After you've used automated machine learning to train some models, you can deploy the best performing model as a service for client applications to use.

Deploy a predictive service

In Azure Machine Learning, you can deploy a service as an Azure Container Instances (ACI) or to an Azure Kubernetes Service (AKS) cluster. For production scenarios, an AKS deployment is recommended, for which you must create an inference cluster compute target. In this exercise, you'll use an ACI service, which is a suitable deployment target for testing, and does not require you to create an inference cluster.

1. In Azure Machine Learning studio , on the Automated ML page, select the run for your automated machine learning experiment and view the Details tab.

2. Select the algorithm name for the best model. Then, on the Model tab, use the Deploy button to deploy the model with the following settings:

3. Wait for the deployment to start - this may take a few seconds. Then, in the Model summary section, observe the Deploy status for the predict-rentals service, which should be Running . Wait for this status to change to Successful . You may need to select ↻ Refresh periodically.

4. In Azure Machine Learning studio, view the Endpoints page and select the predict-rentals real-time endpoint. Then select the Consume tab and note the following information there. You need this information to connect to your deployed service from a client application.

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

Test the deployed service

Now that you've deployed a service, you can test it using some simple code.

1. With the Consume page for the predict-rentals 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 ).

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

3. 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 .

4. Use the ≪ button to collapse the file explorer pane and give you more room to focus on the Test-Bikes.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 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:

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

ADD Python Code into Code Block: 

endpoint = 'YOUR_ENDPOINT' #Replace with your endpoint 

key = 'YOUR_KEY' #Replace with your key 

 

import json 

import requests 

 

#An array of features based on five-day weather forecast 

x = [[1,1,2022,1,0,6,0,2,0.344167,0.363625,0.805833,0.160446], 

    [2,1,2022,1,0,0,0,2,0.363478,0.353739,0.696087,0.248539], 

    [3,1,2022,1,0,1,1,1,0.196364,0.189405,0.437273,0.248309], 

    [4,1,2022,1,0,2,1,1,0.2,0.212122,0.590435,0.160296], 

    [5,1,2022,1,0,3,1,1,0.226957,0.22927,0.436957,0.1869]] 

 

#Convert the array to JSON format 

input_json = json.dumps({"data": x}) 

 

#Set the content type and authentication for the request 

headers = {"Content-Type":"application/json", 

        "Authorization":"Bearer " + key} 


#Send the request 

response = requests.post(endpoint, input_json, headers=headers) 


#If we got a valid response, display the predictions 

if response.status_code == 200: 

    y = json.loads(response.json()) 

    print("Predictions:") 

    for i in range(len(x)): 

        print (" Day: {}. Predicted rentals: {}".format(i+1, max(0, round(y["result"][i])))) 

else: 

    print(response)

Note

Don't worry too much about the details of the code. It just defines features for a five day period using hypothetical weather forecast data, and uses the predict-rentals service you created to predict cycle rentals for those five days.

6. Switch to the browser tab containing the Consume page for the predict-rentals 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.

7. Switch to the browser tab containing the Consume page for the predict-rentals 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.

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

9. Verify that predicted number of rentals for each day in the five day period are returned.