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

Testing to ensure stability and improve accuracy

With the initial development of the use case complete, we can venture into testing out how well the automation performs with the ML Classifier and ML Extractor. Testing any automated workflow before deployment is crucial in order to ensure that the automation works as expected. In this section, we will investigate enabling the Validation Station for the ML Classifier and ML Extractor, as well as starting testing with sample data.

Enabling the Validation Station

During the development of the use case earlier, we deployed both the DocumentUnderstanding classifier and the Receipts ML skill to act as our classifier and extractor respectively. One of the reasons why we deployed these skills to AI Center was the ability to retrain these skills using the Validation Station. This gives us the ability to manually validate automation performance and retrain ML models, something we call Closing the Feedback Loop (Figure 8.36):

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