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Azure Synapse Analytics Cookbook

You're reading from   Azure Synapse Analytics Cookbook Implement a limitless analytical platform using effective recipes for Azure Synapse

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781803231501
Length 238 pages
Edition 1st Edition
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Authors (2):
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Gaurav Agarwal(BLR) Gaurav Agarwal(BLR)
Author Profile Icon Gaurav Agarwal(BLR)
Gaurav Agarwal(BLR)
Meenakshi Muralidharan Meenakshi Muralidharan
Author Profile Icon Meenakshi Muralidharan
Meenakshi Muralidharan
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Toc

Table of Contents (11) Chapters Close

Preface 1. Chapter 1: Choosing the Optimal Method for Loading Data to Synapse 2. Chapter 2: Creating Robust Data Pipelines and Data Transformation FREE CHAPTER 3. Chapter 3: Processing Data Optimally across Multiple Nodes 4. Chapter 4: Engineering Real-Time Analytics with Azure Synapse Link Using Cosmos DB 5. Chapter 5: Data Transformation and Processing with Synapse Notebooks 6. Chapter 6: Enriching Data Using the Azure ML AutoML Regression Model 7. Chapter 7: Visualizing and Reporting Petabytes of Data 8. Chapter 8: Data Cataloging and Governance 9. Chapter 9: MPP Platform Migration to Synapse 10. Other Books You May Enjoy

Training a model using AutoML in Synapse

Azure Synapse Studio gives you the flexibility to develop a machine learning model on top of your dataset. In this recipe, you will learn how you can use the AutoML feature to train your model on the existing Spark tables. You can select the Spark table that you want to train the dataset on with the code-free experience of machine learning models using AutoML.

We will be using the regression model in this recipe. However, it is completely dependent on the problem that you are trying to solve, and you can choose from models including regression, classification, or Time Series Insights to fit your need.

Getting ready

We will be using the same Spark tables that we created in Chapter 2, Creating Robust Data Pipelines and Data Transformation.

We will need to do some setup to prepare for this recipe:

  • We need to do this to get ready for the next steps. For more on how to create the Azure Machine Learning workspace, you can refer...
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