Updated ms.collection attribute for AI content

Author: theresa-i

learn-pr/wwl-data-ai/evaluate-language-models-azure-databricks/index.yml

@@ -5,12 +5,13 @@ metadata:
   prefetch-feature-rollout: true
   title: Evaluate language models with Azure Databricks
   description: Evaluate language models with Azure Databricks
-  ms.date: 08/20/2024
+  ms.date: 03/20/2025
   author: wwlpublish
   ms.author: madiepev
   ms.topic: module
   ms.service: azure
   ai-usage: ai-assisted
+  ms.collection: wwl-ai-copilot
 title: Evaluate language models with Azure Databricks
 summary: Learn to compare Large Language Model (LLM) and traditional Machine Learning (ML) evaluations, understand their relationship with AI system evaluation, and explore various LLM evaluation metrics and specific task-related evaluations.
 abstract: |

learn-pr/wwl-data-ai/fine-tune-azure-databricks/index.yml

@@ -5,13 +5,14 @@ metadata:
   prefetch-feature-rollout: true
   title: Fine-tune language models with Azure Databricks
   description: Fine-tune language models with Azure Databricks
-  ms.date: 08/20/2024
+  ms.date: 03/20/2025
   author: wwlpublish
   ms.author: madiepev
   ms.topic: module
   ms.service: azure
   ai-usage: ai-assisted
   ms.custom: ai-learning-hub
+  ms.collection: wwl-ai-copilot
 title: Fine-tune language models with Azure Databricks
 summary: Fine-tuning uses Large Language Models' (LLMs) general knowledge to improve performance on specific tasks, allowing organizations to create specialized models that are more accurate and relevant while saving resources and time compared to training from scratch. 
 abstract: |

learn-pr/wwl-data-ai/implement-cicd-in-fabric/index.yml

@@ -3,11 +3,11 @@ uid: learn-wwl.implement-cicd-in-fabric
 metadata:
   title: Implement continuous integration and continuous delivery (CI/CD) in Microsoft Fabric
   description: Learn the key concepts and strategies for implementing continuous integration and continuous deployment (CI/CD) in Microsoft Fabric.
-  ms.date: 11/8/2024
+  ms.date: 03/20/2025
   author: wwlpublish
   ms.author: theresai
   ms.topic: module
-  ms.collection: wwl-ai-copilot
+  ms.collection: 
   ms.service: azure
 title: Implement continuous integration and continuous delivery (CI/CD) in Microsoft Fabric
 summary: Microsoft Fabric implements CI/CD using Git integration and deployment pipelines. These tools help you collaborate with your development team and provide you with an efficient process for delivering and updating content.  

learn-pr/wwl-data-ai/implement-llmops-azure-databricks/index.yml

@@ -5,12 +5,13 @@ metadata:
   prefetch-feature-rollout: true
   title: Implement LLMOps in Azure Databricks
   description: Implement LLMOps in Azure Databricks
-  ms.date: 08/23/2024
+  ms.date: 03/20/2025
   author: wwlpublish
   ms.author: madiepev
   ms.topic: module
   ms.service: azure
   ai-usage: ai-assisted
+  ms.collection:  wwl-ai-copilot
 title: Implement LLMOps in Azure Databricks
 summary:  Streamline the implementation of Large Language Models (LLMs) with LLMOps (LLM Operations) in Azure Databricks. Learn how to deploy and manage LLMs throughout their lifecycle using Azure Databricks.
 abstract: |

learn-pr/wwl-data-ai/introduction-language-models-databricks/index.yml

@@ -5,13 +5,14 @@ metadata:
   prefetch-feature-rollout: true
   title: Get started with language models in Azure Databricks
   description: Get started with language models in Azure Databricks
-  ms.date: 08/12/2024
+  ms.date: 03/20/2025
   author: wwlpublish
   ms.author: madiepev
   ms.topic: module
   ms.service: azure
   ai-usage: ai-assisted
   ms.custom: ai-learning-hub
+  ms.collection: wwl-ai-copilot
 title: Get started with language models in Azure Databricks
 summary: Large Language Models (LLMs) have revolutionized various industries by enabling advanced natural language processing (NLP) capabilities. These language models are utilized in a wide array of applications, including text summarization, sentiment analysis, language translation, zero-shot classification, and few-shot learning. 
 abstract: |

learn-pr/wwl-data-ai/multistage-reasoning-azure-databricks/index.yml

@@ -5,13 +5,14 @@ metadata:
   prefetch-feature-rollout: true
   title: Implement multi-stage reasoning in Azure Databricks
   description: Implement multi-stage reasoning in Azure Databricks
-  ms.date: 10/23/2024
+  ms.date: 03/20/2025
   author: wwlpublish
   ms.author: madiepev
   ms.topic: module
   ms.service: azure
   ai-usage: ai-assisted
   ms.custom: ai-learning-hub
+  ms.collection: wwl-ai-copilot
 title: Implement multi-stage reasoning in Azure Databricks
 summary: Multi-stage reasoning systems break down complex problems into multiple stages or steps, with each stage focusing on a specific reasoning task. The output of one stage serves as the input for the next, allowing for a more structured and systematic approach to problem-solving. 
 abstract: |

learn-pr/wwl-data-ai/responsible-language-models-azure-databricks/index.yml

@@ -5,12 +5,13 @@ metadata:
   prefetch-feature-rollout: true
   title: Review responsible AI principles for language models in Azure Databricks
   description: Review responsible AI principles for language models in in Azure Databricks
-  ms.date: 08/23/2024
+  ms.date: 03/20/2025
   author: wwlpublish
   ms.author: madiepev
   ms.topic: module
   ms.service: azure
   ai-usage: ai-assisted
+  ms.collection: wwl-ai-copilot
 title: Review responsible AI principles for language models in Azure Databricks
 summary: When working with Large Language Models (LLMs) in Azure Databricks, it's important to understand the responsible AI principles for implementation, ethical considerations, and how to mitigate risks. Based on identified risks, learn how to implement key security tooling for language models.
 abstract: |

learn-pr/wwl-data-ai/retrieval-augmented-generation-azure-databricks/index.yml

@@ -5,12 +5,13 @@ metadata:
   prefetch-feature-rollout: true
   title: Implement Retrieval Augmented Generation (RAG) with Azure Databricks
   description: Implement Retrieval Augmented Generation (RAG) with Azure Databricks
-  ms.date: 08/12/2024
+  ms.date: 03/25/2025
   author: wwlpublish
   ms.author: madiepev
   ms.topic: module
   ms.service: azure
   ai-usage: ai-assisted
+  ms.collection: wwl-ai-copilot
 title: Implement Retrieval Augmented Generation (RAG) with Azure Databricks
 summary: Retrieval Augmented Generation (RAG) is an advanced technique in natural language processing that enhances the capabilities of generative models by integrating external information retrieval mechanisms. When you use both generative models and retrieval systems, RAG dynamically fetches relevant information from external data sources to augment the generation process, leading to more accurate and contextually relevant outputs. 
 abstract: |

learn-pr/wwl-data-ai/secure-data-access-in-fabric/index.yml

@@ -5,7 +5,7 @@ metadata:
   prefetch-feature-rollout: true
   title: Secure data access in Microsoft Fabric
   description: Learn the key concepts and strategies for securing data access in Microsoft Fabric.
-  ms.date: 10/18/2024
+  ms.date: 03/20/2025
   author: wwlpublish
   ms.author: theresai
   ms.topic: module