Learn Amazon SageMaker
暫譯: 學習 Amazon SageMaker

Simon, Julien

Learn Amazon SageMaker
  • 出版商: Packt Publishing
  • 出版日期: 2020-08-27
  • 售價: $2,010
  • 貴賓價: 9.5 折 $1,910
  • 語言: 英文
  • 頁數: 490
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 180020891X
  • ISBN-13: 9781800208919
  • 相關分類: Computer Vision

    • 海外代購書籍(需單獨結帳)

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商品描述

Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor

Key Features

  • Build, train, and deploy machine learning models quickly using Amazon SageMaker
  • Analyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniques
  • Improve productivity by training and fine-tuning machine learning models in production

Book Description

Amazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker.

You'll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you'll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You'll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you'll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy.

By the end of this Amazon book, you'll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation.

What you will learn

  • Create and automate end-to-end machine learning workflows on Amazon Web Services (AWS)
  • Become well-versed with data annotation and preparation techniques
  • Use AutoML features to build and train machine learning models with AutoPilot
  • Create models using built-in algorithms and frameworks and your own code
  • Train computer vision and NLP models using real-world examples
  • Cover training techniques for scaling, model optimization, model debugging, and cost optimization
  • Automate deployment tasks in a variety of configurations using SDK and several automation tools

Who this book is for

This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.

商品描述(中文翻譯)

快速構建和部署機器學習模型，而無需管理基礎設施，並利用 Amazon SageMaker 的功能（如 Amazon SageMaker Studio、Autopilot、Experiments、Debugger 和 Model Monitor）來提高生產力。

主要特點

- 使用 Amazon SageMaker 快速構建、訓練和部署機器學習模型
- 使用機器學習算法和技術分析、檢測並接收與各種業務問題相關的警報
- 通過在生產環境中訓練和微調機器學習模型來提高生產力

書籍描述

Amazon SageMaker 使您能夠快速構建、訓練和大規模部署機器學習（ML）模型，而無需管理任何基礎設施。它幫助您專注於當前的 ML 問題，並通過消除每個 ML 過程步驟中通常涉及的繁重工作來部署高質量模型。本書是數據科學家和機器學習開發人員的綜合指南，旨在幫助他們了解 Amazon SageMaker 的各個方面。

您將了解如何使用 SageMaker 的各種模組作為單一工具集來解決 ML 面臨的挑戰。隨著進展，您將涵蓋 AutoML、內建算法和框架等功能，以及編寫自己的代碼和算法來構建 ML 模型的選項。稍後，本書將向您展示如何將 Amazon SageMaker 與流行的深度學習庫（如 TensorFlow 和 PyTorch）集成，以增強現有模型的能力。您還將學習如何以最低的努力和成本更快地將模型投入生產。最後，您將探索如何使用 Amazon SageMaker Debugger 來分析、檢測和突出問題，以了解當前模型狀態並提高模型準確性。

在本書結束時，您將能夠在整個 ML 工作流程中使用 Amazon SageMaker，從實驗、訓練和監控到擴展、部署和自動化。

您將學到的內容

- 在 Amazon Web Services (AWS) 上創建和自動化端到端的機器學習工作流程
- 熟悉數據標註和準備技術
- 使用 AutoML 功能通過 AutoPilot 構建和訓練機器學習模型
- 使用內建算法和框架以及您自己的代碼創建模型
- 使用真實世界示例訓練計算機視覺和自然語言處理（NLP）模型
- 涵蓋擴展、模型優化、模型調試和成本優化的訓練技術
- 使用 SDK 和多種自動化工具自動化各種配置中的部署任務

本書適合誰

本書適合軟體工程師、機器學習開發人員、數據科學家和新手 AWS 使用者，他們希望在不擔心基礎設施的情況下構建高質量的機器學習模型。了解 AWS 基礎知識將有助於更有效地掌握本書所涵蓋的概念。對機器學習概念和 Python 程式語言的基本理解也將是有益的。

作者簡介

Julien Simon is a principal AI and machine learning developer advocate. He focuses on helping developers and enterprises to bring their ideas to life. He frequently speaks at conferences and blogs on AWS blogs and on Medium. Prior to joining AWS, Julien served for 10 years as CTO/VP of engineering in top-tier web start-ups where he led large software and ops teams in charge of thousands of servers worldwide. In the process, he fought his way through a wide range of technical, business, and procurement issues, which helped him gain a deep understanding of physical infrastructure, its limitations, and how cloud computing can help.

作者簡介(中文翻譯)

Julien Simon 是一位主要的人工智慧和機器學習開發者倡導者。他專注於幫助開發者和企業將他們的想法變為現實。他經常在會議上發表演講，並在 AWS 博客和 Medium 上撰寫文章。在加入 AWS 之前，Julien 在頂尖的網路初創公司擔任首席技術官/工程副總裁，任職長達 10 年，負責領導大型軟體和運營團隊，管理全球數千台伺服器。在這個過程中，他克服了各種技術、商業和採購問題，這使他對物理基礎設施及其限制有了深入的理解，以及雲端運算如何提供幫助。

目錄大綱

  1. Getting Started with Amazon SageMaker
  2. Handling Data Preparation Techniques
  3. AutoML with Amazon SageMaker AutoPilot
  4. Training Machine Learning Models
  5. Training Computer Vision Models
  6. Training Natural Language Processing Models
  7. Extending Machine Learning Services Using Built-In Frameworks
  8. Using Your Algorithms and Code
  9. Scaling Your Training Jobs
  10. Advanced Training Techniques
  11. Deploying Machine Learning Models
  12. Automating Machine Learning Workflows
  13. Optimizing Prediction Cost and Performance

目錄大綱(中文翻譯)


  1. Getting Started with Amazon SageMaker

  2. Handling Data Preparation Techniques

  3. AutoML with Amazon SageMaker AutoPilot

  4. Training Machine Learning Models

  5. Training Computer Vision Models

  6. Training Natural Language Processing Models

  7. Extending Machine Learning Services Using Built-In Frameworks

  8. Using Your Algorithms and Code

  9. Scaling Your Training Jobs

  10. Advanced Training Techniques

  11. Deploying Machine Learning Models

  12. Automating Machine Learning Workflows

  13. Optimizing Prediction Cost and Performance

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