Run a Batch Inferencing Pipeline and Obtain Outputs

2.5 hours
  • 5 Learning Objectives

About this Hands-on Lab

In this hands-on lab, you will become familiar with creating a batch inferencing pipeline and obtaining its outputs in Azure Machine Learning.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Create a Compute Resource and Access the Provided Jupyter Notebook

Utilize the provisioned workspace to create a compute instance. This will be used to power a Jupyter notebook and execute your machine learning tasks.

If you plan to utilize the existing Jupyter notebook with the data and steps required to create and run the experiment already preconfigured, clone the repo from GitHub.

Train and Register a Model

Train and register a model to use in your batch inferencing pipeline.

Simulate Sample Data

Generate random data to simulate the thousands of patient records that are uploaded each day.

Create and Run a Batch Inferencing Pipeline

Build the necessary steps to create a batch inferencing pipeline, and then execute it.

View the Results

Obtain the outputs produced by the experiment.

Additional Resources

Lab Scenario

In this hands-on lab scenario, you are a Data Scientist for Awesome Company. Recently, the company has been using the Azure Machine Learning service to implement and run their machine learning workloads. One of the problems they are addressing is the need to predict diabetes cases based off of large numbers of patient records. In order to accomplish this, you are going to implement a batch inferencing pipeline.

To accomplish your goal, the following should be completed:

  • Train and register a model
  • Create and load batch data
  • Create and run a batch inference pipeline
  • Obtain the outputs of the experiment

Lab Setup

To utilize the preconfigured Jupyter notebook, clone the repo from GitHub Repo.

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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