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

Book Description

Plan and design model serving infrastructure to run and troubleshoot distributed deep learning training jobs for improved model performance.

Key Features

• Explore key Amazon SageMaker capabilities in the context of deep learning
• Train and deploy deep learning models using SageMaker managed capabilities and optimize your deep learning workloads
• Cover in detail the 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 Amazo欧米伽n 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 Am卡西欧azon 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卡西欧 you will learn

• 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

Who this book is for

This book is relevant for ML engineers who work on deep learning model development and training, and for Solutions Architects who design and optimize end-to-end deep learning workloads. It assumes familiarity with the Python ecosystem, principles of Machine Learning and Deep Learning, and basic knowledge of the AWS cloud.

中文：

书名：使用Amazon SageMaker加速深度学习工作负载: 使用Amazon SageMaker有效地训练、部署和扩展深度学习模型

计划和设计服务于基础架构的模型，以运行分布式深度学习培训作业并对其进行故障排除，以提高模型性能。

主要功能

• 深度学习背景下探索Amazon SageMaker关键功能
• 使用SageMaker托管能力训练和部署深度学习模型，并优化您的深度学习工作负载
• 涵盖详细的理论和实践方面的培训和托管你的深度学习模型亚马逊SageMaker

图书描述

在过去的10年里，深度学习已经从一个学术研究领域发展到看到多个行业的广泛采用。深度学习模型在广泛的实际任务中展示了出色的结果，支撑了虚拟助手，自动驾驶和机器人技术等新兴领域。在本书中，您将了解在Amazon SageMaker上设计，构建和优化深度学习工作负载的实际方面。本书还提供了流行的深度学习任务的端到端实现示例，例如计算机视觉精工和自然语言处理。您将首先在深度学习的背景下探索Amazon SageMaker的关键功能。然后，您将详细探索在Amazon SageMaker上培训和托管您的深度学习模型的理论和实践方面。您将学习如何使用流行的开源框架训练和服务深度学习模型，并了解Amazon SageMaker上可用的硬件和软件选项。本书还涵盖了各种优化技术，以提高深度学习工作负载的性能和成本特征。

在本书结束时，您将美度精通使用Amazon SageMaker运行深度学习工作负载的软件和硬件方面。

什么你会学到

• 涵盖Amazon SageMaker与深度学习工作负载相关的关键功能
• 整理SageMaker开发环境
• 为深度学习培训准备和管理数据集
• 设计、调试和实现深度学习模型的高效训练
• 部署、监控、优化服务

这本书是为谁准备的

本书适用于从事深度学习模型开发和培训的ML工程师，以及设计和优化端到端深度学习工作负载的解决方案架构师。它假设熟悉Python生态系统、机器学习和深度学习的原理以及AWS云的基本知识。