Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples, 2nd Edition Paperback – 11 Sept. 2023

This book is so very helpful both as a reference that can be used for seasoned MLOps veterans in developing , distributing and curating models and also in instructing newcomers in the basics of MLOps (providing examples and explaining the basics behind transformers, neural network models and LLMs). It even provided some background in Python to fill in the gaps in my knowledge where my university courses fell short! As someone who plans to enter the ML/LLM/AI field after graduation, this will be my go-to guide!

Andy McMahon invited me to review the second edition of his book "Machine Learning Engineering with Python", which was published earlier this week.I have to say I REALLY enjoyed this read! 😃 Not only does it dive deep into the crucial role of ML engineers, who serves an acute need to translate the world of data science modelling and exploration into the world of software products and systems engineering. It also uses real world examples on how this role is shaped and how AI/ML applications actually go from Proof-of-Concept (PoC) all the way into production (which is so much harder than most of us woyld think).This is not a theoritical book, it is fully hands-on with code samples and fully fledged applications, which makes it somuch more valuable. And it has an entire chapter covering Deep Learning, Generative AI, and LLMOps (which I believe will be the most important topic of the coming months and years).I highly recommend this book to anyone who wants to actually leverage the power of AI & Machine Learning in production. Well done, Andy

I was (am) really looking forward to sinking my teeth into this book and the numerous topics covered - however my progress has been scuppered by many errors trying to get the python libraries working correctly. So I haven't even got the chance yet to get the ball rolling! I've tried across Mac (issues with Apple silicon) and Windows (Intel) - and just have too many issues out the gate to bother continuing. pity.

For anyone passionate about MLOps, I wholeheartedly recommend this book.Key Insights:- Simplifies the multifaceted roles in ML, providing clarity in a dense field.- Through hands-on examples, tools like ZenML and Kubeflow are demystified.- Provides insights into designing scalable ML systems using tools like Ray.- The book's detailed approach to MLOps for LLMs, with a clear focus on validation and achieving peak performance, is notably distinctive and comprehensive.The book doesn't just dwell on theory; instead, it's deeply practical with tangible code examples, all available on GitHub. Look out for the chapters on ML Development Process and how to automate it, ML System Deployment Patterns, Deep Learning, GenAI and LLMOps.