Merge pull request #396 from sdgilley/sdg-replace-llmops

Replace LLMOps with GenAIOps

Author: Jill Grant

articles/machine-learning/concept-model-catalog.md

@@ -67,7 +67,7 @@ Other models | Available | Not available
 
 ## Managed compute
 
-The capability to deploy models with managed compute builds on platform capabilities of Azure Machine Learning to enable seamless integration, across the entire LLMOps lifecycle, of the wide collection of models in the model catalog. 
+The capability to deploy models with managed compute builds on platform capabilities of Azure Machine Learning to enable seamless integration, across the entire GenAIOps (sometimes called LLMOps) lifecycle, of the wide collection of models in the model catalog. 
 
 :::image type="content" source="media/concept-model-catalog/llm-ops-life-cycle.png" alt-text="A diagram showing the LLMops life cycle." lightbox="media/concept-model-catalog/llm-ops-life-cycle.png":::
 

articles/machine-learning/media/concept-llmops-maturity/llmopsml.png

@@ -43,7 +43,7 @@ To get started with the prompt flow VS Code extension, navigate to the extension
 
 After successful development and testing of your prompt flow within our community ecosystem, the subsequent step you're considering might involve transitioning to a production-grade LLM application. We recommend Azure Machine Learning for this phase to ensure security, efficiency, and scalability.
 
-You can seamlessly shift your local flow to your Azure resource to leverage large-scale execution and management in the cloud. To achieve this, see [Integration with LLMOps](how-to-integrate-with-llm-app-devops.md#go-back-to-studio-ui-for-continuous-development).
+You can seamlessly shift your local flow to your Azure resource to leverage large-scale execution and management in the cloud. To achieve this, see [Integration with GenAIOps](how-to-integrate-with-llm-app-devops.md#go-back-to-studio-ui-for-continuous-development).
 
 ## Community support
 

articles/machine-learning/prompt-flow/community-ecosystem.md

@@ -1,7 +1,7 @@
 ---
-title: Advance your maturity level for LLMOps
+title: Advance your maturity level for GenAIOps
 titleSuffix: Azure Machine Learning
-description: Learn about the different stages of Large Language Operations (LLMOps) and how to advance your organization's capabilities.
+description: Learn about the different stages of Generative Artificial Intelligence Operations (GenAIOps) and how to advance your organization's capabilities.
 services: machine-learning
 ms.service: azure-machine-learning
 ms.subservice: prompt-flow
@@ -12,25 +12,25 @@ ms.reviewer: sasahami
 ms.date: 03/28/2024
 ---
 
-# Advance your maturity level for Large Language Model Operations (LLMOps)
+# Advance your maturity level for Generative Artificial Intelligence Operations (GenAIOps)
 
-Large Language Model Operations, or **LLMOps**, describes the operational practices and strategies for managing large language models (LLMs) in production. This article  provides guidance on how to advance your capabilities in LLMOps, based on your organization's current maturity level.
+Generative Artificial Intelligence Operations, or **GenAIOps** (sometimes called LLMOps), describes the operational practices and strategies for managing large language models (LLMs) in production. This article  provides guidance on how to advance your capabilities in GenAIOps, based on your organization's current maturity level.
 
-:::image type="content" source="../media/concept-llmops-maturity/llmopsml.png" alt-text="Diagram shows maturity level of LLMOps." lightbox="../media/concept-llmops-maturity/llmopsml.png":::
+:::image type="content" source="../media/concept-llmops-maturity/llmopsml.png" alt-text="Diagram shows maturity level of GenAIOps." lightbox="../media/concept-llmops-maturity/llmopsml.png":::
 
-Use the descriptions below to find your *LLMOps Maturity Model* ranking level. These levels provide a general understanding and practical application level of your organization. The guidelines provide you with helpful links to expand your LLMOps knowledge base.  
+Use the descriptions below to find your *GenAIOps Maturity Model* ranking level. These levels provide a general understanding and practical application level of your organization. The guidelines provide you with helpful links to expand your GenAIOps knowledge base.  
 
 > [!TIP]
-> Use the [LLMOps Maturity Model Assessment](/assessments/e14e1e9f-d339-4d7e-b2bb-24f056cf08b6/) to determine your organization's current LLMOps maturity level. The questionnaire is designed to help you understand your organization's current capabilities and identify areas for improvement.
+> Use the [GenAIOps Maturity Model Assessment](/assessments/e14e1e9f-d339-4d7e-b2bb-24f056cf08b6/) to determine your organization's current GenAIOps maturity level. The questionnaire is designed to help you understand your organization's current capabilities and identify areas for improvement.
 >
-> Your results from the assessment corresponds to a *LLMOps Maturity Model* ranking level, providing a general understanding and practical application level of your organization. These guidelines provide you with helpful links to expand your LLMOps knowledge base. 
+> Your results from the assessment corresponds to a *GenAIOps Maturity Model* ranking level, providing a general understanding and practical application level of your organization. These guidelines provide you with helpful links to expand your GenAIOps knowledge base. 
 
 ## <a name="level1"></a>Level 1 - initial
 
 > [!TIP]
-> Score from [LLMOps Maturity Model Assessment](/assessments/e14e1e9f-d339-4d7e-b2bb-24f056cf08b6/): initial (0-9).
+> Score from [GenAIOps Maturity Model Assessment](/assessments/e14e1e9f-d339-4d7e-b2bb-24f056cf08b6/): initial (0-9).
 
-**Description:** Your organization is at the initial foundational stage of LLMOps maturity. You're exploring the capabilities of LLMs but haven't yet developed structured practices or systematic approaches.
+**Description:** Your organization is at the initial foundational stage of GenAIOps maturity. You're exploring the capabilities of LLMs but haven't yet developed structured practices or systematic approaches.
 
 Begin by familiarizing yourself with different LLM APIs and their capabilities. Next, start experimenting with structured prompt design and basic prompt engineering. Review ***Microsoft Learning*** articles as a starting point. Taking what you’ve learned, discover how to introduce basic metrics for LLM application performance evaluation.
 
@@ -45,26 +45,26 @@ Begin by familiarizing yourself with different LLM APIs and their capabilities.
 - [***Evaluate GenAI Applications with Azure AI Studio***](/azure/ai-studio/concepts/evaluation-approach-gen-ai)
 - [***GenAI Evaluation and Monitoring Metrics with Azure AI Studio***](/azure/ai-studio/concepts/evaluation-metrics-built-in)
 
-To better understand LLMOps, consider available MS Learning courses and workshops.
+To better understand GenAIOps, consider available MS Learning courses and workshops.
 - [***Microsoft Azure AI Fundaments: GenAI***](/training/paths/introduction-generative-ai/)
 - [***GenAI for Beginners Course***](https://techcommunity.microsoft.com/t5/educator-developer-blog/generative-ai-for-beginners-a-12-lesson-course/ba-p/3968583)
 
 ## <a name="level2"></a> Level 2 - defined
 
 > [!TIP]
-> Score from [LLMOps Maturity Model Assessment](/assessments/e14e1e9f-d339-4d7e-b2bb-24f056cf08b6/): maturing (10-14).
+> Score from [GenAIOps Maturity Model Assessment](/assessments/e14e1e9f-d339-4d7e-b2bb-24f056cf08b6/): maturing (10-14).
 
 **Description:** Your organization has started to systematize LLM operations, with a focus on structured development and experimentation. However, there's room for more sophisticated integration and optimization.
 
 To improve your capabilities and skills, learn how to develop more complex prompts and begin integrating them effectively into applications. During this journey, you’ll want to implement a systematic approach for LLM application deployment, possibly exploring CI/CD integration. Once you understand the core, you can begin employing more advanced evaluation metrics like groundedness, relevance, and similarity. Ultimately, you’ll want to focus on content safety and ethical considerations in LLM usage.
 
 ### ***Suggested references for level 2 advancement***
 
-- Take our [***step-by-step workshop to elevate your LLMOps practices***](https://github.com/microsoft/llmops-workshop?tab=readme-ov-file) 
+- Take our [***step-by-step workshop to elevate your GenAIOps practices***](https://github.com/microsoft/llmops-workshop?tab=readme-ov-file) 
 - [***Prompt Flow in Azure AI Studio***](/azure/ai-studio/how-to/prompt-flow)
 - [***How to Build with Prompt Flow***](/azure/ai-studio/how-to/flow-develop)
 - [***Deploy a Flow as a Managed Online endpoint for Real-Time Inference***](/azure/ai-studio/how-to/flow-deploy?tabs=azure-studio)
-- [***Integrate Prompt Flow with LLMOps***](/azure/machine-learning/prompt-flow/how-to-integrate-with-llm-app-devops?tabs=cli)
+- [***Integrate Prompt Flow with GenAIOps***](/azure/machine-learning/prompt-flow/how-to-integrate-with-llm-app-devops?tabs=cli)
 - [***GenAI Evaluation with Azure AI Studio***]( /azure/ai-studio/concepts/evaluation-approach-gen-ai)
 - [***GenAI Evaluation and Monitoring Metrics***](/azure/ai-studio/concepts/evaluation-metrics-built-in)
 - [***Azure Content Safety***](/azure/ai-services/content-safety/overview)
@@ -73,7 +73,7 @@ To improve your capabilities and skills, learn how to develop more complex promp
 ## <a name="level3"></a> Level 3 - managed
 
 > [!TIP]
-> Score from [LLMOps Maturity Model Assessment](/assessments/e14e1e9f-d339-4d7e-b2bb-24f056cf08b6/): maturing (15-19).
+> Score from [GenAIOps Maturity Model Assessment](/assessments/e14e1e9f-d339-4d7e-b2bb-24f056cf08b6/): maturing (15-19).
 
 **Description:** Your organization is managing advanced LLM workflows with proactive monitoring and structured deployment strategies. You're close to achieving operational excellence.
 
@@ -84,14 +84,14 @@ To expand your base knowledge, focus on continuous improvement and innovation in
 - [***Fine-tuning with Azure ML Learning***](/training/modules/finetune-foundation-model-with-azure-machine-learning/)
 - [***Model Customization with Fine-tuning***](/azure/ai-services/openai/how-to/fine-tuning?tabs=turbo%2Cpython&pivots=programming-language-studio)
 - [***GenAI Model Monitoring***](/azure/machine-learning/prompt-flow/how-to-monitor-generative-ai-applications)
-- [***Elevate LLM Apps to Production with LLMOps***](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/elevate-your-llm-applications-to-production-via-llmops/ba-p/3979114)
+- [***Elevate LLM Apps to Production with GenAIOps***](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/elevate-your-llm-applications-to-production-via-llmops/ba-p/3979114)
 
 ## <a name="level4"></a> Level 4 - optimized
 
 > [!TIP]
-> Score from [LLMOps Maturity Model Assessment](/assessments/e14e1e9f-d339-4d7e-b2bb-24f056cf08b6/): optimized (20-28).
+> Score from [GenAIOps Maturity Model Assessment](/assessments/e14e1e9f-d339-4d7e-b2bb-24f056cf08b6/): optimized (20-28).
 
-**Description:** Your organization demonstrates operational excellence in LLMOps. You have a sophisticated approach to LLM application development, deployment, and monitoring.
+**Description:** Your organization demonstrates operational excellence in GenAIOps. You have a sophisticated approach to LLM application development, deployment, and monitoring.
 
 As LLMs evolve, you’ll want to maintain your cutting-edge position by staying updated with the latest LLM advancements. Continuously evaluate the alignment of your LLM strategies with evolving business objectives. Ensure that you foster a culture of innovation and continuous learning within your team. Last, but not least, share your knowledge and best practices with the wider community to establish thought leadership in the field.