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 an Azure Kubernetes Service (AKS) cluster that provides better scalability and security.

Deploy a service

1. View the Predict Auto Price 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-auto-price real-time endpoint.

2. When the predict-auto-price 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. Observe that you can use the ⧉ link next to these values to copy them to the clipboard.

4. With the Consume page for the predict-auto-price service page open in your browser, open a new browser tab and open a second instance of 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-Autos.ipynb notebook tab.

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 position, such as a note pad or program, 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:  1. Click inside the code box and select CTL + A followed by CTL+ C 2. Alternatively, if you are using a Mac select Command + A and Command + C to copy all the code to your clipboard8.

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

endpoint = 'YOUR_ENDPOINT' #Replace with your endpoint
key = 'YOUR_KEY' #Replace with your key

import urllib.request
import json
import os

# Prepare the input data
data = {
    "Inputs": {
        "WebServiceInput0":
        [
            {
                    'symboling': 3,
                    'normalized-losses': None,
                    'make': "alfa-romero",
                    'fuel-type': "gas",
                    'aspiration': "std",
                    'num-of-doors': "two",
                    'body-style': "convertible",
                    'drive-wheels': "rwd",
                    'engine-location': "front",
                    'wheel-base': 88.6,
                    'length': 168.8,
                    'width': 64.1,
                    'height': 48.8,
                    'curb-weight': 2548,
                    'engine-type': "dohc",
                    'num-of-cylinders': "four",
                    'engine-size': 130,
                    'fuel-system': "mpfi",
                    'bore': 3.47,
                    'stroke': 2.68,
                    'compression-ratio': 9,
                    'horsepower': 111,
                    'peak-rpm': 5000,
                    'city-mpg': 21,
                    'highway-mpg': 27,
            },
        ],
    },
    "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)
    y = json_result["Results"]["WebServiceOutput0"][0]["predicted_price"]
    print('Predicted price: {:.2f}'.format(y))

except urllib.error.HTTPError as error:
    print("The request failed with status code: " + str(error.code))

    # Print the headers to help debug the error
    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 submits details of a car and uses the predict-auto-price service you created to get a predicted price. 9. Switch to the browser tab containing the Consume page for the predict-auto-price 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-auto-price 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 price is returned.