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Key benefits

This second edition delves deeper into key machine learning topics, CI/CD, and system design

Explore core MLOps practices, such as model management and performance monitoring

Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools

Description

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.

Who is this book for?

This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you’re not a developer but want to manage or understand the product lifecycle of these systems, you’ll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.

What you will learn

Plan and manage end-to-end ML development projects

Explore deep learning, LLMs, and LLMOps to leverage generative AI

Use Python to package your ML tools and scale up your solutions

Get to grips with Apache Spark, Kubernetes, and Ray

Build and run ML pipelines with Apache Airflow, ZenML, and Kubeflow

Detect drift and build retraining mechanisms into your solutions

Improve error handling with control flows and vulnerability scanning

Host and build ML microservices and batch processes running on AWS

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

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Total € 117.97

hawkinflight Sep 11, 2023

I have experience as a statistician, data scientist, software engineer, programmer, and I would say a little bit as an ML engineer. In Chapter 1, the author talks about the different roles, so I look forward to reading that to compare against my experience! haha. I don't have any experience using tools to build pipelines, so I am looking forward to reading about that. I like the content and structure of the book, and with only 9 chapters it's not overwhelming. I feel like I could get up to speed really quickly. I have familiarity with many parts, but not everything. I am interested in reading the section about "Choosing a style" - OOP or FP. I am also interested in exploring the "standard ML patterns" - data lakes, microservices, event-based designs and batching. I am interested in learning more about using AWS, so it's great that that's covered. The chapter on scaling is definitely interesting, as is the chapter on LLMs. I have watched interviews with the OpenAI and MSFT folks on the GPT models and I have interacted with ChatGPT. The LLMs look fun to try and the python code in the book looks very easy to read.I like this book a lot. It concisely convers all the points in moving from concept to solution, including what tools can be used. I think it will be a great starting point for me. I can't wait to try it out!

Amazon Verified review

Ishan Dutta Oct 30, 2023

The width of topics covered along with the code provided makes this a great book! I liked how it started with basics of ML pipelines and went all the way to different LLMOps and so on. The explanation along with the provided diagrams make it easy to understand and retain. I highly recommend this book.

Amazon Verified review

zeroKelvin Sep 09, 2023

There are a lot of books out there that walk you through the steps of putting together a complex ML model using ideal data in a closed setting. This is not one of those books. ML engineering with Python is instead a comprehensive guide to the way machine learning works in practice at most companies.The book does a great job of explaining the MLops tools that almost all businesses today rely on to train, deploy, serve, and iterate on models. In my opinion, the concepts in this book are far more valuable than understanding how to use specific ML frameworks to solve problems. Simply understanding that these tools exist, and knowing how they are used will give engineers a leg up, and lead to more revenue generating impact than any gold medal kaggle model could produce on its own.

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Richard Apr 21, 2024

I recently had the pleasure of reviewing "Machine Learning Engineering with Python - Second Edition" by Andrew McMahon. As a NASA data analyst deeply engaged with the operational side of machine learning, I found this book to be a valuable resource for professionals dedicated to mastering MLOps and managing the lifecycle of ML models. Andrew effectively uses practical examples and a thorough examination of contemporary tools and methodologies to advance this field.One of the standout features of this book is McMahon's approach to integrating Python code to clarify the mechanics behind ML algorithms. While I chose not to run the scripts verbatim, I found them incredibly useful as references, enhancing both my existing projects and new initiatives. This method greatly assisted me in understanding the intricacies of ML pipelines and applying these insights across various applications.A suggestion for future readers would be to approach the first chapter last. The book begins with advanced topics that are more comprehensible after navigating through the foundational material presented in subsequent chapters. This adjustment could help flatten the learning curve and not become discouraged at the advanced material.That said, there are areas where the book could improve. The chapter dedicated to generative AI and large language models, for instance, would benefit from additional case studies that demonstrate their practical applications within industry. Moreover, a deeper focus on the ethical considerations of deploying AI systems at scale is necessary, given the increasing importance of ethics in our field.In conclusion, Andrew McMahon’s second edition is a comprehensive guide that I highly recommend to MLOps practitioners, ML engineers, and data scientists. Its depth of content, combined with practical, real-world applications, positions it as a critical read for professionals aiming to stay at the forefront of technology. If you're in the field, this book is undoubtedly a valuable addition to your professional toolkit.

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Rajesh Sathya Kumar Apr 04, 2024

I have been reading this book by Andy McMahon and just completed it. The book provided excellent coverage of ML Ops concepts, encompassing a wide range of ideas for building ML-powered apps.The Second Edition of this book also covers concepts from LLM and LLMOps. It also includes deeper content in every chapter. The amount of AI developments from 2021 (First edition) to 2023 (Second edition) is very evident from this book and makes it more exciting about the future.It also covers practical examples and applications built using scikit-learn, Spark, Airflow, Kubernetes, Keras, AWS, etc., and lists the key points discussed in each chapter.

Amazon Verified review

Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples , Second Edition

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