Accelerate Deep Learning Workloads with Amazon SageMaker: Train, deploy, and scale deep learning models effectively using Amazon SageMaker

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 learning
• Build, train and host DL models using SageMaker managed capabilities
• Cover 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 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 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.

學習使用實際範例在Amazon SageMaker上實施端到端的深度學習。

主要特點：
- 在深度學習的背景下探索Amazon SageMaker的關鍵功能
- 使用SageMaker的管理功能來建立、訓練和托管深度學習模型
- 詳細介紹在Amazon SageMaker上訓練和托管深度學習模型的理論和實踐方面

書籍描述：
在過去的10年中，深度學習從學術研究領域發展成為在多個行業中廣泛應用的領域。深度學習模型在各種實際任務上展示出優異的結果，支撐著虛擬助手、自動駕駛和機器人等新興領域的發展。在本書中，您將學習在Amazon SageMaker上設計、構建和優化深度學習工作負載的實際方面。該書還提供了流行的深度學習任務（如計算機視覺和自然語言處理）的端到端實施示例。

您將首先在深度學習的背景下探索Amazon SageMaker的關鍵功能。然後，您將詳細了解在Amazon SageMaker上訓練和托管深度學習模型的理論和實踐方面。您將學習如何使用流行的開源框架訓練和提供深度學習模型，並了解Amazon SageMaker上可用的硬件和軟件選項。該書還介紹了各種優化技術，以提高深度學習工作負載的性能和成本特性。

通過閱讀本書，您將熟悉使用Amazon SageMaker運行深度學習工作負載的軟件和硬件方面。

您將學到什麼：
- 探索Amazon SageMaker與深度學習工作負載相關的關鍵功能
- 組織SageMaker開發環境
- 為深度學習訓練準備和管理數據集
- 設計、調試和實施高效的深度學習模型訓練
- 部署、監控和優化深度學習模型的提供

本書適合對深度學習和人工智能有一定了解的工程師，他們希望在AWS雲端使用Amazon SageMaker服務能力訓練和托管深度學習模型並獲得實際經驗。