Used for authenticating your API requests.

Response body

Once you send the request, you'll get the following response. Keep polling this endpoint until the status parameter changes to "succeeded". You should get a 200 code to indicate the success of the request.

{
    "jobId":"{JOB-ID}",
    "createdDateTime":"{CREATED-TIME}",
    "lastUpdatedDateTime":"{UPDATED-TIME}",
    "expirationDateTime":"{EXPIRATION-TIME}",
    "status":"running"
}

Swap deployments

After you are done testing a model assigned to one deployment and you want to assign this model to another deployment you can swap these two deployments. Swapping deployments involves taking the model assigned to the first deployment, and assigning it to the second deployment. Then taking the model assigned to second deployment, and assigning it to the first deployment. You can use this process to swap your production and staging deployments when you want to take the model assigned to staging and assign it to production.

To swap deployments from within Language Studio:

  1. In the Deploying a model page, select the two deployments you want to swap and select Swap deployments from the top menu.

  2. From the window that appears, select the names of the deployments you want to swap.

Delete deployment

To delete a deployment from within Language Studio, go to the Deploying a model page. Select the deployment you want to delete and select Delete deployment from the top menu.

Assign deployment resources

You can deploy your project to multiple regions by assigning different Language resources that exist in different regions.

To assign deployment resources in other regions in Language Studio:

  1. Make sure you've assigned yourself as a Cognitive Services Language Owner to the resource you used to create the project.
  2. Go to the Deploying a model page in Language Studio.
  3. Select the Regions tab.
  4. Select Add deployment resource.
  5. Select a Language resource in another region.

You are now ready to deploy your project to the regions where you have assigned resources.

Unassign deployment resources

When unassigning or removing a deployment resource from a project, you will also delete all the deployments that have been deployed to that resource's region.

To unassign or remove deployment resources in other regions using Language Studio:

  1. Go to the Regions tab in the Deploy a model page.
  2. Select the resource you'd like to unassign.
  3. Select the Remove assignment button.
  4. In the window that appears, type the name of the resource you want to remove.

Next steps