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  • learn-pr/wwl-data-ai
    • evaluate-language-models-azure-databricks
    • fine-tune-azure-databricks
    • implement-llmops-azure-databricks
    • multistage-reasoning-azure-databricks
    • responsible-language-models-azure-databricks

5 files changed

+204
-204
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‎learn-pr/wwl-data-ai/evaluate-language-models-azure-databricks/index.yml

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### YamlMime:Module
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uid: learn.wwl.evaluate-language-models-azure-databricks
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metadata:
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title: Evaluate language models with Azure Databricks
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description: Evaluate language models with Azure Databricks
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ms.date: 03/20/2025
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author: wwlpublish
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ms.author: madiepev
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ms.topic: module
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ms.service: azure
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ai-usage: ai-assisted
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ms.collection: wwl-ai-copilot
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title: Evaluate language models with Azure Databricks
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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.
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abstract: |
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In this module, you learn how to:
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- Compare LLM and traditional ML evaluations.
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- Describe the relationship between LLM evaluation and evaluation of entire AI systems.
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- Describe generic LLM evaluation metrics like accuracy, perplexity, and toxicity.
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- Describe LLM-as-a-judge for evaluation.
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prerequisites: |
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Before starting this module, you should be familiar with Azure Databricks. Consider completing [Explore Azure Databricks](/training/modules/explore-azure-databricks?azure-portal=true) before starting this module.
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iconUrl: /learn/achievements/describe-azure-databricks.svg
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levels:
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- intermediate
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roles:
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- data-engineer
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products:
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- azure-databricks
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units:
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- learn.wwl.evaluate-language-models-azure-databricks.introduction
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- learn.wwl.evaluate-language-models-azure-databricks.compare-evaluations
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- learn.wwl.evaluate-language-models-azure-databricks.ai-systems
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- learn.wwl.evaluate-language-models-azure-databricks.standard-metrics
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- learn.wwl.evaluate-language-models-azure-databricks.language-model-judge
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- learn.wwl.evaluate-language-models-azure-databricks.exercise
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- learn.wwl.evaluate-language-models-azure-databricks.knowledge-check
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- learn.wwl.evaluate-language-models-azure-databricks.summary
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badge:
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uid: learn.wwl.evaluate-language-models-azure-databricks.badge
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### YamlMime:Module
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uid: learn.wwl.evaluate-language-models-azure-databricks
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metadata:
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title: Evaluate language models with Azure Databricks
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description: Evaluate language models with Azure Databricks
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ms.date: 03/20/2025
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author: wwlpublish
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ms.author: theresai
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ms.topic: module
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ms.service: azure
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ai-usage: ai-assisted
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ms.collection: wwl-ai-copilot
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title: Evaluate language models with Azure Databricks
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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.
15+
abstract: |
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In this module, you learn how to:
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- Compare LLM and traditional ML evaluations.
18+
- Describe the relationship between LLM evaluation and evaluation of entire AI systems.
19+
- Describe generic LLM evaluation metrics like accuracy, perplexity, and toxicity.
20+
- Describe LLM-as-a-judge for evaluation.
21+
prerequisites: |
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Before starting this module, you should be familiar with Azure Databricks. Consider completing [Explore Azure Databricks](/training/modules/explore-azure-databricks?azure-portal=true) before starting this module.
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iconUrl: /learn/achievements/describe-azure-databricks.svg
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levels:
25+
- intermediate
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roles:
27+
- data-engineer
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products:
29+
- azure-databricks
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units:
31+
- learn.wwl.evaluate-language-models-azure-databricks.introduction
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- learn.wwl.evaluate-language-models-azure-databricks.compare-evaluations
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- learn.wwl.evaluate-language-models-azure-databricks.ai-systems
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- learn.wwl.evaluate-language-models-azure-databricks.standard-metrics
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- learn.wwl.evaluate-language-models-azure-databricks.language-model-judge
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- learn.wwl.evaluate-language-models-azure-databricks.exercise
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- learn.wwl.evaluate-language-models-azure-databricks.knowledge-check
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- learn.wwl.evaluate-language-models-azure-databricks.summary
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badge:
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uid: learn.wwl.evaluate-language-models-azure-databricks.badge
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‎learn-pr/wwl-data-ai/fine-tune-azure-databricks/index.yml

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### YamlMime:Module
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uid: learn.wwl.fine-tune-azure-databricks
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metadata:
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title: Fine-tune language models with Azure Databricks
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description: Fine-tune language models with Azure Databricks
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ms.date: 03/20/2025
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author: wwlpublish
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ms.author: madiepev
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ms.topic: module
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ms.service: azure
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ai-usage: ai-assisted
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ms.custom: ai-learning-hub
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ms.collection: wwl-ai-copilot
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title: Fine-tune language models with Azure Databricks
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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.
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abstract: |
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In this module, you learn how to:
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- Understand when to use fine-tuning.
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- Prepare your data for fine-tuning.
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- Fine-tune an Azure OpenAI model.
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prerequisites: |
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Before starting this module, you should be familiar with Azure Databricks. Consider completing [Explore Azure Databricks](/training/modules/explore-azure-databricks?azure-portal=true) before starting this module.
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iconUrl: /learn/achievements/describe-azure-databricks.svg
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levels:
25-
- intermediate
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roles:
27-
- data-engineer
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products:
29-
- azure-databricks
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units:
31-
- learn.wwl.fine-tune-azure-databricks.introduction
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- learn.wwl.fine-tune-azure-databricks.fine-tune-concept
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- learn.wwl.fine-tune-azure-databricks.prepare-data
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- learn.wwl.fine-tune-azure-databricks.how-to-fine-tune
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- learn.wwl.fine-tune-azure-databricks.exercise
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- learn.wwl.fine-tune-azure-databricks.knowledge-check
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- learn.wwl.fine-tune-azure-databricks.summary
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badge:
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uid: learn.wwl.fine-tune-azure-databricks.badge
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1+
### YamlMime:Module
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uid: learn.wwl.fine-tune-azure-databricks
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metadata:
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title: Fine-tune language models with Azure Databricks
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description: Fine-tune language models with Azure Databricks
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ms.date: 03/20/2025
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author: wwlpublish
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ms.author: theresai
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ms.topic: module
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ms.service: azure
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ai-usage: ai-assisted
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ms.custom: ai-learning-hub
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ms.collection: wwl-ai-copilot
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title: Fine-tune language models with Azure Databricks
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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.
16+
abstract: |
17+
In this module, you learn how to:
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- Understand when to use fine-tuning.
19+
- Prepare your data for fine-tuning.
20+
- Fine-tune an Azure OpenAI model.
21+
prerequisites: |
22+
Before starting this module, you should be familiar with Azure Databricks. Consider completing [Explore Azure Databricks](/training/modules/explore-azure-databricks?azure-portal=true) before starting this module.
23+
iconUrl: /learn/achievements/describe-azure-databricks.svg
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levels:
25+
- intermediate
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roles:
27+
- data-engineer
28+
products:
29+
- azure-databricks
30+
units:
31+
- learn.wwl.fine-tune-azure-databricks.introduction
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- learn.wwl.fine-tune-azure-databricks.fine-tune-concept
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- learn.wwl.fine-tune-azure-databricks.prepare-data
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- learn.wwl.fine-tune-azure-databricks.how-to-fine-tune
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- learn.wwl.fine-tune-azure-databricks.exercise
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- learn.wwl.fine-tune-azure-databricks.knowledge-check
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- learn.wwl.fine-tune-azure-databricks.summary
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badge:
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uid: learn.wwl.fine-tune-azure-databricks.badge
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‎learn-pr/wwl-data-ai/implement-llmops-azure-databricks/index.yml

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### YamlMime:Module
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uid: learn.wwl.implement-llmops-azure-databricks
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metadata:
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title: Implement LLMOps in Azure Databricks
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description: Implement LLMOps in Azure Databricks
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ms.date: 03/20/2025
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author: wwlpublish
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ms.author: madiepev
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ms.topic: module
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ms.service: azure
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ai-usage: ai-assisted
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ms.collection: wwl-ai-copilot
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title: Implement LLMOps in Azure Databricks
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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.
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abstract: |
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In this module, you learn how to:
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- Describe the LLM lifecycle overview.
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- Identify the model deployment option that best fits your needs.
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- Use MLflow and Unity Catalog to implement LLMops.
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prerequisites: |
21-
Before starting this module, you should be familiar with Azure Databricks. Consider completing [Explore Azure Databricks](/training/modules/explore-azure-databricks?azure-portal=true) before starting this module.
22-
iconUrl: /learn/achievements/describe-azure-databricks.svg
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levels:
24-
- intermediate
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roles:
26-
- data-engineer
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products:
28-
- azure-databricks
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units:
30-
- learn.wwl.implement-llmops-azure-databricks.introduction
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- learn.wwl.implement-llmops-azure-databricks.lifecycle-overview
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- learn.wwl.implement-llmops-azure-databricks.deployments-overview
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- learn.wwl.implement-llmops-azure-databricks.mlflow-overview
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- learn.wwl.implement-llmops-azure-databricks.unity-catalog
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- learn.wwl.implement-llmops-azure-databricks.exercise
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- learn.wwl.implement-llmops-azure-databricks.knowledge-check
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- learn.wwl.implement-llmops-azure-databricks.summary
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badge:
39-
uid: learn.wwl.implement-llmops-azure-databricks.badge
40-
1+
### YamlMime:Module
2+
uid: learn.wwl.implement-llmops-azure-databricks
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metadata:
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title: Implement LLMOps in Azure Databricks
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description: Implement LLMOps in Azure Databricks
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ms.date: 03/20/2025
7+
author: wwlpublish
8+
ms.author: theresai
9+
ms.topic: module
10+
ms.service: azure
11+
ai-usage: ai-assisted
12+
ms.collection: wwl-ai-copilot
13+
title: Implement LLMOps in Azure Databricks
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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.
15+
abstract: |
16+
In this module, you learn how to:
17+
- Describe the LLM lifecycle overview.
18+
- Identify the model deployment option that best fits your needs.
19+
- Use MLflow and Unity Catalog to implement LLMops.
20+
prerequisites: |
21+
Before starting this module, you should be familiar with Azure Databricks. Consider completing [Explore Azure Databricks](/training/modules/explore-azure-databricks?azure-portal=true) before starting this module.
22+
iconUrl: /learn/achievements/describe-azure-databricks.svg
23+
levels:
24+
- intermediate
25+
roles:
26+
- data-engineer
27+
products:
28+
- azure-databricks
29+
units:
30+
- learn.wwl.implement-llmops-azure-databricks.introduction
31+
- learn.wwl.implement-llmops-azure-databricks.lifecycle-overview
32+
- learn.wwl.implement-llmops-azure-databricks.deployments-overview
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- learn.wwl.implement-llmops-azure-databricks.mlflow-overview
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- learn.wwl.implement-llmops-azure-databricks.unity-catalog
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- learn.wwl.implement-llmops-azure-databricks.exercise
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- learn.wwl.implement-llmops-azure-databricks.knowledge-check
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- learn.wwl.implement-llmops-azure-databricks.summary
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badge:
39+
uid: learn.wwl.implement-llmops-azure-databricks.badge
40+

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

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### YamlMime:Module
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uid: learn.wwl.multistage-reasoning-azure-databricks
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metadata:
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title: Implement multi-stage reasoning in Azure Databricks
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description: Implement multi-stage reasoning in Azure Databricks
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ms.date: 03/20/2025
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author: wwlpublish
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ms.author: madiepev
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ms.topic: module
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ms.service: azure
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ai-usage: ai-assisted
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ms.custom: ai-learning-hub
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ms.collection: wwl-ai-copilot
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title: Implement multi-stage reasoning in Azure Databricks
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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.
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abstract: |
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In this module, you learn how to:
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- Identify the need for multi-stage reasoning systems.
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- Describe a multi-stage reasoning workflow.
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- Implement multi-stage reasoning with libraries like LangChain, LlamaIndex, Haystack, and the DSPy framework.
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prerequisites: |
22-
Before starting this module, you should be familiar with Azure Databricks. Consider completing [Explore Azure Databricks](/training/modules/explore-azure-databricks?azure-portal=true) before starting this module.
23-
iconUrl: /learn/achievements/describe-azure-databricks.svg
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levels:
25-
- intermediate
26-
roles:
27-
- data-engineer
28-
products:
29-
- azure-databricks
30-
units:
31-
- learn.wwl.multistage-reasoning-azure-databricks.introduction
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- learn.wwl.multistage-reasoning-azure-databricks.multi-stage-reasoning-concepts
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- learn.wwl.multistage-reasoning-azure-databricks.langchain
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- learn.wwl.multistage-reasoning-azure-databricks.llamaindex
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- learn.wwl.multistage-reasoning-azure-databricks.haystack
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- learn.wwl.multistage-reasoning-azure-databricks.dspy-framework
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- learn.wwl.multistage-reasoning-azure-databricks.exercise
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- learn.wwl.multistage-reasoning-azure-databricks.knowledge-check
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- learn.wwl.multistage-reasoning-azure-databricks.summary
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badge:
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uid: learn.wwl.multistage-reasoning-azure-databricks.badge
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1+
### YamlMime:Module
2+
uid: learn.wwl.multistage-reasoning-azure-databricks
3+
metadata:
4+
title: Implement multi-stage reasoning in Azure Databricks
5+
description: Implement multi-stage reasoning in Azure Databricks
6+
ms.date: 03/20/2025
7+
author: wwlpublish
8+
ms.author: theresai
9+
ms.topic: module
10+
ms.service: azure
11+
ai-usage: ai-assisted
12+
ms.custom: ai-learning-hub
13+
ms.collection: wwl-ai-copilot
14+
title: Implement multi-stage reasoning in Azure Databricks
15+
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.
16+
abstract: |
17+
In this module, you learn how to:
18+
- Identify the need for multi-stage reasoning systems.
19+
- Describe a multi-stage reasoning workflow.
20+
- Implement multi-stage reasoning with libraries like LangChain, LlamaIndex, Haystack, and the DSPy framework.
21+
prerequisites: |
22+
Before starting this module, you should be familiar with Azure Databricks. Consider completing [Explore Azure Databricks](/training/modules/explore-azure-databricks?azure-portal=true) before starting this module.
23+
iconUrl: /learn/achievements/describe-azure-databricks.svg
24+
levels:
25+
- intermediate
26+
roles:
27+
- data-engineer
28+
products:
29+
- azure-databricks
30+
units:
31+
- learn.wwl.multistage-reasoning-azure-databricks.introduction
32+
- learn.wwl.multistage-reasoning-azure-databricks.multi-stage-reasoning-concepts
33+
- learn.wwl.multistage-reasoning-azure-databricks.langchain
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- learn.wwl.multistage-reasoning-azure-databricks.llamaindex
35+
- learn.wwl.multistage-reasoning-azure-databricks.haystack
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- learn.wwl.multistage-reasoning-azure-databricks.dspy-framework
37+
- learn.wwl.multistage-reasoning-azure-databricks.exercise
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- learn.wwl.multistage-reasoning-azure-databricks.knowledge-check
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- learn.wwl.multistage-reasoning-azure-databricks.summary
40+
badge:
41+
uid: learn.wwl.multistage-reasoning-azure-databricks.badge
42+

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