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)
1
Section 1: Introducing Databricks
4
Section 2: Data Pipelines with Databricks
9
Section 3: Machine and Deep Learning with Databricks

Summary

We have learned in this chapter about how we can improve the performance of our training pipelines for deep learning algorithms, using distributed learning with Horovod and the native TensorFlow for Spark in Azure Databricks. We have discussed the core algorithms that drive the capability of being able to effectively distribute key operations such as gradient descent and model weights update, how this is implemented in the horovod library, included with Azure Databricks Runtime for Machine Learning, and how we can use the native support now available for Spark in the TensorFlow framework for distributed training of deep learning models.

This chapter concludes this book. Hopefully, it enabled you to learn in an easier way the incredible number of features available in Azure Databricks for data engineering and data science. As mentioned before, most of the code examples are modifications of the official libraries or are taken from the Azure Databricks documentation in order...