Asked to change instances of Generative AI / generative AI to just AI by stakeholders.

Orin-Thomas · Orin-Thomas · commit 43c0d6b068dc · 2025-06-06T14:36:00.000+10:00
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/1-implement-genaiops-processes.yml b/learn-pr/advocates/guide-ai-operations-center-excellence/1-implement-genaiops-processes.yml
@@ -3,7 +3,7 @@ uid: learn.workload-operations-generative-ai-center-excellence.implement-genaiop
 title: Implement GenAIOps processes
 metadata:
   title: Implement GenAIOps processes
-  description: Learn about GenAIOps processes and how they can help organizations effectively deploy, manage, and maintain generative AI workloads.
+  description: Learn about GenAIOps processes and how they can help organizations effectively deploy, manage, and maintain AI workloads.
   ms.date: 06/04/2025
   author: Orin-Thomas
   ms.author: orthomas
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/2-security-methods-practices.yml b/learn-pr/advocates/guide-ai-operations-center-excellence/2-security-methods-practices.yml
@@ -3,7 +3,7 @@ uid: learn.workload-operations-generative-ai-center-excellence.security-methods-
 title: Security methods and practices
 metadata:
   title: Security methods and practices
-  description: Learn about security methods and practices for generative AI workloads.
+  description: Learn about security methods and practices for AI workloads.
   ms.date: 06/04/2025
   author: Orin-Thomas
   ms.author: orthomas
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/3-compliance-governance-practices.yml b/learn-pr/advocates/guide-ai-operations-center-excellence/3-compliance-governance-practices.yml
@@ -3,7 +3,7 @@ uid: learn.workload-operations-generative-ai-center-excellence.compliance-govern
 title: Compliance governance practices
 metadata:
   title: Compliance governance practices
-  description: Discover how to implement compliance and governance practices for generative AI workloads.
+  description: Discover how to implement compliance and governance practices for AI workloads.
   ms.date: 06/04/2025
   author: Orin-Thomas
   ms.author: orthomas
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/4-infrastructure-management-processes.yml b/learn-pr/advocates/guide-ai-operations-center-excellence/4-infrastructure-management-processes.yml
@@ -3,7 +3,7 @@ uid: learn.workload-operations-generative-ai-center-excellence.infrastructure-ma
 title: Infrastructure management processes
 metadata:
   title: Infrastructure management processes
-  description: Learn how to manage infrastructure for generative AI workloads.
+  description: Learn how to manage infrastructure for AI workloads.
   ms.date: 06/04/2025
   author: Orin-Thomas
   ms.author: orthomas
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/5-cost-management-processes.yml b/learn-pr/advocates/guide-ai-operations-center-excellence/5-cost-management-processes.yml
@@ -3,7 +3,7 @@ uid: learn.workload-operations-generative-ai-center-excellence.cost-management-p
 title: Cost management processes
 metadata:
   title: Cost Management processes
-  description: Learn how to manage costs for generative AI workloads.
+  description: Learn how to manage costs for AI workloads.
   ms.date: 06/04/2025
   author: Orin-Thomas
   ms.author: orthomas
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/6-monitoring-and-optimization-practices.yml b/learn-pr/advocates/guide-ai-operations-center-excellence/6-monitoring-and-optimization-practices.yml
@@ -3,7 +3,7 @@ uid: learn.workload-operations-generative-ai-center-excellence.monitoring-and-op
 title: Monitoring and optimization practices
 metadata:
   title: Monitoring and optimization practices
-  description: Understand how to monitor and optimize generative AI workloads.
+  description: Understand how to monitor and optimize AI workloads.
   ms.date: 06/04/2025
   author: Orin-Thomas
   ms.author: orthomas
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/7-well-architected-generative-ai-workloads.yml b/learn-pr/advocates/guide-ai-operations-center-excellence/7-well-architected-generative-ai-workloads.yml
@@ -1,9 +1,9 @@
 ### YamlMime:ModuleUnit
 uid: learn.workload-operations-generative-ai-center-excellence.well-architected-generative-ai-workloads
-title: Well architected generative AI workloads
+title: Well architected AI workloads
 metadata:
-  title: Well architected generative AI workloads
-  description: This module covers the principles and practices for architecting generative AI workloads that are robust, scalable, and aligned with best practices.
+  title: Well architected AI workloads
+  description: This module covers the principles and practices for architecting AI workloads that are robust, scalable, and aligned with best practices.
   ms.date: 06/04/2025
   author: Orin-Thomas
   ms.author: orthomas
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/8-deploy-generative-ai-workloads.yml b/learn-pr/advocates/guide-ai-operations-center-excellence/8-deploy-generative-ai-workloads.yml
@@ -1,9 +1,9 @@
 ### YamlMime:ModuleUnit
 uid: learn.workload-operations-generative-ai-center-excellence.deploy-generative-ai-workloads
-title: Deploy generative AI workloads
+title: Deploy AI workloads
 metadata:
-  title: Deploy generative AI workloads
-  description: Learn how to deploy generative AI workloads effectively, ensuring they're secure, scalable, and aligned with organizational goals.
+  title: Deploy AI workloads
+  description: Learn how to deploy AI workloads effectively, ensuring they're secure, scalable, and aligned with organizational goals.
   ms.date: 06/04/2025
   author: Orin-Thomas
   ms.author: orthomas
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/9-knowledge-check.yml b/learn-pr/advocates/guide-ai-operations-center-excellence/9-knowledge-check.yml
@@ -12,7 +12,7 @@ durationInMinutes: 3
 content: Choose the best response for each question.
 quiz:
   questions:
-    - content: "What is the primary role of a generative AI Center of Excellence (CoE)?"
+    - content: "What is the primary role of an AI Center of Excellence (CoE)?"
       choices:
         - content: "To manage the financial aspects of AI projects."
           isCorrect: false
@@ -34,14 +34,14 @@ quiz:
         - content: "Ignoring monitoring and metrics."
           isCorrect: false
           explanation: "Monitoring and metrics are essential components of GenAIOps processes to evaluate workload performance and relevance."
-    - content: "What does the CoE promote to ensure financial efficiency in generative AI workloads?"
+    - content: "What does the CoE promote to ensure financial efficiency in AI workloads?"
       choices:
         - content: "Ignoring cost management processes."
           isCorrect: false
           explanation: "The CoE promotes cost management processes to ensure financial efficiency."
         - content: "Implementing FinOps principles."
           isCorrect: true
-          explanation: "The CoE promotes FinOps principles to monitor and optimize spending on generative AI workloads."
+          explanation: "The CoE promotes FinOps principles to monitor and optimize spending on AI workloads."
         - content: "Reducing the number of AI models used."
           isCorrect: false
           explanation: "The CoE doesn't focus on reducing the number of AI models used; it focuses on optimizing the financial aspects of AI workloads."
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/includes/1-implement-genaiops-processes.md b/learn-pr/advocates/guide-ai-operations-center-excellence/includes/1-implement-genaiops-processes.md
@@ -1,21 +1,21 @@
-A generative AI CoE typically focuses on setting direction, defining strategies, providing support, establishing metrics, and monitoring the impact of generative AI initiatives. A generative AI CoE provides an organization with a central location and set of people that have the knowledge, skills, and capabilities to effectively leverage AI. When properly implemented, a generative AI CoE holds the authority and influence within an organization needed to drive adoption of generative AI solutions.
+An AI CoE typically focuses on setting direction, defining strategies, providing support, establishing metrics, and monitoring the impact of AI initiatives. An AI CoE provides an organization with a central location and set of people that have the knowledge, skills, and capabilities to effectively leverage AI. When properly implemented, an AI CoE holds the authority and influence within an organization needed to drive adoption of AI solutions.
 
-GenAIOps is a set of methods and practices aligned with the deployment, management, and operation of generative AI workloads. To build effective GenAI solutions, developers need to select the right models, and also understand how these models fit into the broader operational framework to develop an application. The end-to-end application development of a GenAI solution is also referred to as Large Language Model Operations (LLMOps), or GenAI Operations (GenAIOps). GenAIOps involves tasks like model discovery and refining pretrained models with enriched data. The overall goal of GenAIOps is capability delivery with efficient operations throughout the development lifecycle. The expected outcomes are:
+GenAIOps is a set of methods and practices aligned with the deployment, management, and operation of AI workloads. To build effective GenAI solutions, developers need to select the right models, and also understand how these models fit into the broader operational framework to develop an application. The end-to-end application development of a GenAI solution is also referred to as Large Language Model Operations (LLMOps), or GenAI Operations (GenAIOps). GenAIOps involves tasks like model discovery and refining pretrained models with enriched data. The overall goal of GenAIOps is capability delivery with efficient operations throughout the development lifecycle. The expected outcomes are:
 
 - Repeatable processes with consistent results.
 - Sustained accuracy of the models over time.
 - Effective governance that minimizes risks.
 - Change management processes for adapting to model drift.
 
-An organization's generative AI CoE can help ensuring that appropriate GenAIOps processes are implemented throughout a generative AI workload's lifecycle. GenAIOps methods and practices include the following elements:
+An organization's AI CoE can help ensuring that appropriate GenAIOps processes are implemented throughout an AI workload's lifecycle. GenAIOps methods and practices include the following elements:
 
 - The workload integrates security and compliance technologies. Security and compliance are built into the workload rather than addressed as an ancillary concern.
 - Model choice and refinement. Evaluate existing models to identify one that's pretrained for the specific workload use case. Refine the model for domain-specific grounding.
-- Monitoring and Metrics. Ensure that generative AI workloads are continually  assessed to determine if they meet organizational needs, are cost effective, and meet Return on Investment (ROI) goals. Also determines whether the output of the AI workload is generating appropriate or anomalous results.
+- Monitoring and Metrics. Ensure that AI workloads are continually  assessed to determine if they meet organizational needs, are cost effective, and meet Return on Investment (ROI) goals. Also determines whether the output of the AI workload is generating appropriate or anomalous results.
 - Workload governance. Ensure that workloads include the appropriate mechanisms to allow them to configured meet relevant governance and compliance requirements, and that the workloads meet those requirements.
 - Ensures that workloads include versioning and rollback mechanisms. This allows the workload deployment to be automated, for updates to be rolled out quickly, and for rollback to occur should a new deployment lead to undesired outcomes.
 
-While an organization's generative AI CoE provides strategic guidance, frameworks, and recommended best practices for GenAIOps, the implementation and operational execution of these frameworks is usually managed by individual dedicated teams within the organization. 
+While an organization's AI CoE provides strategic guidance, frameworks, and recommended best practices for GenAIOps, the implementation and operational execution of these frameworks is usually managed by individual dedicated teams within the organization. 
 
 The CoE facilitates GenAIOps processes by providing training, templates, and governance policies. The CoE helps ensure that workloads are implemented with a consistent set of technologies, are scalable, and adhere to governance standards across the enterprise. 
 
@@ -26,10 +26,10 @@ The CoE should provide GenAIOps guidance on the following elements of workload o
 - Cost management processes
 - Monitoring and optimization processes
 
-![Diagram showing the relationships between DataOps, MLOps, GenAIOps, deployment and monitoring.](../media/genaiops-processes.png)
+![Diagram showing the relationships between DataOps, Maching Learning Ops, GenAIOps, deployment and monitoring.](../media/genaiops-processes.png)
 
-There should be bidirectional communication between the generative AI CoE and workload teams. 
+There should be bidirectional communication between the AI CoE and workload teams. 
 
 - A CoE disseminate recommended methods and practices to workload teams
 - Workload teams provide feedback to the CoE on the effectiveness of those methods and practices based on actual operational experience
-- The CoE can redistribute that feedback to other generative AI workload teams in the organization.
+- The CoE can redistribute that feedback to other AI workload teams in the organization.
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/includes/10-summary.md b/learn-pr/advocates/guide-ai-operations-center-excellence/includes/10-summary.md
@@ -1,4 +1,4 @@
-In this module, you learned about how a generative AI Center of Excellence (CoE) can serve as the central point of advice and procedure for teams who need to manage the deployment and operations of generative AI workloads. The AI CoE helps emphasizes the importance of implementing GenAIOps processes, ensuring that AI workloads are well-architected, and the workloads adhere to compliance and security requirements. The CoE can advise on best practice for the organization's infrastructure management to ensure that generative AI workloads meet scalability and reliability requirements.
+In this module, you learned about how an AI Center of Excellence (CoE) can serve as the central point of advice and procedure for teams who need to manage the deployment and operations of AI workloads. The AI CoE helps emphasizes the importance of implementing GenAIOps processes, ensuring that AI workloads are well-architected, and the workloads adhere to compliance and security requirements. The CoE can advise on best practice for the organization's infrastructure management to ensure that AI workloads meet scalability and reliability requirements.
 
 ## Learn more
 
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/includes/2-security-methods-practices.md b/learn-pr/advocates/guide-ai-operations-center-excellence/includes/2-security-methods-practices.md
@@ -1,12 +1,12 @@
-Beyond the traditional security challenges of running workloads in public clouds, as organizations integrate generative AI into their workflows, they must account for additional risks related to data protection, model security, compliance, adversarial attacks, and responsible AI governance. 
+Beyond the traditional security challenges of running workloads in public clouds, as organizations integrate AI into their workflows, they must account for additional risks related to data protection, model security, compliance, adversarial attacks, and responsible AI governance. 
 
-An organization's generative AI CoE plays a key role in setting guidelines, ensuring compliance, and supporting teams in secure AI implementation. The CoE works with existing security and workload teams to ensure that security considerations are embedded into all AI-related processes and aligned with organizational security policies.
+An organization's AI CoE plays a key role in setting guidelines, ensuring compliance, and supporting teams in secure AI implementation. The CoE works with existing security and workload teams to ensure that security considerations are embedded into all AI-related processes and aligned with organizational security policies.
 
-Ensuring the security of AI workloads requires continuous collaboration with security teams to integrate AI-specific safeguards into enterprise policies. A generative AI CoE can define which security frameworks are relevant for the organization, align those frameworks with compliance, and support teams in ensuring that AI workloads are secure and compliant.
+Ensuring the security of AI workloads requires continuous collaboration with security teams to integrate AI-specific safeguards into enterprise policies. An AI CoE can define which security frameworks are relevant for the organization, align those frameworks with compliance, and support teams in ensuring that AI workloads are secure and compliant.
 
 ## Security controls and guardrails
 
-The generative AI CoE should determine methods and practices for secure and responsible generative AI workload deployment and operation. These include but aren't limited to:
+The AI CoE should determine methods and practices for secure and responsible AI workload deployment and operation. These include but aren't limited to:
 
 - Implement guardrails to detect and mitigate risks such as prompt injection attacks and unsafe outputs.
 - Require adversarial testing and prompt attack simulations to detect and neutralize threats.
@@ -15,17 +15,17 @@ The generative AI CoE should determine methods and practices for secure and resp
 - Determine whether your use case is an ethical target for AI and maintaining ethical standards throughout the planning and implementation phases to ensure that you're building a responsible system.
 - Establish version control policies to prevent the unintended use of outdated or manipulated models.
 - Define security standards that integrate real-time monitoring for model drift, data exposure, and anomalous AI behavior.
-- Work with IT security teams to harden API security, ensuring generative AI model endpoints are protected from unauthorized queries and misuse.
+- Work with IT security teams to harden API security, ensuring AI model endpoints are protected from unauthorized queries and misuse.
 - Prevent unauthorized AI model export or transfer, ensuring proprietary AI systems remain protected.
 - Ensure role-based access control (RBAC) and identity management for AI model usage and modification.
 - Establish real-time security monitoring for AI anomalies, adversarial behaviors, and suspicious patterns.
 - Implement automated logging and forensic tools to investigate security events and potential breaches.
 - Track security incidents and unauthorized AI access within AI performance dashboards.
-- Ensure that hardening processes are applied to generative AI workload infrastructure.
+- Ensure that hardening processes are applied to AI workload infrastructure.
 
 ## Security of organizational data
 
-The generative AI CoE's role in the security of organizational data isn't to manage data holistically but to provide guidance, AI-specific standards, and frameworks that address the unique needs of generative AI systems. These responsibilities include: 
+The AI CoE's role in the security of organizational data isn't to manage data holistically but to provide guidance, AI-specific standards, and frameworks that address the unique needs of AI systems. These responsibilities include: 
 
 - Preparing datasets for AI training and inference
 - Generating and validating synthetic data
@@ -34,10 +34,10 @@ The generative AI CoE's role in the security of organizational data isn't to man
 
 Broader data management responsibilities, such as governance and architecture and pipeline management, are handled by specialized Data Platform teams or Data & Analytics teams. 
 
-The generative AI CoE should define the following methods and practices:
+The AI CoE should define the following methods and practices:
 
-- Security best practices for data ingestion, storage, and processing to ensure generative AI models are trained on validated, high-quality datasets.
-- Collaborate with data security teams to enforce encryption, anonymization, and synthetic data validation tailored for generative AI use cases.
+- Security best practices for data ingestion, storage, and processing to ensure AI models are trained on validated, high-quality datasets.
+- Collaborate with data security teams to enforce encryption, anonymization, and synthetic data validation tailored for AI use cases.
 - Define safeguards that prevent data poisoning attacks that could manipulate model training through adversarial or biased input data.
 - Develop security-aligned data curation guidelines to ensure compliance with Responsible AI principles, including bias mitigation and ethical dataset construction.
 - Ensure secure data practice including enforcing the encryption data in transit and at rest, the isolation of sensitive assets with fine-grained access controls, and the enforcement of data loss prevention policies.
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/includes/3-compliance-governance-practices.md b/learn-pr/advocates/guide-ai-operations-center-excellence/includes/3-compliance-governance-practices.md
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/includes/5-cost-management-processes.md b/learn-pr/advocates/guide-ai-operations-center-excellence/includes/5-cost-management-processes.md
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/includes/6-monitoring-and-optimization-practices.md b/learn-pr/advocates/guide-ai-operations-center-excellence/includes/6-monitoring-and-optimization-practices.md
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/includes/7-well-architected-generative-ai-workloads.md b/learn-pr/advocates/guide-ai-operations-center-excellence/includes/7-well-architected-generative-ai-workloads.md
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/includes/8-deploy-generative-ai-workloads.md b/learn-pr/advocates/guide-ai-operations-center-excellence/includes/8-deploy-generative-ai-workloads.md
diff --git a/learn-pr/advocates/guide-ai-operations-center-excellence/index.yml b/learn-pr/advocates/guide-ai-operations-center-excellence/index.yml
