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)
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Writing good Python

As discussed throughout this book, Python is an extremely popular and very versatile programming language. Some of the most widely used software products in the world, and some of the most widely used ML engineering solutions in the world, use Python as a core language. Given this scope and scale, it is clear that if we are to write similarly amazing pieces of ML-driven software, we should once again follow the best practices and standards already adopted by these solutions. In the following sections, we will explore what packaging up means in practice, and start to really level up our ML code in terms of quality and consistency.

Recapping the basics

Before we get stuck into some more advanced concepts, let's make sure we are all on the same page and go over some of the basic terminology of the Python world. This will ensure that we apply the right thought processes to the right things and that we can feel confident when writing our code.

In Python, we have the...