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

Automating model tracking with MLflow

As we mentioned previously, MLflow is an open-source platform for managing machine and deep learning model life cycles, which allows us to perform experiments, ensure reproducibility, and support easy model deployment. It also provides us with a centralized model registry. As a general overview, the components of MLflow are as follows:

  • MLflow Tracking: It records all data associated with an experiment, such as code, data, configuration, and results.
  • MLflow Projects: It wraps the code in a format that ensures the results can be reproduced between runs, regardless of the platform.
  • MLflow Models: This provides us with a deployment platform for our machine learning and deep learning models.
  • Model Registry: The central repository for our machine learning and deep learning models.

In this section, we will focus on MLflow Tracking, which is the component that allows us to log and register the code, properties, hyperparameters...