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Course Outline

UiPath IPA Architecture & Environment Setup

  • RPA vs. IPA: When to use traditional automation vs. AI-driven workflows
  • UiPath ecosystem overview: Studio, Orchestrator, Robot, AI Center, Document Understanding
  • Installing UiPath Community Edition & validating runtime environment
  • Workflow foundations: Sequences, State Machines, Flowcharts, and .NET/C#/VB integration points
  • Lab 1: Environment provisioning, connecting Studio to Orchestrator, and deploying a baseline automation

Computer Vision for Robust UI Automation

  • Limitations of traditional selectors & the role of Computer Vision
  • UiPath Computer Vision engine: image matching, fuzzy matching, and resolution-independent automation
  • Handling dynamic, virtualized, or API-restricted interfaces
  • Fallback strategies & error recovery for CV-based interactions
  • Lab 2: Build a CV-driven workflow to locate, verify, and interact with non-intrusive screen elements across multiple UI states

AI-Powered Text Processing & Sentiment Analysis

  • Integrating AI into RPA: Architecture & data flow considerations
  • UiPath’s built-in NLP & language pattern detection activities
  • Performing sentiment analysis on unstructured content (emails, reviews, chat logs, documents)
  • Connecting external AI services (Azure Cognitive Services, AWS Comprehend, or custom APIs)
  • Lab 3: Process a batch of unstructured text, extract key language patterns, and output sentiment scores into structured formats

Multi-Robot Orchestration & Enterprise Management

  • Orchestrator deep dive: Environments, Queues, Assets, Users, & Security Roles
  • Robot types: Attended, Unattended, & Hybrid configurations
  • Enabling robots to manage other robots: Queue-driven work distribution, dynamic scaling, & supervisor patterns
  • Monitoring, logging, exception handling, & audit trails at scale
  • Lab 4: Configure Orchestrator, deploy a multi-robot queue system, and validate cross-robot task distribution & error routing

End-to-End IPA Design & Production Readiness

  • Architecting an IPA solution: Data ingestion → AI processing → RPA execution → output routing
  • Optimizing performance: CV tuning, queue prioritization, retry logic, & memory management
  • Security & compliance: credential management, data masking, & enterprise governance
  • Transitioning from training to production: deployment pipelines, version control, & maintenance strategies
  • Capstone: Build & deploy a complete IPA workflow combining Computer Vision, AI/NLP, multi-robot orchestration, and scheduled execution

Q&A, Customization & Advanced Pathways

  • Review of key IPA patterns & troubleshooting common pitfalls
  • Advanced paths: Process Mining, Automation Hub, .NET library interop, & API-first automation
  • Enterprise licensing pathways vs. Community Edition limitations
  • Open Q&A and personalized implementation guidance
  • Customization Options: Available for domain-specific datasets, proprietary UI automation, or hybrid cloud/on-prem deployment
     

To request a customized training schedule or enterprise-grade delivery, please contact our solutions team.

Requirements

  • Basic programming experience (.NET, C#, VB, or similar OOP/scripting language)
  • Familiarity with basic automation concepts is helpful but not required

Audience

  • Software Developers & Automation Engineers
  • Business Intelligence Professionals with technical skills
  • Digital Marketing Professionals with technical skills
  • IT/Operations leads extending legacy RPA with AI capabilities
 14 Hours

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