Built-in Predictive Analytics Mode

Input

This is a built-in input and output mode for predictive analytics. The models using this mode are identified as predictive analytics models. The prediction request path is /, the request protocol is HTTP, the request method is POST, and Content-Type is application/json. The request body is in JSON format. For details about the JSON fields, see Table 1. Before selecting this mode, ensure that your model can process the input data in JSON Schema format. For details about the JSON Schema format, see the official guide.

ReqData is of the Object type and indicates the inference data. The data structure is determined by the application scenario. For models using this mode, the preprocessing logic in the custom model inference code should be able to correctly process the data inputted in the format defined by the mode.

The JSON Schema of a prediction request is as follows:

{
	"type": "object",
	"properties": {
		"data": {
			"type": "object",
			"properties": {
				"req_data": {
					"items": [{
						"type": "object",
						"properties": {}
					}],
					"type": "array"
				}
			}
		}
	}
}

Output

The inference result is returned in JSON format. For details about the JSON fields, see Table 3.

Similar to ReqData, RespData is also of the Object type and indicates the prediction result. Its structure is determined by the application scenario. For models using this mode, the postprocessing logic in the custom model inference code should be able to correctly output data in the format defined by the mode.

The JSON Schema of a prediction result is as follows:

{
	"type": "object",
	"properties": {
		"data": {
			"type": "object",
			"properties": {
				"resp_data": {
					"type": "array",
					"items": [{
						"type": "object",
						"properties": {}
					}]
				}
			}
		}
	}
}

Sample Request

In this mode, input the data to be predicted in JSON format. The prediction result is returned in JSON format. The following are examples:

• Performing prediction on the console

  On the Prediction tab page of the service details page, enter inference code and click Predict to obtain the prediction result.

• Using Postman to call a RESTful API for prediction

  After a model is deployed as a service, you can obtain the API URL on the Usage Guides tab page of the service details page.

  • On the Headers tab page, set Content-Type to application/json and X-Auth-Token to the actual token obtained.
    Figure 1 Setting the request header for prediction
    
  • On the Body tab page, edit the data to be predicted and click send to send your prediction request.