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Democratizing Artificial Intelligence with UiPath

You're reading from   Democratizing Artificial Intelligence with UiPath Expand automation in your organization to achieve operational efficiency and high performance

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801817653
Length 376 pages
Edition 1st Edition
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Authors (2):
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Fanny Ip Fanny Ip
Author Profile Icon Fanny Ip
Fanny Ip
Jeremiah Crowley Jeremiah Crowley
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Jeremiah Crowley
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Table of Contents (16) Chapters Close

Preface 1. Section 1: The Basics
2. Chapter 1: Understanding Essential Artificial Intelligence Basics for RPA Developers FREE CHAPTER 3. Chapter 2: Bridging the Gap between RPA and Cognitive Automation 4. Chapter 3: Understanding the UiPath Platform in the Cognitive Automation Life Cycle 5. Section 2: The Development Life Cycle with AI Center and Document Understanding
6. Chapter 4: Identifying Cognitive Opportunities 7. Chapter 5: Designing Automation with End User Considerations 8. Chapter 6: Understanding Your Tools 9. Chapter 7: Testing and Refining Development Efforts 10. Section 3: Building with UiPath Document Understanding, AI Center, and Druid
11. Chapter 8: Use Case 1 – Receipt Processing with Document Understanding 12. Chapter 9: Use Case 2 – Email Classification with AI Center 13. Chapter 10: Use Case 3 – Chatbots with Druid 14. Chapter 11: AI Center Advanced Topics 15. Other Books You May Enjoy

Getting started with UiPath AI Center

UiPath AI Center unleashes the capability to leverage ML models with RPA. With AI Center, we're able to deploy, manage, and retrain out-of-the-box or custom ML models. In this section, we'll navigate around the core concepts of AI Center and dive into the activities of the AI Center activity package.

Using AI Center

In this section, we will navigate around the key concepts of AI Center. By getting familiar with these concepts, we will build experience with leveraging AI Center, key for building automation with ML capabilities.

When creating an automation project, we generally want to follow two paths into production:

  • Use pretrained models: The developer should follow the following flow:
    1. Creating a project
    2. Leveraging an ML package
    3. Deploying it as an ML skill
    4. Dragging and dropping the ML model into RPA
  • Use retrainable models: The developer should follow the following flow:
    1. Creating a project
    2. Uploading a dataset
    3. Leveraging...
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