Enterprise Architecture (EA) - Revision history

BPeat: /* Conway's Law */

2024-06-07T15:24:36Z

‎Conway's Law

BPeat at 15:18, 7 June 2024

2024-06-07T15:18:46Z

BPeat at 20:40, 28 April 2024

2024-04-28T20:40:54Z

BPeat at 01:54, 27 April 2024

2024-04-27T01:54:25Z

BPeat at 19:57, 1 March 2024

2024-03-01T19:57:50Z

BPeat at 18:24, 18 September 2023

2023-09-18T18:24:08Z

BPeat at 11:43, 17 August 2023

2023-08-17T11:43:40Z

BPeat at 01:31, 10 July 2023

2023-07-10T01:31:26Z

BPeat at 16:05, 9 July 2023

2023-07-09T16:05:38Z

BPeat at 00:16, 6 July 2023

2023-07-06T00:16:32Z

@@ Line 318: / Line 318: @@
 = <span id="Conway's Law"></span>Conway's Law =
 <hr>
-<center><b><i>Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization's communication structure.</i> — Melvin E. Conway</b> [https://en.wikipedia.org/wiki/Conway%27s_law - Conway's Law]</center>
+<hr>
-<hr>
+In the context of AI development, Conway's Law suggests that the organization of teams working on AI projects will impact the design and structure of the AI systems they produce. If teams are organized around specific AI techniques or technologies (e.g., natural language processing, computer vision), the resulting AI systems may reflect this organizational structure, potentially leading to disjointed or fragmented solutions. Conway's Law serves as a reminder that the social and organizational aspects of AI development are as crucial as the technical aspects, and paying attention to how teams are organized and how they communicate can ultimately impact the quality and effectiveness of the AI systems they produce.
 = <span id="ITIL - IT Service Management"></span>ITIL - IT Service Management =

@@ Line 138: / Line 138: @@
 ** An example can be ‘Assign Sales person’ – essentially through a mechanism that is to be followed for assigning a particular sales person on availability to the customer – the service that helps to locate the next available sales person to be booked. Another example can be ‘update customer details’ as they apply for the offer through a system/application. Remember these services can be called upon by other processes also – ‘Assign sales person’ can be used to assign sales person for solving a customer complaint as well.
 = Enterprise Architecture Frameworks =
@@ Line 323: / Line 316: @@
 <img src="https://PRIMO.AI/BCM/BCM.InformationLevels.jpg" width="800">
 = <span id="ITIL - IT Service Management"></span>ITIL - IT Service Management =

@@ Line 13: / Line 13: @@
 * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
-* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
+* [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]]
 * [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
 * [[Defense]]: [[Joint Capabilities Integration and Development System (JCIDS)]]

@@ Line 11: / Line 11: @@
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
-* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
+* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
 * [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]

@@ Line 395: / Line 395: @@
 <youtube>J5uaKjNKbw8</youtube>
 <b>Transforming Organizational Capability and Performance Using CMMI V2.0 | Ronald Lear
-</b><br>Key Topics covered include: Operational Excellence   Business Transformation  Lean Transformation   Lean Manufacturing  Six Sigma  Continuous Improvement   Digital Transformation  Cultural Transformation   Agile & Agile Culture   Business Process Management / iBPM  Robotic Process Automation (RPA) Download the FREE 130 pages research report, the most comprehensive study of critical challenges and future trends within Operational Excellence.  https://bit.ly/BTOEreport
+</b><br>Key Topics covered include: Operational Excellence   Business Transformation  Lean Transformation   Lean Manufacturing  Six Sigma  Continuous Improvement   Digital Transformation  Cultural Transformation   Agile & Agile Culture   Business Process Management / iBPM  [[Robotic Process Automation (RPA)]] Download the FREE 130 pages research report, the most comprehensive study of critical challenges and future trends within Operational Excellence.  https://bit.ly/BTOEreport
 |}
 |<!-- M -->

@@ Line 8: / Line 8: @@
 [https://www.google.com/search?q=Enterprise+Architecture+artificial+intelligence+deep+learning+AI ...Google search]
-* [[Architectures]] for AI ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
+* [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]

@@ Line 45: / Line 45: @@
 *** [https://www.orbussoftware.com/ppc/archimate-30-starter-pack/ ArchiMate Orbus] …[https://publications.opengroup.org/guides/archimate  Guide]
-In order to bring measurable value to their firms, Enterprise Architects of Tomorrow must understand, utilize, and evangelize the latest technologies driving the industry. These current trends include predictive analytics, deep learning, prescriptive analytics, and machine learning. All of these trends use current data to make predictions about unknown future events. [https://blog.leanix.net/en/an-enterprise-architects-guide-to-machine-learning-series-part-1 Lesa Moné | LeanIX]
+In order to bring measurable value to their firms, Enterprise Architects of Tomorrow must understand, utilize, and evangelize the latest technologies driving the industry. These current trends include [[Predictive Analytics]], [[Deep Learning]], prescriptive analytics, and [[Machine Learning (ML)]]. All of these trends use current data to make predictions about unknown future events. [https://blog.leanix.net/en/an-enterprise-architects-guide-to-machine-learning-series-part-1 Lesa Moné | LeanIX]
 ==== Architecture Attributes for AI ====