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Cloud Scale Analytics with Azure Data Services

You're reading from   Cloud Scale Analytics with Azure Data Services Build modern data warehouses on Microsoft Azure

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800562936
Length 520 pages
Edition 1st Edition
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Author (1):
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Patrik Borosch Patrik Borosch
Author Profile Icon Patrik Borosch
Patrik Borosch
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Data Warehousing and Considerations Regarding Cloud Computing
2. Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses FREE CHAPTER 3. Chapter 2: Connecting Requirements and Technology 4. Section 2: The Storage Layer
5. Chapter 3: Understanding the Data Lake Storage Layer 6. Chapter 4: Understanding Synapse SQL Pools and SQL Options 7. Section 3: Cloud-Scale Data Integration and Data Transformation
8. Chapter 5: Integrating Data into Your Modern Data Warehouse 9. Chapter 6: Using Synapse Spark Pools 10. Chapter 7: Using Databricks Spark Clusters 11. Chapter 8: Streaming Data into Your MDWH 12. Chapter 9: Integrating Azure Cognitive Services and Machine Learning 13. Chapter 10: Loading the Presentation Layer 14. Section 4: Data Presentation, Dashboarding, and Distribution
15. Chapter 11: Developing and Maintaining the Presentation Layer 16. Chapter 12: Distributing Data 17. Chapter 13: Introducing Industry Data Models 18. Chapter 14: Establishing Data Governance 19. Other Books You May Enjoy

Examining Azure Machine Learning

Azure Machine Learning will offer you a wide collection of capabilities to develop, train, and deploy machine learning models. Additionally, the environment supports you with the automation and management of your models. This includes the versioning and tracking not only of the models but also the training data that you use to build and retrain them.

Browsing the different Azure ML tools

As mentioned, Azure Machine Learning comes with a collection of tools and functionalities to support you in any aspect of the machine learning life cycle:

  • Azure ML Designer: A graphical interface to build machine learning models on a point-and-click basis.
  • Jupyter Notebooks: A programming interface where you can build your own ML models using Python.
  • R Scripts/Notebooks: Programming interface where you can build your own ML models using R.
  • Many Models Solution Accelerator: Offers you capabilities to work with thousands of ML models if needed...
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