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 during this lab.
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 a real-time inference pipeline by adding the Web Service Input module to the pipeline and moving its output to the Apply Transformation module. Then, remove the Evaluate Model module. After making those modifications, run the pipeline.
