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Distributed Data Systems with Azure Databricks

You're reading from   Distributed Data Systems with Azure Databricks Create, deploy, and manage enterprise data pipelines

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781838647216
Length 414 pages
Edition 1st Edition
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Author (1):
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Alan Bernardo Palacio Alan Bernardo Palacio
Author Profile Icon Alan Bernardo Palacio
Alan Bernardo Palacio
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Introducing Databricks
2. Chapter 1: Introduction to Azure Databricks FREE CHAPTER 3. Chapter 2: Creating an Azure Databricks Workspace 4. Section 2: Data Pipelines with Databricks
5. Chapter 3: Creating ETL Operations with Azure Databricks 6. Chapter 4: Delta Lake with Azure Databricks 7. Chapter 5: Introducing Delta Engine 8. Chapter 6: Introducing Structured Streaming 9. Section 3: Machine and Deep Learning with Databricks
10. Chapter 7: Using Python Libraries in Azure Databricks 11. Chapter 8: Databricks Runtime for Machine Learning 12. Chapter 9: Databricks Runtime for Deep Learning 13. Chapter 10: Model Tracking and Tuning in Azure Databricks 14. Chapter 11: Managing and Serving Models with MLflow and MLeap 15. Chapter 12: Distributed Deep Learning in Azure Databricks 16. Other Books You May Enjoy

Section 3: Machine and Deep Learning with Databricks

This section explores concepts around the use of machine learning and deep learning models. At the end of this section, you will be able to create, train, and deploy predictive models harnessing the power of distributed computing.

This section contains the following chapters:

  • Chapter 7, Using Python Libraries in Azure Databricks
  • Chapter 8, Databricks Runtime for Machine Learning
  • Chapter 9, Databricks Runtime for Deep Learning
  • Chapter 10, Model Tracking and Tuning in Azure Databricks
  • Chapter 11, Managing and Serving Models with MLflow and MLeap
  • Chapter 12, Distributed Deep Learning in Azure Databricks
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