Deploying AI in the Enterprise: It Approaches for Design, Devops, Governance, Change Management, Blockchain, and Quantum Computing
暫譯: 在企業中部署AI：設計、DevOps、治理、變更管理、區塊鏈與量子計算的IT方法

Hechler, Eberhard, Oberhofer, Martin, Schaeck, Thomas

Deploying AI in the Enterprise: It Approaches for Design, Devops, Governance, Change Management, Blockchain, and Quantum Computing
  • 出版商: Apress
  • 出版日期: 2020-09-26
  • 售價: $2,350
  • 貴賓價: 9.5 折 $2,233
  • 語言: 英文
  • 頁數: 590
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484262050
  • ISBN-13: 9781484262054
  • 相關分類: DevOps、人工智慧、區塊鏈 Blockchain、量子計算

    • 海外代購書籍(需單獨結帳)

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相關主題

商品描述

Your company has committed to AI. Congratulations, now what? This practical book offers a holistic plan for implementing AI from the perspective of IT and IT operations in the enterprise. You will learn about AI's capabilities, potential, limitations, and challenges. This book teaches you about the role of AI in the context of well-established areas, such as design thinking and DevOps, governance and change management, blockchain, and quantum computing, and discusses the convergence of AI in these key areas of the enterprise.
Deploying AI in the Enterprise provides guidance and methods to effectively deploy and operationalize sustainable AI solutions. You will learn about deployment challenges, such as AI operationalization issues and roadblocks when it comes to turning insight into actionable predictions. You also will learn how to recognize the key components of AI information architecture, and its role in enabling successful and sustainable AI deployments. And you will come away with an understanding of how to effectively leverage AI to augment usage of core information in Master Data Management (MDM) solutions.

What You Will Learn

  • Understand the most important AI concepts, including machine learning and deep learning
  • Follow best practices and methods to successfully deploy and operationalize AI solutions
  • Identify critical components of AI information architecture and the importance of having a plan
  • Integrate AI into existing initiatives within an organization
  • Recognize current limitations of AI, and how this could impact your business
  • Build awareness about important and timely AI research
  • Adjust your mindset to consider AI from a holistic standpoint
  • Get acquainted with AI opportunities that exist in various industries

Who This Book Is For
IT pros, data scientists, and architects who need to address deployment and operational challenges related to AI and need a comprehensive overview on how AI impacts other business critical areas. It is not an introduction, but is for the reader who is looking for examples on how to leverage data to derive actionable insight and predictions, and needs to understand and factor in the current risks and limitations of AI and what it means in an industry-relevant context.

商品描述(中文翻譯)

您的公司已經承諾投入人工智慧（AI）。恭喜您，接下來該怎麼做？這本實用的書籍提供了一個全面的計劃，從企業的IT和IT運營的角度來實施AI。您將了解AI的能力、潛力、限制和挑戰。本書教您在設計思維、DevOps、治理和變更管理、區塊鏈以及量子計算等成熟領域中，AI的角色，並討論AI在這些企業關鍵領域的融合。

《在企業中部署AI》提供了有效部署和運營可持續AI解決方案的指導和方法。您將學習到部署挑戰，例如AI運營化問題以及將洞察轉化為可行預測時的障礙。您還將學會如何識別AI信息架構的關鍵組成部分，以及它在促進成功和可持續AI部署中的作用。您將了解如何有效利用AI來增強主數據管理（MDM）解決方案中核心信息的使用。

您將學到什麼


  • 理解最重要的AI概念，包括機器學習和深度學習

  • 遵循最佳實踐和方法，成功部署和運營AI解決方案

  • 識別AI信息架構的關鍵組成部分及擁有計劃的重要性

  • 將AI整合到組織內的現有計劃中

  • 認識AI的當前限制，以及這可能對您的業務造成的影響

  • 提高對重要且及時的AI研究的認識

  • 調整您的思維方式，從整體的角度考慮AI

  • 熟悉各行各業中存在的AI機會

本書適合誰閱讀

IT專業人士、數據科學家和架構師，他們需要解決與AI相關的部署和運營挑戰，並需要全面了解AI如何影響其他業務關鍵領域。這不是一本入門書，而是針對那些尋求如何利用數據來獲取可行洞察和預測的讀者，並需要理解和考慮AI的當前風險和限制，以及這在行業相關背景下的意義。

作者簡介

Eberhard Hechler is an Executive Architect at the IBM Germany R&D Lab. He is a member of the DB2 Analytics Accelerator development group and addresses the broader data and AI on IBM Z scope, including machine learning for z/OS. After two-and-a-half years at the IBM Kingston Lab in New York, he worked in software development, performance optimization, IT/solution architecture and design, open source (Hadoop and Spark) integration, and master data management. He is a member of the IBM Academy of Technology Leadership team, and co-authored the following books: Enterprise MDM, The Art of Enterprise Information Architecture, and Beyond Big Data.

 

Martin Oberhofer is an IBM Distinguished Engineer and Executive Architect. He is a technologist and engineering leader with deep expertise in master data management, data governance, data integration, metadata and reference data management, artificial intelligence, and machine learning. He is accomplished at translating customer needs into software solutions, and works collaboratively with globally distributed development, design, and management teams. He guides development teams using Agile and DevOps software development methods. He is an elected member of the IBM Academy of Technology and the TEC CR. He is a certified IBM Master Inventor with over 100 granted patents and numerous publications, including four books.

 

Thomas Schaeck is an IBM Distinguished Engineer at IBM Data and AI, leading Watson Studio on IBM Cloud (Cloud Pak for Data) Desktop and integration with other IBM offerings. Previously, he led architecture and technical strategy for IBM Connections, WebSphere Portal, and IBM OpenPages. He also led architecture and technical direction for WebSphere Portal Platform and development of the WebSphere Portal Foundation, initiated and led the portal standards Java Portlet API and OASIS WSRP and Apache open source reference implementations, and initiated and led the Web 2.0 initiative for WebSphere Portal.

 

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作者簡介(中文翻譯)

Eberhard Hechler 是 IBM 德國研發實驗室的執行架構師。他是 DB2 Analytics Accelerator 開發小組的成員，負責 IBM Z 的更廣泛數據和人工智慧範疇，包括 z/OS 的機器學習。在 IBM 纽约的 Kingston Lab 工作了兩年半後，他從事軟體開發、性能優化、IT/解決方案架構與設計、開源（Hadoop 和 Spark）整合以及主數據管理。他是 IBM 技術學院領導團隊的成員，並共同撰寫了以下書籍：Enterprise MDM、The Art of Enterprise Information Architecture 和 Beyond Big Data。

Martin Oberhofer 是 IBM 傑出工程師和執行架構師。他是一位技術專家和工程領導者，在主數據管理、數據治理、數據整合、元數據和參考數據管理、人工智慧和機器學習方面擁有深厚的專業知識。他擅長將客戶需求轉化為軟體解決方案，並與全球分布的開發、設計和管理團隊協作。他指導開發團隊使用 Agile 和 DevOps 軟體開發方法。他是 IBM 技術學院的當選成員和 TEC CR 的成員。他是一位認證的 IBM Master Inventor，擁有超過 100 項授權專利和多篇出版物，包括四本書籍。

Thomas Schaeck 是 IBM 數據與人工智慧部門的傑出工程師，負責 IBM Cloud（Cloud Pak for Data）桌面上的 Watson Studio 及與其他 IBM 產品的整合。之前，他負責 IBM Connections、WebSphere Portal 和 IBM OpenPages 的架構和技術策略。他還負責 WebSphere Portal 平台的架構和技術方向，以及 WebSphere Portal 基礎的開發，啟動並領導了門戶標準 Java Portlet API 和 OASIS WSRP 及 Apache 開源參考實作，並啟動並領導了 WebSphere Portal 的 Web 2.0 計畫。

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