After you've created and tested an inference pipeline for real-time inferencing, you can publish it as a service for client applications to use.

Note : In this exercise, you'll deploy the web service to an Azure Container Instance (ACI). This type of compute is created dynamically, and is useful for development and testing. For production, you should create an inference cluster , which provide an Azure Kubernetes Service (AKS) cluster that provides better scalability and security.

Deploy a service

1. View the Predict Diabetes inference pipeline you created in the previous unit.

2. At the top right, select Deploy , and deploy a new real-time endpoint, using the following settings:

3. Wait for the web service to be deployed - this can take several minutes. The deployment status is shown at the top left of the designer interface.

Test the service

Now you can test your deployed service from a client application - in this case, you'll use the code in the cell below to simulate a client application.

1. On the Endpoints page, open the predict-diabetes real-time endpoint.

2. When the predict-diabetes endpoint opens, view the Consume tab and note the following information there. You need this to connect to your deployed service from a client application.

3. Note that you can use the ⧉ link next to these values to copy them to the clipboard.

4. With the Consume page for the predict-diabetes service page open in your browser, open a new browser tab and open a second instance of Microsoft Azure Machine Learning studio . Then in the new tab, view the Notebooks page (under Author ).

5. In the Notebooks page, under My files , use the 🗋 button to create a new file with the following settings:

6. When the new notebook has been created, ensure that the compute instance you created previously is selected in the Compute box, and that it has a status of Running .

7. Use the ≪ button to collapse the file explorer pane and give you more room to focus on the Test-Diabetes.ipynb notebook tab.

8. In the rectangular cell that has been created in the notebook, paste the following code:

Deploy a predictive service

After you've created and tested an inference pipeline for real-time inferencing, you can publish it as a service for client applications to use.

Note : In this exercise, you'll deploy the web service to a Microsoft Azure Container Instance (ACI). This type of compute is created dynamically, and is useful for development and testing. For production, you should create an inference cluster , which provide a Microsoft Azure Kubernetes Service (AKS) cluster that provides better scalability and security.

1. View the Predict Penguin Clusters inference pipeline you created in the previous unit.

2. At the top right, select Deploy , and deploy a new real-time endpoint, using the following settings:

3. Wait for the web service to be deployed - this can take several minutes. The deployment status is shown at the top left of the designer interface.

Test the service

Now you can test your deployed service from a client application - in this case, you'll use the code in the cell below to simulate a client application.   1. On the Endpoints page, open the predict-penguin-clusters real-time endpoint.

2. When the predict-penguin-clusters endpoint opens, view the Consume tab and note the following information there. You need this to connect to your deployed service from a client application.

3. Note that you can use the ⧉ link next to these values to copy them to the clipboard.

4. With the Consume page for the predict-penguin-clusters service page open in your browser, open a new browser tab and open a second instance of Microsoft Azure Machine Learning studio . Then in the new tab, view the Notebooks page (under Author ).

5. In the Notebooks page, under My files , use the 🗋 button to create a new file with the following settings:

6. When the new notebook has been created, ensure that the compute instance you created previously is selected in the Compute box, and that it has a status of Running .

7. Use the ≪ button to collapse the file explorer pane and give you more room to focus on the Test-Penguins.ipynb notebook tab.

8. In the rectangular cell that has been created in the notebook, paste the following code:

Important Note

You will need to copy and paste the entire block of text presented in the code block.

Make sure you have selected all of the text or data in the code block including endpoints, brackets etc. before copying this over and placing it into the specified location or site in the exercise. This will help to avoid errors occurring or having to go back and start the exercise again.

Example

/public function processAPI () {    if (method_exists($this, $this->endpoint)) {        return $this->_response($this->{$this->endpoint}($this->args));    }    return $this->_response("No Endpoint: $this->endpoint", 404);/

You can also use the following shortcuts to copy and paste the code: 

endpoint = 'YOUR_ENDPOINT' #Replace with your endpoint 
key = 'YOUR_KEY' #Replace with your key 
  
import urllib.request 
import json 
import os 
  
data = { 
    "Inputs": { 
        "WebServiceInput0": 
        [ 
            { 
                    'PatientID': 1882185, 
                    'Pregnancies': 9, 
                    'PlasmaGlucose': 104, 
                    'DiastolicBloodPressure': 51, 
                    'TricepsThickness': 7, 
                    'SerumInsulin': 24, 
                    'BMI': 27.36983156, 
                    'DiabetesPedigree': 1.3504720469999998, 
                    'Age': 43, 
            }, 
        ], 
    }, 
    "GlobalParameters":  { 
    } 
} 
  
body = str.encode(json.dumps(data)) 
  
  
headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ key)} 
  
req = urllib.request.Request(endpoint, body, headers) 
  
try: 
    response = urllib.request.urlopen(req) 
    result = response.read() 
    json_result = json.loads(result) 
    output = json_result["Results"]["WebServiceOutput0"][0] 
    print('Patient: {}\nPrediction: {}\nProbability: {:.2f}'.format(output["PatientID"], 
                                                            output["DiabetesPrediction"], 
                                                            output["Probability"])) 
except urllib.error.HTTPError as error: 
    print("The request failed with status code: " + str(error.code)) 
  
    # Print the headers to help debug 
    print(error.info()) 
    print(json.loads(error.read().decode("utf8", 'ignore'))) 

Note : Don't worry too much about the details of the code. It just defines features for a patient, and uses the predict-diabetes service you created to predict a diabetes diagnosis.

9. Switch to the browser tab containing the Consume page for the predict-diabetes service, and copy the REST endpoint for your service. The switch back to the tab containing the notebook and paste the key into the code, replacing YOUR_ENDPOINT.

10. Switch to the browser tab containing the Consume page for the predict-diabetes service, and copy the Primary Key for your service. The switch back to the tab containing the notebook and paste the key into the code, replacing YOUR_KEY.

11. Save the notebook. Then use the ▷ button next to the cell to run the code.

12. Verify that predicted diabetes diagnosis is returned.