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

Chapter 9: Databricks Runtime for Deep Learning

This chapter will take a deep dive into the development of classic deep learning algorithms to train and deploy models based on unstructured data, exploring libraries and algorithms as well. The examples will be focused on the particularities and advantages of using Databricks for DL, creating DL models. In this chapter, we will learn about how we can efficiently train deep learning models in Azure Databricks and implementations of the different libraries that we have available to use.

The following topics will be introduced in this chapter:

  • Loading data for deep learning
  • Managing data using TFRecords
  • Automating scheme inference
  • Using Petastorm for distributed learning
  • Reading a dataset
  • Data preprocessing and featurization

This chapter will have more of a focus on deep learning models rather than machine learning ones. The main distinction is that we will focus more on handling large amounts of unstructured...