Book Image

Distributed Data Systems with Azure Databricks

By : Alan Bernardo Palacio
Book Image

Distributed Data Systems with Azure Databricks

By: Alan Bernardo Palacio

Overview of this book

Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.
Table of Contents (17 chapters)
Section 1: Introducing Databricks
Section 2: Data Pipelines with Databricks
Section 3: Machine and Deep Learning with Databricks

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.