You're reading from Machine Learning Engineering with Python Manage the production life cycle of machine learning models using MLOps with practical examples

Product type Paperback

Published in Nov 2021

Publisher Packt

ISBN-13 9781801079259

Length 276 pages

Edition 1st Edition

Languages

Python

Tools

GitHub

Concepts

Machine Learning

Author (1):

Andrew P. McMahon

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Table of Contents (13) Chapters

Preface

1. Section 1: What Is ML Engineering?

2. Chapter 1: Introduction to ML Engineering FREE CHAPTER

3. Chapter 2: The Machine Learning Development Process

4. Section 2: ML Development and Deployment

5. Chapter 3: From Model to Model Factory

6. Chapter 4: Packaging Up

7. Chapter 5: Deployment Patterns and Tools

8. Chapter 6: Scaling Up

9. Section 3: End-to-End Examples

10. Chapter 7: Building an Example ML Microservice

11. Chapter 8: Building an Extract Transform Machine Learning Use Case

12. Other Books You May Enjoy

To get the most out of this book

To get the most out of the examples in the book, you will need access to a computer or server where you have privileges to install and run Python and Apache Spark applications. For many of the examples, you will also require access to a terminal, such as Bash. The examples in the book were built on a Linux machine running Bash so you may need to translate some pieces for your operating system and terminal. For some examples using AWS, you will require an account where you can enable billing. Examples in the book used Apache Spark v3.0.2.

In Chapter 5, Deployment Patterns and Tools, we use the Managed Workflows with Apache Spark (MWAA) service from AWS. There is no free tier option for MWAA so as soon as you spin up the example, you will be charged for the environment and any instances. Ensure you are happy to do this before proceeding and I recommend closing down your MWAA instances when finished.

In Chapter 7, Building an Example ML Microservice, we build out a use case leveraging the AWS Forecast service, which is only available in a subset of AWS Regions. To check the availability in your Region, and what Regions you can switch to for that example, you can use https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/.

Technical requirements are given in most of the chapters, but to support this, there are Conda environment .yml files provided in the book repository: https://github.com/PacktPublishing/Machine-Learning-Engineering-with-Python.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book's GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.