Deploying a Text Classification Service

Deploying a Service

You can deploy a model as a real-time service that provides a real-time test UI and monitoring capabilities. After the model is trained, you can deploy a Successful version with ideal accuracy as a service. The procedure is as follows:

1. On the Dashboard page, after the service deployment status changes to Awaiting input, double-click Deploy Service. On the configuration details page, configure resource parameters.
2. On the service deployment page, select the resource specifications used for service deployment.
   • AI Application Source: defaults to the generated AI application.
   • AI Application and Version: The current AI application version is automatically selected, which is changeable.
   • Resource Pool: defaults to public resource pools.
   • Traffic Ratio: defaults to 100 and supports a value range of 0 to 100.
   • Specifications: Select available specifications based on the list displayed on the console. The specifications in gray cannot be used in the current environment. If there are no specifications after you select a public resource pool, no public resource pool is available in the current environment. In this case, use a dedicated resource pool or contact the administrator to create a public resource pool.
   • Compute Nodes: an integer ranging from 1 to 5. The default value is 1.
   • Auto Stop: enables a service to automatically stop at a specified time. If this function is not enabled, the real-time service continuously runs and fees are incurred accordingly. Auto stop is enabled by default and its default value is 1 hour later.

     The auto stop options are 1 hour later, 2 hours later, 4 hours later, 6 hours later, and Custom. If you select Custom, enter any integer from 1 to 24 in the text box on the right.

     

     You can choose the package that you have bought when you select specifications. On the configuration fee tag, you can view your remaining package quota and how much you will pay for any extra usage.

3. After configuring resources, click Next and confirm the operation. Wait until the status changes to Executed, which means the AI application has been deployed as a real-time service.

Testing a Service

After the service is deployed, click Instance Details to go to the real-time service details page. Click the Prediction tab to test the service. You can also use code to test a service. For details, see Accessing Real-Time Services.

Figure 1 Testing the service