Skip to content

Latest commit

 

History

History
95 lines (66 loc) · 8.18 KB

Lab02B.md

File metadata and controls

95 lines (66 loc) · 8.18 KB
Raw

Lab 2B: Deploying a Service with the Azure ML Designer

Now that you have a trained model, you can take the training pipeline and use it to create an inference pipeline for scoring new data.

Before You Start

Before you start this lab, ensure that you have completed Lab 1A and Lab 1B, which include tasks to create the Azure Machine Learning workspace and other resources used in this lab. You must also complete Lab 2A, which includes tasks to create the Designer training pipeline used in this lab.

Task 1: Prepare Compute

In this lab, you will publish an inference pipeline as a containerized service in an Azure Kubernetes Service (AKS) cluster. An AKS cluster can take some time to initialize, so you'll start the process before preparing your inference pipeline.

  1. In Azure Machine Learning studio, on the Compute page for your workspace, review the existing compute targets under each tab. These should include:

    • Compute Instances: The compute instance you created in a previous lab.
    • Training Clusters: The aml-cluster compute target you created in a previous lab].
    • Inference Clusters: None (yet!)
    • Attached Compute: None (this is where you could attach a virtual machine or Databricks cluster that exists outside of your workspace)

  2. In the Compute Instances tab, if your compute instance is not already running, start it - you will use it later in this lab.

  3. On the Inference Clusters tab, add a new cluster with the following settings:

    • Compute name: aks-cluster
    • Kubernetes Service: Create new
    • Region: Any available region
    • Virtual Machine size: Standard_D2_v2
    • Cluster purpose: Production
    • Number of nodes: 3
    • Network configuration: Basic
    • Enable SSL configuration: Unselected

  4. Verify that the compute target is in the Creating state, and proceed to the next task.

Task 2: Create an Inference Pipeline

While the inference compute is being provisioned, you can prepare the inference pipeline for deployment.

  1. On the Designer page, open the Visual Diabetes Training pipeline you created in the previous lab.

  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 Visual Diabetes Training-real time inference will be opened.

  3. Rename the new pipeline to Predict Diabetes, and then review the new pipeline. Note that 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.

  4. The inference pipeline assumes that new data will match the schema of the original training data, so the diabetes dataset module from the training pipeline is included. However, this input data includes the Diabetic label that the model predicts, which is unintuitive to include in new patient data for which a diabetes 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, connected to the same dataset input of the Apply Transformation module as the Web Service Input. Then modify the settings of the Enter Data Manually module to use the following CSV input, which includes feature values without labels for three new patient observations:

    PatientID,Pregnancies,PlasmaGlucose,DiastolicBloodPressure,TricepsThickness,SerumInsulin,BMI,DiabetesPedigree,Age
1882185,9,104,51,7,24,27.36983156,1.350472047,43
1662484,6,73,61,35,24,18.74367404,1.074147566,75
1228510,4,115,50,29,243,34.69215364,0.741159926,59

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

  6. The ouput from the Score Model module includes all of the input features as well as the predicted label and probability score. To limit the output to only the prediction and probability, delete the connection between the Score Model module and the web Service Output, add an Apply SQL Transformation module from the Data Transformations section, connect the output from the Score Model module to the t1 (left-most) input of the Apply SQL Transformation, and connect the output of the Apply SQL Transformation module to the Web Service Output. Then modify the settings of the Apply SQL Transformation module to use the following SQL query script:

    SELECT PatientID,
       [Scored Labels] AS DiabetesPrediction,
       [Scored Probabilities] AS Probability
FROM t1

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

    Visual Inference Pipeline

  8. Run the pipeline as a new experiment named predict-diabetes on the aml-compute compute target you used for training. This may take a while!

Task 3: Publish a Web Service

Now you have an inference pipeline for real-time inferencing, which you can publish as a web service for client applications to use.

  1. Return the the Compute page and on the Inference Compute tab, refresh the view and verify that your aks-cluster compute has been created. If not, wait for your inference cluster to be created. This may take quite a bit of time.

  2. Switch back to the Designer tab and reopen your Predict Diabetes inference pipeline. If it has not yet finished running, await it's completion. Then visualize the output of the Apply SQL Transformation module to see the predicted labels and probabilties for the three patient observations in the input data.

  3. At the top right, click Deploy, and set up a new real-time endpoint named predict-diabetes on the aks-cluster compute target you created.

  4. 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.

    Tip: While you're waiting for your service to be deployed, why not spend some time reviewing the Azure Machine Learning Designer documentation at https://docs.microsoft.com/azure/machine-learning/service/concept-designer?

Task 4: Test the Web Service

Now you can test your deployed service from a client application - in this case, you'll use a notebook in your Notebook VM.

  1. On the Endpoints page, open the predict-diabetes real-time endpoint.
  2. When the predict-diabetes endpoint opens, on the Test page, note the default test input parameters and then click Test to submit them to the deployed web service and generate a prediction.
  3. On the Consume tab, view the sample code that is provided for Python, and then copy the entire Python sample script to the clipboard.
  4. On the Compute page, if your compute instance is not yet running, wait for it to start. Then click its Jupyter link.
  5. In Jupyter, in the Users/DP100 folder, open 02B - Using the Visual Designer.ipynb.
  6. In the notebook, paste the code you copied into the empty code cell.
  7. Run the code cell and view the output returned by your web service.

Task 5: Delete the Web Service and Compute

The web service is hosted in a Kubernetes cluster. If you don't intend to experiment with it further, you should delete the endpoint and the cluster to avoid accruing unnecessary Azure charges.

  1. In the Studio web interface for your Azure ML workspace, on the Endpoints tab, select the predict-diabetes endpoint. Then click the Delete (🗑) button and confirm that you want to delete the endpoint.
  2. On the Compute page, on the Inference Clusters tab, select the select the aks-cluster endpoint. Then click the Delete (🗑) button and confirm that you want to delete the compute target.

Note: If you intend to continue straight to the next exercise, leave your compute instance running. If you're taking a break, you might want to close the Jupyter tabs and Stop your compute instance to avoid incurring unnecessary costs.