Intelligent Automation with Blue Prism: Combine Machine Learning with RPA and become an IPA pioneer in your organization

Intelligent Automation with Blue Prism
Contributors
About the author
About the reviewer
Preface
   Who this book is for
   What this book covers
   To get the most out of this book
   Download the example code files
   Conventions used
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Part 1:Connecting Blue Prism to ML Models
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
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
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
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
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
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
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
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
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
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
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
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
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
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