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

I got an opportunity to but this book to improve my Machine Learning knowledge this book has been nothing short of gem, the detailed description of all the concepts with example and screenshots where suitable makes it an interactive sessions.This book is also good for beginners because it starts with various definitions of career tracks and how to effective work with the team. It is a great book who want to kick start the career i MLOPS and work all the way through lifecycle of the MLOps.This book is Must have

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

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!

It is a comprehensive guide that provides a practical and insightful approach to the world of machine learning engineering. This book delves into the intricacies of building and deploying machine learning models, making it a valuable resource for both beginners and experienced practitioners in the field.The book is thoughtfully organized into nine chapters, each of which provides invaluable insights and practical knowledge. Let's delve into the essence of each chapter:Chapter 1: Introduction to ML EngineeringThe book kicks off with a solid introduction to the world of machine learning engineering with taxonomy of data disciplines, ML design with Python and setting the stage for the subsequent chapters. It explains key concepts and terminology, making it accessible to readers new to the field.Chapter 2: The Machine Learning Development ProcessThis chapter delves deep into the machine learning development process, and introduces Concept to solution in four steps: Dicover, Play, Develop, Deploy.It provides a structured framework for building robust machine learning models.Chapter 3: From Model to Model FactoryThis chapter delves deep into the heart of machine learning - the model factory. It addresses the nuances of building and maintaining a model factory, including feature engineering, training systems, model persistence, and the use of pipelines. The inclusion of learning about learning adds a layer of sophistication to the discussion.Chapter 4: Packaging UpChapter 4 deals with the practical aspect of writing and packaging Python code effectively. The author not only emphasizes writing good code but also guides readers through packaging, testing, logging, securing, and error handling - crucial aspects of any ML project.Chapter 5: Deployment Patterns and ToolsThe topic of deploying ML models is a critical one, and Chapter 5 provides readers with various deployment patterns and tools. From containerization to hosting microservices on AWS and building ML pipelines with Airflow, this chapter equips readers with a vast toolbox for deployment.Chapter 6: Scaling UpScaling ML solutions is a necessity in today's data-driven world. This chapter explores different scaling techniques, including Spark, serverless infrastructure, Kubernetes, and Ray, while also highlighting the importance of system design at scale.Chapter 7: Deep Learning, Generative AI, and LLMOpsAs the field of machine learning continues to evolve, deep learning and generative AI are at the forefront. Chapter 7 dives into these advanced topics and introduces the concept of LLMOps (Machine Learning Model Operations), showcasing the cutting-edge technologies shaping the future.Chapter 8: Building an Example ML MicroserviceThis chapter takes a practical approach by walking readers through the development of an ML microservice. From understanding the problem to selecting tools and deploying to Kubernetes, readers get a hands-on experience that bridges the gap between theory and practice.Chapter 9: Building an Extract, Transform, Machine Learning Use CaseThe final chapter demonstrates how to address batch processing problems with an ETML (Extract, Transform, Machine Learning) solution. It covers tool selection, interfaces, scaling models, and pipeline scheduling, all while incorporating advanced Airflow features."Machine Learning Engineering with Python - Second Edition" excels in its ability to blend theory with hands-on practice. The inclusion of technical requirements, real-world examples, and practical implementation details makes it an invaluable resource for readers at all skill levels. Additionally, the book's focus on modern tools and technologies keeps it current and relevant.In conclusion, this book is a must-have for anyone interested in mastering the art and science of machine learning engineering. It's a comprehensive guide that empowers readers to not only understand the intricacies of ML engineering but also to excel in the field. Highly recommended!

I only had the chance to skim the book, but it seems like a good overview. The author does go through some comprehensive examples of deployments (all on AWS), but since I'm working with data drift right now I jumped to that section and read it. It seems like the data drift section is a very high-level overview, but I suppose an entire book could be written on it. I wouldn't recommend using the KS statistic mentioned for drift since it can be overly sensitive especially with larger datasets. Jensen-Shannon distance is another one to use, or Pearson correlations between histograms (with outliers removed, importantly). Overall it seems like a good reference tome and something for learning some new techniques and Python packages for ML Ops.