INTELLIGENT AUTOMATION WITH BLUE PRISM design intelligent automation solutions using best practices with RPA and machine learning
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Man, J. (2024). INTELLIGENT AUTOMATION WITH BLUE PRISM: design intelligent automation solutions using best practices with RPA and machine learning (1st edition.). Packt Publishing Ltd..
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Man, James. 2024. INTELLIGENT AUTOMATION WITH BLUE PRISM: Design Intelligent Automation Solutions Using Best Practices With RPA and Machine Learning. Birmingham, UK: Packt Publishing Ltd.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Man, James. INTELLIGENT AUTOMATION WITH BLUE PRISM: Design Intelligent Automation Solutions Using Best Practices With RPA and Machine Learning Birmingham, UK: Packt Publishing Ltd, 2024.
Harvard Citation (style guide)Man, J. (2024). INTELLIGENT AUTOMATION WITH BLUE PRISM: design intelligent automation solutions using best practices with RPA and machine learning. 1st edn. Birmingham, UK: Packt Publishing Ltd.
MLA Citation, 9th Edition (style guide)Man, James. INTELLIGENT AUTOMATION WITH BLUE PRISM: Design Intelligent Automation Solutions Using Best Practices With RPA and Machine Learning 1st edition., Packt Publishing Ltd., 2024.
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Grouped Work ID | 6bd30f57-09b0-766a-c38e-30ae8ab6bce8-eng |
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Full title | intelligent automation with blue prism design intelligent automation solutions using best practices with rpa and machine learning |
Author | man james |
Grouping Category | book |
Last Update | 2025-01-24 12:33:29PM |
Last Indexed | 2025-01-30 03:15:11AM |
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First Loaded | Dec 22, 2024 |
Last Used | Feb 4, 2025 |
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300 | |a 1 online resource | ||
505 | 0 | |a 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 | |
505 | 8 | |a 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 | |
505 | 8 | |a 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 | |
505 | 8 | |a 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) | |
505 | 8 | |a Design 4 -- fully independent HITL reviews (two Processes, two Work Queues) | |
520 | |a 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 differs from RPA while working on real-world IA use cases Purchase of the print or Kindle book includes a free PDF eBook Book Description Intelligent Automation (IA) stands out as an impactful enterprise technology, shaping the future of work. As the world grapples with challenges like labor shortages and an aging workforce, the spotlight is on IA as a transformative solution. This book is your hands-on guide to integrating machine learning with Blue Prism (BP). You'll learn how to design IA solutions using Work Queues, Session Variables, and more, and understand the criteria for evaluating ML for automation to create proper solution design. Once you've learned how to create reusable IA templates from best practices, you'll see how they reduce the time needed to bring a solution into production. The book then takes you through the BP Control Room and management aspects of an IA solution, introducing you to the unique management concerns IA presents compared to RPA, due to the uncertainty by model predictions and an evolving regulatory environment that restricts IA use. The book highlights IA's impact on the wider automation context through user permissions, security, deployments, and BP's Robotic Operating Model, and concludes by recreating a real-world intelligent automation processes in BP. This book is not just practical; it's also enriched by real-life experience and the insights distilled from the authors' research at MIT, which examined over 70 IA use cases. What you will learn Harness the integration of Blue Prism with machine learning to make predictions Explore concepts that guide to the design of maintainable IA solutions Understand how IA impacts Control Room operations Implement best practices for managing the risks associated with IA Develop reusable templates to kickstart IA development Uncover the design principles behind real-life IA examples used in production Who this book is for This book is for RPA developers looking to get hands-on with integrating machine learning predictions into Blue Prism processes. No prior model building experience is required, as the focus is on using ML in an automated manner. Developers who want to understand how to design Blue Prism solutions for intelligent automation, as well as RPA process controllers and COE management will benefit from this book. Anyone involved in managing the uncertainty and risks introduced by machine learning in the automation program will find this guide insightful. | ||
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650 | 0 | |a Human-computer interaction.|9 63884 | |
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650 | 0 | |a Robotics.|9 53695 | |
650 | 0 | |a Autonomous robots.|9 72230 | |
650 | 0 | |a Robots, Industrial.|9 395595 | |
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