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


In this chapter, we learned about some of the valuable features of Azure Databricks that allow us to track training runs, as well as find the optimal set of hyperparameters of machine learning models, using the MLflow Model Registry. We have also learned how we can optimize how we scan the search space of optimal parameters using Hyperopt. This is a great set of tools because we can fine-tune models that have complete tracking for the hyperparameters that are used for training. We also explored a defined search space of hyperparameters using adaptative search strategies, which are much more optimized than the common grid and random search strategies.

In the next chapter, we will explore how to use the MLflow Model Registry, which is integrated into Azure Databricks. MLflow makes it easier to keep track of the entire life cycle of a machine learning model and all the associated parameters and artifacts used in the training process, but it also allows us to deploy these models...