Intelligent Automation with Blue Prism Design Intelligent Automation Solutions Using Best Practices with RPA and Machine Learning
Become an expert at developing, designing, and managing intelligent automation solutions in Blue Prism Key Features Learn how to develop and design complex IPA solutions in Blue Prism Leverage machine learning to accelerate productions running at high scale and volume Discover how development in IA...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing Ltd
2024.
Birmingham, England : [2024] |
| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009827938406719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright
- Contributors
- Table of Contents
- Preface
- Part 1: Connecting Blue Prism to ML Models
- Chapter 1: Machine Learning as a Service: the Digital Exchange and Web APIs
- Technical requirements
- Using the DX
- Accessing the DX
- Machine learning web API fundamentals
- An overview of MLaaS on the DX
- Vendor selection
- Examples
- Example 1 - AWS Comprehend for text entity extraction, key phrase extraction, and sentiment analysis
- Example 2 - Azure Form Recognizer for invoice extraction
- Example 3 - GCP Cloud Vision batch OCR processing
- Summary
- Chapter 2: Predicting from Command Prompt and PowerShell
- Technical requirements
- Command-line basics
- Output streams
- Output redirection
- Blocking versus non-blocking execution
- Predicting from the command line using Utility - Environment
- Example 1 - Running a program using the Start Process
- Example 2 - Running a program using Run Process Until Ended
- Getting prediction results back into BP
- Example 3 - Saving STDOUT and STDERR as Data Items
- Example 4 - Redirecting an internal command (dir) to files
- Timing out long-running predictions
- Example 5 - PS script timeout
- DX VBOs - Utility - PowerShell and Script Execution VBO
- Utility - PowerShell
- Script Execution VBO
- Example 7 - Calling a Python program
- Summary
- Chapter 3: Code Stages
- Technical requirements
- Setting up ML.NET in BP
- Adding references and namespaces to BP
- Example 1 - preparation work before BP
- Porting ML.NET C# into a Code Stage
- Global Code
- Example 2 - porting the source code into BP
- Improving BP integration
- Example 3 - refactoring
- Summary
- Part 2: Designing IA Solutions
- Chapter 4: Reviewing Predictions and Human in the Loop
- Technical requirements
- Why should we review predictions?.
- Reduce business risk
- Stay ahead of regulatory concerns
- What does HITL mean in the context of IA?
- What criteria can be used to trigger human intervention?
- Random sampling
- Thresholding
- How can we share prediction data between prediction reviewers and BP?
- Reviewing predictions through shared folders
- Summary
- Chapter 5: IA Process and Work Queue Designs for HITL
- Technical requirements
- Single-Process, single-Work Queue designs
- Asynchronous (non-blocking) reviews
- Synchronous (blocking) reviews
- Multiple-Process, single-Work Queue designs
- Independent manual review logic
- Multiple-process, multiple-work queue designs
- Fully independent manual reviews
- Separating ML predictions and manual reviews into their own Processes and Work Queues
- Design comparison
- Design 1 - asynchronous reviews (one Process, one Work Queue)
- Design 2 - synchronous (polling) reviews (one Process, one Work Queue)
- Design 3 - independent HITL review logic (two Processes, one Work Queue)
- Design 4 - fully independent HITL reviews (two Processes, two Work Queues)
- Design 5 - full separation (three Processes, three Work Queues)
- Summary
- Chapter 6: Reusable IA Components
- Technical requirements
- IA session control
- Forcing HITL review
- Disabling HITL review
- Forcing review data recreation
- Example 1 - three IA Session Variables
- ML prediction kill switch
- Example 2 - kill switch
- ML model versioning
- Two different ways of calling web APIs
- Calling a web API using an Object when a new endpoint is provided
- Calling a web API using an Object when the vendor reuses an existing endpoint
- Example 3 - versioning ML endpoints manually
- Calling Web API Services
- New ML model evaluation
- Example 4 - new ML model evaluation Process template
- Reusable IA components review
- Summary.
- Chapter 7: IA Templates and Utility - IA Object
- Technical requirements
- Object - Utility - IA
- Random Integer in Range
- Random Decimal in Range
- Run Process Read Stdout Stderr with Timeout
- File to Base64
- Threshold Excel to Collection
- Get Threshold by Label
- Object Overview
- Process templates
- Single-Process, single-Work Queue, synchronous review Process template
- Single-Process, single-Work Queue, asynchronous review Process template
- Three-Process, three-Work Queue, asynchronous review Process template
- Summary
- Part 3: Control Room and Management
- Chapter 8: The LAM, User Roles, and MTE
- Technical requirements
- IA User Roles and Permissions
- ML Auditor
- ML Deployer
- ML Reviewer
- A User Role comparison
- MTEs
- MTE for the ML Auditor and ML Reviewer User Roles
- MTE limitations
- An updated LAM template
- Summary
- Chapter 9: ML Deployments and Database Operations
- ML deployments and rollbacks
- Web API deployment strategies
- Script deployment strategies
- Code Stage deployment strategies
- Database operations
- Table growth maintenance
- Extracting ML prediction data from the database
- Exporting reviewed prediction data from the database
- Summary
- Chapter 10: IA's Impact on the Robotic Operating Model
- Strategy
- Future of Work Vision
- Business case and value
- Governance, Risk, and Controls
- Workforce
- Building your organizational model
- Adopting new ways of thinking and working
- Roles and career paths
- Design
- Assessment and Prioritization
- Requirements Design
- Development
- Methodology and Teamwork
- Delivery Controls
- Testing and Quality Assurance
- Operations
- Deploy and Release
- Support model
- Summary
- Part 4: Real-Life Scenarios and Other Blue Prism Products
- Chapter 11: Processing Refunds
- Technical requirements.
- ML model background information
- EC model
- Entity recognition model
- Generative AI model
- ML model summary
- Solution design
- Email classification model
- Entity recognition model
- Generative AI model
- Solution design diagram
- Implementation
- Example 1 - Creating the solution structure from IA templates
- Example 2 - Implementing the IA details in Process 1
- Summary
- Chapter 12: Power Service Interruptions
- Technical requirements
- ML model background information
- Outage prediction model
- Customer complaints model
- ML model summary
- Solution design
- Handling model deployments
- Example 1 - Outage prediction model deployment
- Example 2 - Customer complaint model deployment
- Example 3 - Rollback customer complaint model deployment
- Exporting data for audit
- Example 4 - Exporting OP model data through SQL
- Example 5 - Exporting customer complaint model data through SQL
- Summary
- Chapter 13: Other Intelligent Blue Prism Products and Future IA Trends
- Decipher IDP
- How is Decipher related to IA?
- Using Decipher
- Next steps
- Document Automation
- How is Document Automation related to IA?
- Using Document Automation
- Next steps
- Decision
- How is Decision related to IA?
- Using Decision
- Next steps
- Interact
- How is Interact related to IA?
- Using Interact
- Next steps
- Future IA trends
- Improved AI product integration
- Democratized ML using LLMs
- AI ethics and safety
- Summary
- Appendix: IA Risk Management
- Socio-organizational IA risks
- Operational IA risks
- IA risk mitigation measures
- Index
- Other Books You May Enjoy.
