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

Distributed training for deep learning

Deep neural networks (DNNs) have driven the advancement of artificial intelligence (AI) in the last decades in areas such as computer vision and neural network processing. These are applied every day to solve challenges in diverse use cases.

In order to scale the performance of models, it is necessary to develop complex model architectures with millions of trainable parameters, making the computations required for the training a resourceful operation. As the amount of available data to train models increases, we need to scale up the training pipeline of deep learning models in order to be able to use this available data..

Commonly, in order to train a DNN, we need to follow three basic steps, which are listed here:

  1. Pass the data through the layers of the network to compute the model loss in an operation called forward propagation.
  2. Backpropagate this loss from the output layer to the first layer in order to compute the gradients...