After creating and running a pipeline to train the clustering model, you can create an inference pipeline that uses the model to assign new data observations to clusters. This will form the basis for a predictive service that you can publish for applications to use.

Create an inference pipeline

1. In Microsoft Azure Machine Learning Studio, open the Train Penguin Clustering 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 Train Penguin Clustering-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 Train Penguin Clustering-real time inference pipeline.

3. Rename the new pipeline to Predict Penguin Clusters , 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. The transformations and clustering model in your training pipeline are encapsulated in this pipeline based on the statistics from your training data, and will be used to transform and score the new data.

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

• Replace the penguin-data dataset with an Enter Data Manually module that does not include the Species column.

• Remove the Select Columns in Dataset module, which is now redundant.

• Connect the Web Service Input and Enter Data Manually modules (which represent inputs for data to be clustered) to the first Apply Transformation module.

• Remove the Evaluate Model module.

• 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 penguin-data dataset from the training pipeline is included. However, this input data includes a column for the penguin species, which the model does not use. Delete both the penguin-data dataset and the Select Columns in Dataset modules, and replace them with an Enter Data Manually module from the Data Input and Output section. Then modify the settings of the Enter Data Manually module to use the following CSV input, which contains feature values for three new penguin observations (including headers):

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

CulmenLength,CulmenDepth,FlipperLength,BodyMass
39.1,18.7,181,3750
49.1,14.8,220,5150
46.6,17.8,193,3800

5. Connect the outputs from both the Web Service Input and Enter Data Manually modules to the Dataset (right) input of the first Apply Transformation module.

6. Delete the Evaluate Model module.

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

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

9. When the pipeline has finished, visualize the Results dataset output of the Assign Data to Clusters module to see the predicted cluster assignments and metrics for the three penguin observations in the input data.

Your inference pipeline assigns penguin observations to clusters based on their features. Now you're ready to publish the pipeline so that client applications can use it.