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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

Technical requirements

In this chapter, we will use concepts and apply tools related to common topics in data science and optimization tasks. We will assume that you already have a good understanding of concepts such as neural networks and hyperparameter tuning, and also general knowledge of machine learning frameworks such as TensorFlow, PyTorch, or Keras.

In this chapter, we will discuss various techniques in order to distribute and optimize the training of deep learning models using Azure Databricks, so if you are not familiar with these terms, it would be advisable for you to review the TensorFlow documentation on how neural networks are designed and trained.

In order to work on the examples given in this chapter, you will need to have the following:

  • An Azure Databricks subscription.
  • An Azure Databricks notebook attached to a running cluster with Databricks Runtime ML version 7.0 or higher.
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