Book Image

Democratizing Artificial Intelligence with UiPath

By : Fanny Ip, Jeremiah Crowley
Book Image

Democratizing Artificial Intelligence with UiPath

By: Fanny Ip, Jeremiah Crowley

Overview of this book

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.
Table of Contents (16 chapters)
1
Section 1: The Basics
5
Section 2: The Development Life Cycle with AI Center and Document Understanding
10
Section 3: Building with UiPath Document Understanding, AI Center, and Druid

QnA

  1. What are the characteristics of the Document Understanding framework?
    • Taxonomy, digitization, classification, extraction, and export.
  2. Can you digitize a document without an OCR engine?
    • Yes, you can; however, the digitization activity of the Document Understanding framework will require an OCR engine, even if OCR is unneeded.
  3. When should the Classification Station or Validation Station be used?
    • They should be used when automation confidence is not high and human validation is needed to augment automation. They should also be used when closing the feedback loop and retraining via human validation.
  4. Is AI Center limited to out-of-the-box ML packages?
    • No. AI Center can handle custom ML models made with Python in addition to the out-of-the-box packages it offers.
  5. When is CV best used?
    • CV is best for applications that contain unreliable selectors or VDI/VM environments where selectors cannot be captured.