Accelerate Deep Learning Workloads with Amazon SageMaker: Train, deploy, and scale deep learning models effectively using Amazon SageMaker
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Learn to implement end-to-end deep learning on Amazon SageMaker with practical examples.
Key Features:
Explore key Amazon SageMaker capabilities in the context of deep learningBuild, train and host DL models using SageMaker managed capabilitiesCover in detail theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMaker
Book Description:
Over the past 10 years, deep learning has grown from being an academic research field to seeing wide-scale adoption across multiple industries. Deep learning models demonstrate excellent results on a wide range of practical tasks, underpinning emerging fields such as virtual assistants, autonomous driving, and robotics. In this book, you will learn about the practical aspects of designing, building, and optimizing deep learning workloads on Amazon SageMaker. The book also provides end-to-end implementation examples for popular deep learning tasks, such as computer vision and natural language processing.
You will begin by exploring key Amazon SageMaker capabilities in the context of deep learning. Then, you will explore in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMaker. You will learn how to train and serve deep learning models using popular open source frameworks and understand the hardware and software options available for you on Amazon SageMaker. The book also covers various optimizations technique to improve the performance and cost characteristics of your deep learning workloads.
By the end of this book, you will be fluent in the software and hardware aspects of running deep learning workloads using Amazon SageMaker.
What You Will Learn:
Explore the key capabilities of Amazon SageMaker relevant to deep learning workloadsOrganize SageMaker development environmentPrepare and manage datasets for deep learning trainingDesign, debug, and implement the efficient training of deep learning modelsDeploy, monitor, and optimize the serving of deep learning models
Who this book is for:
This book is written for deep learning and AI engineers who have a working knowledge of the Deep Learning domain and who wants to learn and gain practical experience in training and hosting DL models in the AWS cloud using Amazon SageMaker service capabilities.
Publisher : Packt Publishing (October 28, 2022)
Language : English
Paperback : 278 pages
ISBN-10 : 1801816441
ISBN-13 : 978-1801816441
Item Weight : 1.08 pounds
Dimensions : 9.25 x 7.52 x 0.59 inches
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4 reviews for Accelerate Deep Learning Workloads with Amazon SageMaker: Train, deploy, and scale deep learning models effectively using Amazon SageMaker
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Colin G –
Excellent reference for anyone performing deep learning in sagemaker
The content is both comprehensive and concisely organized & written. I appreciated the attention to detail in the storage optimization section; the recommendations and best practices provided for optimizing inference are also very valuable — too often, the key differences between production inference and pre-prod development are glossed over; perhaps, because so few projects actually make it to production? With this book, you’ll be a lot less likely to fall into that camp.
Om S –
Unleashing the Power of Amazon SageMaker for Deep Learning Workloads: A Comprehensive Guide
The book “Accelerate Deep Learning Workloads with Amazon SageMaker” is a comprehensive guide for deep learning practitioners. It covers all the key topics related to using Amazon SageMaker for deep learning, from the basics of introducing deep learning with Amazon SageMaker to operationalizing inference workloads. The book is well-organized and provides a clear and concise overview of the different aspects of deep learning workloads with Amazon SageMaker.The author provides a thorough explanation of deep learning frameworks and containers on SageMaker and managing the SageMaker development environment. The book also covers the important topic of managing deep learning datasets, including considerations for hardware for deep learning training and engineering distributed training. The author also goes into detail on how to implement model servers and operationalize inference workloads, including considerations for hardware for inference.Overall, the book is an excellent resource for anyone looking to leverage Amazon SageMaker for deep learning workloads. It is well-written, easy to understand, and provides practical advice on how to design, debug, deploy, monitor, and optimize deep learning models.Summery is really useful at end of the every chapter.Yes, a case study could have added more depth and practical application to the concepts discussed in the book. A real-world example would have helped to illustrate the concepts and provide a more concrete understanding of how to apply the theories discussed in the book. The addition of a case study could have made the book even more valuable to the reader, as it would have provided a more hands-on, real-world perspective on the use of Amazon SageMaker for deep learning workloads.If you’re looking for a comprehensive guide to Amazon SageMaker for deep learning, this book is a must-read.
Didi –
If you run deep learning workloads on Amazon SageMaker, this is THE book for you
This book is packed with useful and practical information on using Amazon SageMaker for deep learning (DL). The coverage is both broad and deep, and the exposition is clear and focused.The author does a wonderful job in describing the most important features of Amazon SageMaker for running DL workloads. Detailed information is provided on Amazon SageMaker’s capabilities related to the entire ML lifecycle – model development, training and inference – and useful advice is given on various important aspects of DL training and inference, such as understanding cloud costs and addressing distributed training needs.I’d recommend this book to any ML engineer looking to gain a better understanding of how to effectively utilize Amazon SageMaker’s rapidly expanding feature set, as well as to data scientists and researchers looking for a practical guide on scaling up training and deployment in a cutting-edge cloud environment.In summary, this is an excellent book that will prove indispensable for those interested in running DL workloads on one of the most advanced ML platforms in the market – Amazon SageMaker. Beginners and experts alike can greatly benefit from the detailed and practical information in this book. Highly recommended!
tt0507 –
Great book with a lot of detail
I would recommend this book to any ML engineer looking to better understand how to use Amazon SageMaker’s features. The book provides many details regarding possible ways to utilize the services provided by Sagemaker. I would also recommend the book to data scientists and researchers looking for a practical guide on SageMaker. Overall, I believe is an excellent book that will come in handy for both beginners and experienced ML engineers.