This invite-only workshop for ICStar Hackathon 2021 participants will help you understand all about UiPath AI Center, Document Understanding, and Action Center. 

📌Prerequisite

• Software required: UiPath Studio Pro installed

• UiPath Cloud Account created with College ID.
• Cloud Account: cloud.uipath.com
• Gmail.com will not work
• Enable enterprise trial edition. Reference: https://youtu.be/Unl2Z_o0jCk
• Enable UiPath AI Center: https://youtu.be/Unl2Z_o0jCk
• Enable Document Understanding Key.
• Navigate to Cloud.uipath.com -> Admin -> Licenses -> Robots & Services -> Document Und

📍Agenda

Machine Learning Introduction (10 Min)

• Basics of Machine learning
• Introduction to prebuilt ML concept
• How AI and RPA work together.

Introduction to AI Center (10 Min)

• What is AI Center?
• How to enable AI Center
• How to create a new project
• How to select ML Model

Out of the BOX ML models (10 Min)

• Introduction to each OOB model
• The difference in retrainable and non-retrainable model
• How to deploy a non-retrainable model

Build a QNA bot (45 Min)

• How to Create a Project
• Deploy QNA ML model
• Connect with Orchestrator
• Build a bot in UiPath studio
• Execute the bot and show the ML predictions

Build restaurant feedback model (45 Min)

• How to deploy the retrainable model
• How to train the dataset
• Build a BOT in UiPath studio
• Connect the BOT with UiPath App
• Run the app and make a prediction

Introduction to Document Understanding (15 Min)

• What is document understanding
• What is OCR
• How the data is extracted using OCR

Document Understanding Basic (15 Min)

• Introduction to the framework
• Build a framework in UiPath Studio
• Extract data from PDF

Handwritten Data extraction (45 Mins)

• Introduction to extracting handwritten data
• Introduction to intelligent form extractor
• Introduction to OCR comparison

ML Extractor – Document Understanding (45 Mins)

• What are available in house DU Models
• How to deploy the ML DU model
• How to use Invoices ML Model
• How to use ML Extractor
• How to predict data in invoices