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1. Practical Introduction to the AWS MLOps suite of Services
   This book is loaded with lots of practical knowledge on how to use the ML services of AWS. This is not a dry cookbook, but also explains what and why. Of note, the book is closely tied to the “Practical Data Science on the AWS Cloud” course on Coursera. The authors are also part of the instructor team for this course. Everything in the course is in the book. But the book has more depth and additional material. Reading the section of the book really enriched the lectures and helped in working on the assignments. But the examples in the book and the assignments in the class are not the same. But it does help to have another example.Overall, I would recommend this book, and the course, to anyone who has a basic familiarity of ML concepts and needs to learn how to implement a MLOps pipeline in an AWS cloud environment.

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2. DEA n MLS exams
   Got sagemaker n udemy classes!!AWS mls-c01 sagemaker Studio udemy classes and exams!!

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3. It’s not just a book you read once; it’s a reference guide
   If you’re looking to learn how to build machine learning workflows using AWS, this book is a fantastic choice. It covers a wide range of AWS services like SageMaker, Lambda, and Step Functions, showing how to use them together to create powerful data science pipelines. The explanations are clear and easy to understand, even for topics that can be quite technical.What stands out most is how the book is organized. It starts with the basics and gradually moves to advanced topics, making it great for readers at all levels. The authors include step-by-step examples and practical projects, which make it easy to follow along and apply what you learn to real-world tasks.Another highlight is the focus on scalability and automation. These are essential when putting machine learning models into production, and the book goes beyond just teaching the tools—it also explains best practices for optimizing workflows, tracking performance, and keeping your models reliable.Whether you’re new to AWS or experienced in data science, this book has something for everyone. Highly recommended!

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4. This book is so great that I bought 12 extra copies to distribute to my team!
   Very well written, this book covers many AWS services across the entire Amazon AI/ML data science stack. After clearly explaining the value proposition of doing data science in the cloud, the authors navigate the reader through an complete end-to-end machine learning pipeline using the latest in natural language processing techniques including BERT, HuggingFace transformers, and Amazon SageMaker. The authors demonstrate how to implement automated pipelines using TensorFlow, PyTorch, MXNet, Python, and even Java! This book has both technical depth and practical breadth. This book helped me prepare for – and complete – my AWS ML Specialty certification. It was a true delight to read!

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5. Very practical
   I like how the authors present the contents. There is a good balance between sample code and explanation.There are also many related insights such as Parquet format diagram, compression consideration, performance consideration, etc. The code repository is being actively maintained as well.

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6. Good mixture of technical and usable examples
   I have been following this book since it was in beta thanks to an Oreilly subscription. I have also attended a workshop put on by the authors. It has greatly helped my overall understanding of how to practically implement in AWS. You can use this as a framework to figure out how and what services to implement.

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7. It just arrived but the front page and first pages where folded
    

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8. Amazing book for performing end to end machine learning on AWS Cloud
   Loved the way the book is structured and very well written.Follows the end to end approach on performing machine learning, specifically on tools available on AWS for moving the ml models in production following the industry best practices.

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9. Lo usé para una prácticas de AWS en la escuela y me ayudó bastante.Mi gato también lo aprueba.

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10. Gook book, which provides good guidelines based on a vast amount of practical examples about how to use the AWS Ecosystem Toward data science correctly and accordingly.

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11. The book is great to discover the different services and tools for AI/ML in AWS. But the code in the GitHub repository is very messy, and doesn’t work anymore, which goes against the goal of the book.

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12. This book is great!

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13. It is interesting, mainly for those that want to get used to some data science jargon, to get an overview of some processes, and tools. But for the price, it doesn’t worth the money, it is very superficial, with too much high-level explanation. If the price of the book were much lower, maybe should deserve more stars, but for that price, it falls short.

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