Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker

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Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker Customer Reviews

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• 5.0 out of 5 stars from Wesley Pasfield -- Phenomenal overview of the breadth and depth of Amazon SageMaker : Overall this is an excellent and practical overview of the full breadth of the SageMaker platform, I’d recommend this to anyone who is using SageMaker on AWS for full end-to-end machine learning workflows. There are an overwhelming number of products within the SageMaker platform, and this book does a great job of clearly explaining the benefit of each product, and placing it within the broader AWS ecosystem. Often with machine learning platforms and tutorials there’s an over-emphasis on the modeling portion of the process, but this book covers the full machine learning lifecycle including model management, versioning and deployment, and I really appreciated the book’s practical focus. ( Reviewed in the United States on September 24, 2021 )
• 4.0 out of 5 stars from Samuel -- A panoramic view of AWS Sagemaker : This book provides a panoramic view of AWS Sagemaker. Although I've used Sagemaker intensively for my job, I still learn a lot from this book. Particularly, the sections about a/b testing and model monitoring are indeed eye-opening. ( Reviewed in the United States on September 30, 2021 )
• 5.0 out of 5 stars from Jim -- Amazon SageMaker Practical Advices for End-to-End Machine Learning Process : This is a comprehensive book on applying Amazon SageMaker to the whole lifecycle of a machine learning process. I highly recommend it to anyone who uses Amazon SageMaker to develop machine learning applications, especially the systems with large-scale datasets. The book provides many advices with examples on how to address data science challenges with large-scale datasets such as data pre-processing and preparation at scale, model training at scale using big data pipelines. In addition, the book also shows how to integrate machine learning pipelines with MLOps principles to automate the development of a secure and performant machine learning application system. ( Reviewed in the United States on October 27, 2021 )
• 5.0 out of 5 stars from Amrita -- Pretty exhaustive coverage of Sagemaker features : The book is a great addition to the documentation around AWS Sagemaker - from my personal experience, Sagemaker is a very powerful MLOps tool, but the official documentation/examples/tutorials are definitely too sparse on many details. This book would be a great resource for both beginners and advanced users. I think one thing the book could have covered in more detail is examples on how Sagemaker can be adapted to existing ML workflows - since that is not always obvious, as the book covers each Sagemaker feature separately. ( Reviewed in the United States on October 28, 2021 )

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