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Azure Data Factory Cookbook

You're reading from   Azure Data Factory Cookbook Build and manage ETL and ELT pipelines with Microsoft Azure's serverless data integration service

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781800565296
Length 382 pages
Edition 1st Edition
Tools
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Authors (4):
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Dmitry Anoshin Dmitry Anoshin
Author Profile Icon Dmitry Anoshin
Dmitry Anoshin
Roman Storchak Roman Storchak
Author Profile Icon Roman Storchak
Roman Storchak
Xenia Ireton Xenia Ireton
Author Profile Icon Xenia Ireton
Xenia Ireton
Dmitry Foshin Dmitry Foshin
Author Profile Icon Dmitry Foshin
Dmitry Foshin
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Toc

Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Getting Started with ADF 2. Chapter 2: Orchestration and Control Flow FREE CHAPTER 3. Chapter 3: Setting Up a Cloud Data Warehouse 4. Chapter 4: Working with Azure Data Lake 5. Chapter 5: Working with Big Data – HDInsight and Databricks 6. Chapter 6: Integration with MS SSIS 7. Chapter 7: Data Migration – Azure Data Factory and Other Cloud Services 8. Chapter 8: Working with Azure Services Integration 9. Chapter 9: Managing Deployment Processes with Azure DevOps 10. Chapter 10: Monitoring and Troubleshooting Data Pipelines 11. Other Books You May Enjoy

Automatically building ML models with speed and scale

In ADF, it is possible to call ML algorithms to make predictive analytics as a step of the pipeline. In this recipe, you will learn how to create an Azure ML workspace and call an Azure ML experiment from ADF.

Getting ready

Before we start, please ensure that you have an Azure license and are familiar with the basics of Azure resources, such as the Azure portal, creating and deleting Azure resources, and creating pipelines in ADF. You can find more information about Azure resources in Chapter 1, Getting Started with ADF, and Chapter 2, Orchestration and Control Flow, of this book.

How to do it...

We are going to use Machine Learning Studio (classic), use an API to connect it with our ADF pipeline, run an ML experiment on a file from Blob storage, and save the results of the ML experiment to the output file:

  1. First, you need to create an Azure Machine Learning Studio environment. Go to the Azure Resources page...
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