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 8: Databricks Runtime for Machine Learning

This chapter will be a deep dive into the development of classic machine learning algorithms to train and deploy models based on tabular data, exploring libraries and algorithms as well. The examples will be focused on the particularities and advantages of using Azure Databricks Runtime for Machine Learning (Databricks Runtime ML).

In this chapter we will explore the following concepts, which are focused on how we can extract and improve the features available in our data to train our machine learning and deep learning models. The topics that we will cover are listed here:

  • Loading data
  • Feature engineering
  • Time-series data sources
  • Handling missing values
  • Extracting features from text
  • Training machine learning models on tabular data

In the following sections, we will discuss the necessary libraries needed to perform the operations introduced, as well as providing some context on how best practices...