Democratizing Artificial Intelligence with UiPath: Expand automation in your organization to achieve operational efficiency and high performance (Paperback)

Build an end-to-end business solution in the cognitive automation lifecycle and explore UiPath Document Understanding, UiPath AI Center, and Druid

Key Features

- Explore out-of-the-box (OOTB) AI Models in UiPath
- Learn how to deploy, manage, and continuously improve machine learning models using UiPath AI Center
- Deploy UiPath-integrated chatbots and master UiPath Document Understanding

Book Description

Artificial intelligence (AI) enables enterprises to optimize business processes that are probabilistic, highly variable, and require cognitive abilities with unstructured data. Many believe there is a steep learning curve with AI, however, the goal of our book is to lower the barrier to using AI. This practical guide to AI with UiPath will help RPA developers and tech-savvy business users learn how to incorporate cognitive abilities into business process optimization. With the hands-on approach of this book, you'll quickly be on your way to implementing cognitive automation to solve everyday business problems.

Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will help you understand the power of AI and give you an overview of the relevant out-of-the-box models. You'll learn about cognitive AI in the context of RPA, the basics of machine learning, and how to apply cognitive automation within the development lifecycle. You'll then put your skills to test by building three use cases with UiPath Document Understanding, UiPath AI Center, and Druid.

By the end of this AI book, you'll be able to build UiPath automations with the cognitive capabilities of intelligent document processing, machine learning, and chatbots, while understanding the development lifecycle.

What you will learn

- Discover how to bridge the gap between RPA and cognitive automation
- Understand how to configure, deploy, and maintain ML models in UiPath
- Explore OOTB models to manage documents, chats, emails, and more
- Prepare test data and test cases for user acceptance testing (UAT)
- Build a UiPath automation to act upon Druid responses
- Find out how to connect custom models to RPA

Who this book is for

AI Engineers and RPA developers who want to upskill and deploy out-of-the-box models using UiPath's AI capabilities will find this guide useful. A basic understanding of robotic process automation and machine learning will be beneficial but not mandatory to get started with this UiPath book.

在認知自動化生命周期中建立一個端到端的商業解決方案，並探索UiPath文件理解、UiPath AI中心和Druid。

主要特點：
- 探索UiPath中的即插即用AI模型
- 學習如何使用UiPath AI中心部署、管理和持續改進機器學習模型
- 部署與UiPath集成的聊天機器人，並掌握UiPath文件理解

書籍描述：
人工智能（AI）使企業能夠優化概率性高、變動性大且需要具有非結構化數據的認知能力的業務流程。許多人認為AI有一個陡峭的學習曲線，然而，我們的書的目標是降低使用AI的門檻。這本與UiPath一起實踐AI的實用指南將幫助RPA開發人員和技術熟練的業務用戶學習如何將認知能力融入業務流程優化中。通過這本書的實踐方法，您將迅速開始實施認知自動化，解決日常業務問題。

本書通過逐步解釋基本概念、實際示例和自我評估問題，幫助您了解AI的威力，並概述相關的即插即用模型。您將了解RPA背景下的認知AI、機器學習的基礎以及如何在開發生命周期中應用認知自動化。然後，您將通過使用UiPath文件理解、UiPath AI中心和Druid來構建三個用例來測試您的技能。

通過閱讀本書，您將能夠使用智能文件處理、機器學習和聊天機器人的認知能力來構建UiPath自動化，同時了解開發生命周期。

您將學到什麼：
- 發現如何彌合RPA和認知自動化之間的差距
- 瞭解如何在UiPath中配置、部署和維護機器學習模型
- 探索即插即用模型以管理文件、聊天、郵件等
- 為用戶驗收測試（UAT）準備測試數據和測試用例
- 構建一個UiPath自動化以對Druid的響應進行操作
- 瞭解如何將自定義模型連接到RPA

本書適合對使用UiPath的AI能力進行技能提升和部署即插即用模型的AI工程師和RPA開發人員。對機器人流程自動化和機器學習有基本的理解將有助於入門。

Fanny Ip is a thought leader in Automation, Business Transformation, and Innovation. Currently, Fanny is the VP of the Automation Consulting Innovation Office at UiPath and leads a team of experienced management consultants to develop field-tested solutions for customers - from start-up, expand, to scale.

Before joining UiPath, Fanny co-led McKinsey's Automation practice in North America and developed automation strategies for CxOs to deliver top-line revenue growth and achieve bottom-line impact. Before automation, Fanny has also led many business transformation projects at PwC Advisory and Deloitte Consulting. Fanny earned a BA in Economics from the University of Chicago and an MBA from the Anderson School of Management at UCLA. Originally from Hong Kong, Fanny resides in Los Angeles with her family.

Jeremiah is a multi-disciplined developer with a wide technical and operational lens. His initial passion for software development and implementation led him to automation and process efficiency. He enjoys working on and solving problems irrespective of size and technology. Jeremiah received his BA in Computer Science from New York University. His work history includes the likes of EY and McKinsey & Company, where he played roles within their Automation and Service Operations practices. He is currently a Director Manager within UiPath’s Automation Consulting Innovation Office, assisting organizations drive adoption and scale of automation by managing digital transformation initiatives and developing hyper-automation frameworks and methodologies.

Fanny Ip 是自動化、業務轉型和創新領域的思想領袖。目前，Fanny 是 UiPath 自動化諮詢創新辦公室的副總裁，帶領一支經驗豐富的管理顧問團隊為客戶開發經過實地測試的解決方案 - 從初創、擴展到規模化。

在加入 UiPath 之前，Fanny 曾共同領導 McKinsey 在北美的自動化實踐，為高層管理人員制定自動化策略，實現營收增長和底線影響。在自動化之前，Fanny 也在普華永道諮詢和德勤諮詢擔任過許多業務轉型項目的領導角色。Fanny 在芝加哥大學獲得了經濟學學士學位，並在加州大學洛杉磯分校安德森管理學院獲得了工商管理碩士學位。Fanny 來自香港，與家人居住在洛杉磯。

Jeremiah 是一位多才多藝的開發人員，具有廣泛的技術和運營視野。他對軟件開發和實施的熱情使他轉向自動化和流程效率。他喜歡解決各種大小和技術的問題。Jeremiah 在紐約大學獲得了計算機科學學士學位。他的工作經歷包括 EY 和 McKinsey & Company，在其中擔任自動化和服務運營實踐的角色。他目前是 UiPath 自動化諮詢創新辦公室的總監經理，協助組織推動自動化的采用和規模化，管理數字化轉型項目，並開發超自動化框架和方法論。

1. Understanding Essential Artificial Intelligence Basics for RPA Developers
2. Bridging the Gap between RPA and Cognitive Automation
3. Understanding the UiPath Platform in the Cognitive Automation Life Cycle
4. Identifying Cognitive Opportunities
5. Designing Automation with End User Considerations
6. Understanding Your Tools
7. Testing and Refining Development Efforts
8. Use Case 2 - Email Classification with AI Center
9. Use Case 3 - Chatbots with Druid
10. AI Center Advanced Topics

1. 瞭解RPA開發人員的基本人工智慧概念
2. 橋接RPA和認知自動化之間的差距
3. 瞭解UiPath平台在認知自動化生命週期中的角色
4. 辨識認知機會
5. 考慮最終使用者的設計自動化
6. 瞭解您的工具
7. 測試和改進開發工作
8. 使用案例2 - 使用AI中心進行電子郵件分類
9. 使用案例3 - 使用Druid進行聊天機器人
10. AI中心進階主題