Computer Vision on AWS: Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker
暫譯: 在AWS上進行電腦視覺:使用Amazon Rekognition、Lookout for Vision和SageMaker構建和部署實際的CV解決方案

Mullennex, Lauren, Bachmeier, Nate, Rao, Jay

  • 出版商: Packt Publishing
  • 出版日期: 2023-03-31
  • 售價: $1,650
  • 貴賓價: 9.5$1,568
  • 語言: 英文
  • 頁數: 324
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1801078688
  • ISBN-13: 9781801078689
  • 相關分類: Amazon Web ServicesMakerComputer Vision
  • 立即出貨 (庫存=1)

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商品描述

Scale up your Windows containers seamlessly on AWS powered by field-proven expertise and best practices on Amazon ECS, EKS, and Fargate

Purchase of the print or Kindle book includes a free PDF eBook


Key Features:

  • Learn how to quickly deploy and automate end-to-end CV pipelines on AWS
  • Implement design principles to mitigate bias and scale production of CV workloads
  • Work with code examples to master CV concepts using AWS AI/ML services


Book Description:

Computer vision (CV) is a field of artificial intelligence that helps transform visual data into actionable insights to solve a wide range of business challenges. This book provides prescriptive guidance to anyone looking to learn how to approach CV problems for quickly building and deploying production-ready models.

You'll begin by exploring the applications of CV and the features of Amazon Rekognition and Amazon Lookout for Vision. The book will then walk you through real-world use cases such as identity verification, real-time video analysis, content moderation, and detecting manufacturing defects that'll enable you to understand how to implement AWS AI/ML services. As you make progress, you'll also use Amazon SageMaker for data annotation, training, and deploying CV models. In the concluding chapters, you'll work with practical code examples, and discover best practices and design principles for scaling, reducing cost, improving the security posture, and mitigating bias of CV workloads.

By the end of this AWS book, you'll be able to accelerate your business outcomes by building and implementing CV into your production environments with the help of AWS AI/ML services.


What You Will Learn:

  • Apply CV across industries, including e-commerce, logistics, and media
  • Build custom image classifiers with Amazon Rekognition Custom Labels
  • Create automated end-to-end CV workflows on AWS
  • Detect product defects on edge devices using Amazon Lookout for Vision
  • Build, deploy, and monitor CV models using Amazon SageMaker
  • Discover best practices for designing and evaluating CV workloads
  • Develop an AI governance strategy across the entire machine learning life cycle


Who this book is for:

If you are a machine learning engineer or data scientist looking to discover best practices and learn how to build comprehensive CV solutions on AWS, this book is for you. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.

商品描述(中文翻譯)

在 AWS 上無縫擴展您的 Windows 容器,依靠經過實地驗證的專業知識和 Amazon ECS、EKS 和 Fargate 的最佳實踐

購買印刷版或 Kindle 書籍包括免費 PDF 電子書


主要特點:


  • 學習如何快速在 AWS 上部署和自動化端到端的計算機視覺 (CV) 管道

  • 實施設計原則以減少偏見並擴展 CV 工作負載的生產

  • 使用代碼示例掌握使用 AWS AI/ML 服務的 CV 概念


書籍描述:
計算機視覺 (CV) 是人工智慧的一個領域,幫助將視覺數據轉化為可行的見解,以解決各種商業挑戰。本書為任何希望學習如何處理 CV 問題以快速構建和部署生產就緒模型的人提供了指導。

您將首先探索 CV 的應用以及 Amazon Rekognition 和 Amazon Lookout for Vision 的特性。然後,本書將帶您了解實際用例,例如身份驗證、實時視頻分析、內容審核和檢測製造缺陷,這將使您了解如何實施 AWS AI/ML 服務。隨著進展,您還將使用 Amazon SageMaker 進行數據標註、訓練和部署 CV 模型。在結尾章節中,您將使用實用的代碼示例,並發現擴展、降低成本、改善安全性和減少 CV 工作負載偏見的最佳實踐和設計原則。

在這本 AWS 書籍結束時,您將能夠通過構建和實施 CV 進入您的生產環境,來加速您的商業成果,並借助 AWS AI/ML 服務的幫助。


您將學到什麼:


  • 在各行各業中應用 CV,包括電子商務、物流和媒體

  • 使用 Amazon Rekognition Custom Labels 構建自定義圖像分類器

  • 在 AWS 上創建自動化的端到端 CV 工作流程

  • 使用 Amazon Lookout for Vision 在邊緣設備上檢測產品缺陷

  • 使用 Amazon SageMaker 構建、部署和監控 CV 模型

  • 發現設計和評估 CV 工作負載的最佳實踐

  • 在整個機器學習生命週期中發展 AI 治理策略


本書適合誰:
如果您是一名機器學習工程師或數據科學家,想要發現最佳實踐並學習如何在 AWS 上構建全面的 CV 解決方案,本書適合您。了解 AWS 基礎知識是更有效掌握本書所涵蓋概念的必要條件。對機器學習概念和 Python 程式語言的扎實理解也將是有益的。