Building Azure ML Pipelines using the Azure Machine Learning SDK

Lab Steps

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Logging into the Microsoft Azure Portal
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Launching Azure Machine Learning Studio
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Creating an Azure Notebook for the Azure ML Pipeline
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Working Through the Azure Notebook

The hands-on lab is part of this learning path

Ready for the real environment experience?

DifficultyIntermediate
Time Limit1h 30m
Students68
Ratings
5/5
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Description

The Azure Machine Learning SDK allows data scientists and machine learning engineers to harness the cloud's power in their day-to-day operations. Data science enthusiasts can train their models against scalable compute and GPU clusters on-demand in the cloud, all from the same Jupyter notebook experience.

With the Azure Machine Learning SDK, comes Azure ML pipelines. Machine learning engineers can create a CI/CD approach to their data science tasks by splitting their workflows into pipeline steps. This allows for greater scalability when dealing with large scale data. The separation of functions greatly benefits complex model orchestration as engineers and scientists can focus on one segment at a time. It also provides cost savings by enabling different levels of compute for each step.

In this lab, you will dive into Azure Notebooks and launch a Jupyter notebook to build an Azure ML pipeline that ingests data, trains a model, and deploys a web service.

Learning Objectives

Upon completion of this lab you will be able to:

  • Manage a workspace using the Azure Machine Learning SDK
  • Pass data between pipelines
  • Create and run Azure Notebooks
  • Build Azure ML Pipelines for your Data Science workflows

Intended Audience

This lab is intended for:

  • Individuals studying to take the Azure DP-100 exam
  • Anyone interested in learning how to use the Azure Machine Learning SDK

Lab Prerequisites

You should be familiar with:

  • Basic concepts of Azure Machine Learning
  • Experience with Python is not required but preferred. 
Environment before
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Environment after
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About the Author

Luke currently serves as a Cloud Labs Developer at Cloud Academy. His background is infrastructure development using Terraform. He is an Azure DevOps Engineer Expert, Azure Administrator Associate, and HashiCorp Certified - Terraform Associate.