After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This will form the basis for a predictive service that you can publish for applications to use.

Create and run an inference pipeline

1. In Microsoft Azure Machine Learning Studio, click the Designer page to view all of the pipelines you have created. Then open the Auto Price Training pipeline you created previously.

2. In the Create inference pipeline drop-down list, click Real-time inference pipeline . After a few seconds, a new version of your pipeline named Auto Price Training-real time inference will be opened.

If the pipeline does not include Web Service Input and Web Service Output modules, go back to the Designer page and then re-open the Auto Price Training-real time inference pipeline.

3. Rename the new pipeline to Predict Auto Price , and then review the new pipeline. It contains a web service input for new data to be submitted, and a web service output to return results. Some of the transformations and training steps have been encapsulated in this pipeline so that the statistics from your training data will be used to normalize any new data values, and the trained model will be used to score the new data.

You are going to make the following changes to the inference pipeline:

Replace the Automobile price data (Raw) dataset with an Enter Data Manually module that does not include the label column ( price )
Modify the Select Columns in Dataset module to remove any reference to the (now absent) price column.
Remove the Evaluate Model module.
Insert an Execute Python Script module before the web service output to return only the predicted label.

Follow the remaining steps below, using the image and information above for reference as you modify the pipeline.

4. The inference pipeline assumes that new data will match the schema of the original training data, so the Automobile price data (Raw) dataset from the training pipeline is included. However, this input data includes the price label that the model predicts, which is unintuitive to include in new car data for which a price prediction has not yet been made. Delete this module and replace it with an Enter Data Manually module from the Data Input and Output section, containing the following CSV data, which includes feature values without labels for three cars.

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: Click inside the code box and select CTL + A followed by CTL + C Alternatively, if you are using a Mac select Command + A and Command + C to copy all the code to your clipboard.

CSV Data

symboling,normalized-losses,make,fuel-type,aspiration,num-of-doors,body-style,drive-wheels,engine-location,wheel-base,length,width,height,curb-weight,engine-type,num-of-cylinders,engine-size,fuel-system,bore,stroke,compression-ratio,horsepower,peak-rpm,city-mpg,highway-mpg
3,NaN,alfa-romero,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27
3,NaN,alfa-romero,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27
1,NaN,alfa-romero,gas,std,two,hatchback,rwd,front,94.5,171.2,65.5,52.4,2823,ohcv,six,152,mpfi,2.68,3.47,9,154,5000,19,26

5. Connect the new Enter Data Manually module to the same dataset input of the Select Columns in Dataset module as the Web Service Input .

6. Now that you've changed the schema of the incoming data to exclude the price field, you need to remove any explicit uses of this field in the remaining modules. Select the Select Columns in Dataset module and then in the settings pane, edit the columns to remove the price field.

7. The inference pipeline includes the Evaluate Model module, which is not useful when predicting from new data, so delete this module.

8. The output from the Score Model module includes all of the input features as well as the predicted label. To modify the output to include only the prediction:

Delete the connection between the Score Model module and the Web Service Output .
Add an Execute Python Script module from the Python Language section, replacing all of the the default python script with the following code (which selects only the Scored Labels column and renames it to predicted_price ):

Important Note

import pandas as pd

def azureml_main(dataframe1 = None, dataframe2 = None):

    scored_results = dataframe1[['Scored Labels']]
    scored_results.rename(columns={'Scored Labels':'predicted_price'},
                        inplace=True)
    return scored_results

Connect the output from the Score Model module to the Dataset1 (left-most) input of the Execute Python Script , and connect the output of the Execute Python Script module to the Web Service Output .

9. Verify that your pipeline looks similar to the following:

10. Submit the pipeline as a new experiment named mslearn-auto-inference on your compute cluster. This may take a while!

11. When the pipeline has completed, select the Execute Python Script module, and in the settings pane, on the Output + logs tab, visualize the Result dataset to see the predicted prices for the three cars in the input data.

12. Close the visualization window.

Your inference pipeline predicts prices for cars based on their features. Now you're ready to publish the pipeline so that client applications can use it.