Book Image

Machine Learning Engineering with Python - Second Edition

By : Andrew P. McMahon
2.5 (2)
Book Image

Machine Learning Engineering with Python - Second Edition

2.5 (2)
By: Andrew P. McMahon

Overview of this book

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.
Table of Contents (12 chapters)
10
Other Books You May Enjoy
11
Index

To get the most out of this book

  • In this book, some previous exposure to Python development is assumed. Many introductory concepts are covered for completeness but in general it will be easier to get through the examples if you have already written at least some Python programs before. The book also assumes some exposure to the main concepts from machine learning, such as what a model is, what training and inference refer to and an understanding of similar concepts. Several of these are recapped in the text but again it will be a smoother ride if you have previously been acquainted with the main ideas behind building a machine learning model, even at a rudimentary level.
  • On the technical side, 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 other software packages and applications. For many of the examples, access to a UNIX type terminal, such as bash or zsh, is assumed. The examples in this book were written and tested on both a Linux machine running Ubuntu LTS and an M2 Macbook Pro running macOS. If you use a different setup, for example Windows, the examples may require some translation in order to work for your system. Note that the use of the M2 Macbook Pro means several examples show some additional information to get the examples working on Apple Silicon devices. These sections can comfortably be skipped if your system does not require this extra setup.
  • Many of the Cloud based examples leverage Amazon Web Services (AWS) and so require an AWS account with billing setup. Most of the examples will use the free-tier services available from AWS but this is not always possible. Caution is advised in order to avoid large bills. If in doubt, it is recommended you consult the AWS documentation for more information. As a concrete example of this, 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 tearing down your MWAA instances when finished.
  • Conda and Pip are used for package and environment management throughout this book, but Poetry is also used in many cases. To facilitate easy reproduction of development environments for each chapters in the book’s GitHub repository, (https://github.com/PacktPublishing/Machine-Learning-Engineering-with-Python-Second-Edition), each chapter of the book has a corresponding folder and within that folder are requirements.txt and Conda environment.yml files, as well as helpful README files. The commands for replicating the environments and any other requirements are given at the beginning of each chapter within the book.
  • If you are using the digital version of this book, I still adviseyou to type the code yourself or access the code from the book’s GitHub repository (https://github.com/PacktPublishing/Machine-Learning-Engineering-with-Python-Second-Edition). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

As mentioned above, the code bundle for the book is hosted on GitHub at https://github.com/PacktPublishing/Machine-Learning-Engineering-with-Python-Second-Edition. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/LMqir.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “First, we must import the TabularDrift detector from the alibi-detect package, as well as the relevant packages for loading and splitting the data.”

A block of code is set as follows:

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
import alibi
from alibi_detect.cd import TabularDrift

Any command-line input or output is written as follows and are indicated as command-line commands in the main body of the text:

pip install tensorflow-macos

Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: “Select the Deploy button. This will provide a dropdown where you can select Create service.”

References to additional resources or background information appear like this.

Helpful tips and important caveats appear like this.