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

Approaching cognitive automation testing

As we continue through the automation life cycle once we have developed our automation, we can continue to test what was developed. While adding a cognitive component (Document Understanding, machine learning (ML) skills, and so on) brings additional complexities to automation from traditional rules-based robotic process automation (RPA), testing cognitive automation isn't too different from the traditional approach to testing automation.

In Chapter 6, Understanding Your Tools, we saw that when developing cognitive automation, we were essentially developing two distinct components: RPA and cognitive automation—we would build the ML skill first, then build RPA to leverage that ML skill. With this mindset, we can think of RPA and cognitive automation as modular components of the broader automation project, mimicking the design principle called separation of concerns (SoC).

SoC

This is a software design pattern where we separate...