Creating a Classification Model with Azure ML Designer

1.5 hours
  • 5 Learning Objectives

About this Hands-on Lab

In this hands-on lab, you are working as a data scientist for AIDoctor, a medical clinic. You’ve been asked to come up with a model that can be used to predict if a patient is diabetic based on factors such as age, blood pressure, BMI, and more. You’ll use Azure Machine Learning studio and Azure Machine Learning designer to create a classification model that will predict if a patient is diabetic or not. You’ll also get to create an inference pipeline and deploy the model.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Open the Azure ML Studio and Create a New Dataset

In the Azure ML studio, create a new dataset using the diabetes data file from Microsoft.

Review the Dataset

Take a look at the data you’re working with and see if there are any missing values or values that will cause issues.

Design a Pipeline to Predict if Patients Have Diabetes and Review Evaluation Results

Drag and drop modules to create a pipeline that will train a classification model. After it runs, review the evaluation results and metrics.

Create and Run an Inference Pipeline

Create an inference pipeline, change the Web Service Input module’s output to the Apply Transformation module, and remove the Diabetic column in the Select Columns in Dataset module. After those modifications, run the pipeline.

Deploy the Model to Azure Container Instance and Test It

Deploy the model to an Azure Container Instance, and test the endpoint.

Note: Make sure you click Advanced before deployment and change both the CPU reserve capacity and memory reserve capacity to 1 or the resources will be deleted.

Additional Resources

In this lab scenario, you are working as a data scientist for AIDoctor, a medical clinic. You’ve been asked to come up with a model that can be used to predict if a patient is diabetic based on factors such as age, blood pressure, BMI, and more.

Use Azure Machine Learning studio and Azure Machine Learning designer to create a classification model that will predict if a patient is diabetic or not. Also, create an inference pipeline and deploy the model.


Log in to the Azure portal using the credentials provided on the lab page. Be sure to use an incognito or private browser window to ensure you're using the lab account rather than your own.

What are Hands-on Labs

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

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