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

Managing machine learning models

As we have seen before, in Azure Databricks we have at our disposal the MLflow Model Registry, which is an open source platform for managing the complete lifecycle of a machine learning or deep learning model. It allows us to directly manage models with a chronological linage, model versioning, and stage transition. It provides us with tools such as Experiments and Runs, which allow us to quickly visualize the results of training runs and hyperparameter optimization, and to maintain a proper model version control to keep track of which models we have available for serving and quickly update the current version if necessary.

MLflow has in Azure Databricks a Model Repository user interface (UI) in which we can set our models to respond to REST API requests for inference, transition models between stages, and visualize metrics and unstructured data associated with the models, such as description and comments. It gives us the possibility of managing...