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

Product type Paperback

Published in Aug 2023

Publisher Packt

ISBN-13 9781837631964

Length 462 pages

Edition 2nd Edition

Languages

Python

Tools

GitHub

Concepts

Machine Learning

Author (1):

Andrew P. McMahon

View More author details

Table of Contents (12) Chapters

Preface

1. Introduction to ML Engineering

2. The Machine Learning Development Process FREE CHAPTER

3. From Model to Model Factory

4. Packaging Up

5. Deployment Patterns and Tools

6. Scaling Up

7. Deep Learning, Generative AI, and LLMOps

8. Building an Example ML Microservice

9. Building an Extract, Transform, Machine Learning Use Case

10. Other Books You May Enjoy

11. Index

Technical requirements

As in Chapter 1, Introduction to ML Engineering if you want to run the examples provided here, you can create a Conda environment using the environment YAML file provided in the Chapter02 folder of the book’s GitHub repository:

conda env create –f mlewp-chapter02.yml

On top of this, many of the examples in this chapter will require the use of the following software and packages. These will also stand you in good stead for following the examples in the rest of the book:

Anaconda
PyCharm Community Edition, VS Code, or another Python-compatible IDE
Git

You will also need the following:

An Atlassian Jira account. We will discuss this more later in the chapter, but you can sign up for one for free at https://www.atlassian.com/software/jira/free.
An AWS account. This will also be covered in the chapter, but you can sign up for an account at https://aws.amazon.com/. You will need to add payment details to sign up for AWS, but everything we do in this book will only require the free tier solutions.

The technical steps in this chapter were all tested on both a Linux machine running Ubuntu 22.04 LTS with a user profile that had admin rights and on a Macbook Pro M2 with the setup described in Chapter 1, Introduction to ML Engineering. If you are running the steps on a different system, then you may have to consult the documentation for that specific tool if the steps do not work as planned. Even if this is the case, most of the steps will be the same, or very similar, for most systems. You can also check out all of the code for this chapter in the book’s repository at https://github.com/PacktPublishing/Machine-Learning-Engineering-with-Python-Second-Edition/tree/main/Chapter02. The repo will also contain further resources for getting the code examples up and running.