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

Model Registry example

In this section, we will go through an example in which we will develop a machine learning model and use the MLflow Model Registry to save it, manage the stages in which it belongs, and use it to make predictions. The model will be a Keras neural network, and we will use the Windfarm US dataset to predict the power output of wind farms based on parameters from weather conditions such as wind direction, speed, and air temperature. We will make use of MLflow to keep track of the stage of the model and be able to register and load it back again to make predictions:

  1. First, we will retrieve the dataset that will be used to train the model. We will use the pandas read_csv() function to load directly from the Uniform Resource Identifier (URI) of the file in GitHub, as follows:
    import pandas as pd
    wind_farm_data = pd.read_csv("", index_col=0)

    The dataset...