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Distributed Data Systems with Azure Databricks

You're reading from Distributed Data Systems with Azure Databricks

Product type Book

Published in May 2021

Publisher Packt

ISBN-13 9781838647216

Pages 414 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Alan Bernardo Palacio

Table of Contents (17) Chapters

Preface

Section 1: Introducing Databricks

Chapter 1: Introduction to Azure Databricks

Chapter 2: Creating an Azure Databricks Workspace

Section 2: Data Pipelines with Databricks

Chapter 3: Creating ETL Operations with Azure Databricks

Chapter 4: Delta Lake with Azure Databricks

Chapter 5: Introducing Delta Engine

Chapter 6: Introducing Structured Streaming

Section 3: Machine and Deep Learning with Databricks

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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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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Authors (1)

Alan Bernardo Palacio

Alan Bernardo Palacio is a data scientist and an engineer with vast experience in different engineering fields. His focus has been the development and application of state-of-the-art data products and algorithms in several industries. He has worked for companies such as Ernst and Young, Globant, and now holds a data engineer position at Ebiquity Media helping the company to create a scalable data pipeline. Alan graduated with a Mechanical Engineering degree from the National University of Tucuman in 2015, participated as the founder in startups, and later on earned a Master's degree from the faculty of Mathematics in the Autonomous University of Barcelona in 2017. Originally from Argentina, he now works and resides in the Netherlands.

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