Accelerate Deep Learning Workloads with Amazon SageMaker
- Paperback: 278 pages
- Publisher: WOW! eBook (October 28, 2022)
- Language: English
- ISBN-10: 1801816441
- ISBN-13: 978-1801816441
Accelerate Deep Learning Workloads with Amazon SageMaker: Plan and design model serving infrastructure to run and troubleshoot distributed deep learning training jobs for improved model performance
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.
- Cover key capabilities of Amazon SageMaker relevant to deep learning workloads
- Organize SageMaker development environment
- Prepare and manage datasets for deep learning training
- Design, debug, and implement the efficient training of deep learning models
- Deploy, monitor, and optimize the serving of DL models
By the end of this Accelerate Deep Learning Workloads with Amazon SageMaker book, you will be fluent in the software and hardware aspects of running deep learning workloads using Amazon SageMaker.