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

Chapter 11: Managing and Serving Models with MLflow and MLeap

In the previous chapter, we learned how we can fine-tune models created in Azure Databricks. The next step is how we can effectively keep track and make use of the models that we train. Software development has clear methodologies for keeping track of code, having stages such as staging or production versions of the code and general code lifecycle management processes, but it's not that common to see that applied to machine learning models. The reasons for this might vary, but one reason could be that the data science team follows its own methodologies that might be closer to academia than the production of software, as well as the fact that machine learning doesn't have clearly defined methodologies for development life cycles. We can apply some of the methodologies used commonly in software for machine learning models in Azure Databricks.

This chapter will focus on exploring how the models and processes...