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)
1
Section 1: Introducing Databricks
4
Section 2: Data Pipelines with Databricks
9
Section 3: Machine and Deep Learning with Databricks

Optimizing model selection with scikit-learn, Hyperopt, and MLflow

As we saw in the previous sections, Hyperopt is a Python library that allows us to track optimization runs that can be used for hyperparameter model tuning distributed computing environments such as Azure Databricks. In this section, we will go through an example of training a scikit-learn model. We will use Hyperopt to track the tuning process and log the results to MLflow, the model life cycle management platform.

In Azure Databricks Runtime for Machine Learning, we have an optimized version of Hyperopt at our disposal that supports MLflow tracking. Here, we can use the SparkTrials objects to log the results of the tuning process of single-machine models during parallel executions. We will use these tools to find the best set of hyperparameters for several scikit-learn models.

We will do the following:

  • Prepare the training dataset.
  • Use Hyperopt to define the objective function to be minimized.
  • ...