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 12: Distributed Deep Learning in Azure Databricks

In the previous chapter, we have learned how we can effectively serialize machine learning pipelines and manage the full development life cycle of machine learning models in Azure Databricks. This chapter will focus on how we can apply distributed training in Azure Databricks.

Distributed training of deep learning models is a technique in which the training process is distributed across workers in clusters of computers. This process is not trivial and its implementation requires us to fine-tune the way in which the workers communicate and transmit data between them, otherwise distributing training can take longer than single-machine training. Azure Databricks Runtime for Machine Learning includes Horovod, a library that allows us to solve most of the issues that arise from distributed training of deep learning algorithms. We will also show how we can leverage the native Spark support of the TensorFlow machine learning framework...