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

Exporting and loading pipelines with MLeap

When we train a machine learning or deep learning model, our intention is to be able to use it several times to predict new observations of data. To do this, we must be able to not only store the model but also load it back again into one or more platforms. Therefore, we encounter the need to serialize the model for future use in scoring or predictions.

MLeap is a commonly used format to serialize and execute machine learning and deep learning pipelines made in popular frameworks such as Apache Spark, scikit-learn, and TensorFlow. It is commonly used for making individual predictions rather than batch predictions. These serialized pipelines are called bundles and can be exported as models and later be loaded and deployed back into Azure Databricks to make new predictions.

In this section, we will learn how to use MLeap to export and load back again a DecisionTreeClassifier MLlib model to make predictions using a saved pipeline in...