Learn Amazon SageMaker

Simon, Julien

  • 出版商: Packt Publishing
  • 出版日期: 2020-08-27
  • 售價: $1,920
  • 貴賓價: 9.5$1,824
  • 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)
    無現貨庫存(No stock available)

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)

快速建立和部署機器學習模型,無需管理基礎架構,並使用Amazon SageMaker的功能(如Amazon SageMaker Studio、Autopilot、Experiments、Debugger和Model Monitor)提高生產力。

主要功能:

- 使用Amazon SageMaker快速建立、訓練和部署機器學習模型。
- 使用機器學習算法和技術分析、檢測和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使您能夠快速建立、訓練和部署大規模的機器學習(ML)模型,無需管理任何基礎架構。它幫助您專注於手頭的ML問題,通過消除ML過程中通常涉及的繁重工作,部署高質量模型。本書是一本針對數據科學家和ML開發人員的全面指南,他們想要深入了解Amazon SageMaker的內幕。

您將了解如何使用SageMaker的各個模塊作為一個單一工具集來解決ML中面臨的挑戰。隨著進展,您將涵蓋AutoML、內置算法和框架以及編寫自己的代碼和算法來構建ML模型的選項。隨後,本書將向您展示如何將Amazon SageMaker與TensorFlow和PyTorch等流行的深度學習庫集成,以增加現有模型的功能。您還將學習如何以最小的努力和更低的成本將模型更快地投入生產。最後,您將探索如何使用Amazon SageMaker Debugger來分析、檢測和突出顯示問題,以了解當前模型狀態並提高模型準確性。

通過閱讀本書,您將能夠在Amazon Web Services(AWS)上使用Amazon SageMaker進行完整的ML工作流程,從實驗、訓練和監控到擴展、部署和自動化。

您將學到什麼:

- 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功能使用AutoPilot構建和訓練機器學習模型。
- 使用內置算法和框架以及自己的代碼創建模型。
- 使用真實世界的示例訓練計算機視覺和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和多種自動化工具在各種配置中自動化部署任務。

本書適合軟件工程師、機器學習開發人員、數據科學家和AWS用戶,他們初次使用Amazon SageMaker並希望在不擔心基礎架構的情況下構建高質量的機器學習模型。為了更有效地理解本書中涵蓋的概念,需要了解AWS基礎知識。對機器學習概念和Python編程語言的一些理解也將有益。

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是一位主要從事人工智慧和機器學習的開發者倡導者。他專注於幫助開發者和企業將他們的想法實現。他經常在會議上演講,並在AWS的部落格和Medium上撰寫文章。在加入AWS之前,Julien在頂尖網絡初創公司擔任了10年的CTO/VP工程師,領導著負責全球數千台服務器的大型軟件和運營團隊。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以及雲計算如何幫助有了深入的理解。

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)

Amazon SageMaker 入門指南
處理資料準備技術
使用 Amazon SageMaker AutoPilot 進行自動機器學習
訓練機器學習模型
訓練電腦視覺模型
訓練自然語言處理模型
使用內建框架擴展機器學習服務
使用您的演算法和程式碼
擴展您的訓練工作
進階訓練技術
部署機器學習模型
自動化機器學習工作流程
優化預測成本和效能

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