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

Technical requirements

Throughout the book, all code examples will assume the use of Python 3.10.8 unless specified otherwise. Examples in this edition have been run on a 2022 Macbook Pro with an M2 Apple silicon chip, with Rosetta 2 installed to allow backward compatibility with Intel-based applications and packages. Most examples have also been tested on a Linux machine running Ubuntu 22.04 LTS. The required Python packages for each chapter are stored in conda environment .yml files in the appropriate chapter folder in the book’s Git repository. We will discuss package and environment management in detail later in the book. But in the meantime, assuming you have a GitHub account and have configured your environment to be able to pull and push from GitHub remote repositories, to get started you can clone the book repository from the command line:

git clone https://github.com/PacktPublishing/Machine-Learning-Engineering-with-Python-Second-Edition.git

Assuming you have...