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

Data preprocessing and featurization

Featurization is the process that we use to transform unstructured data such as text, images, or time-series data into numerical continuous features that are more easily handled by machine and deep learning models. It can be differentiated from featuring engineering from the fact that in featuring engineering the variables are already in the numerical form or have a more defined structure that leads us to the need to refactor or transform these variables into something that makes the machine or deep learning algorithm easier to extract patterns. In featurization, we need to first define a way in which we will extract numerical features from the unstructured data that we have.

We have the need to perform featurization basically because our deep learning models cannot interpret unstructured data directly and therefore, we need not only to extract it but to do this in a computationally efficient manner. This process needs to be incorporated into...