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Working with large language models using Azure · LearnSpace
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Working with large language models using Azure

Курс от Microsoft
Средний≈ 21.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Learn how to build, customize, and deploy generative AI applications using Large Language Models (LLMs) and Microsoft Azure. This hands-on course introduces the practical techniques developers use to improve AI application performance, reliability, and business relevance. You’ll begin by exploring how LLMs work, including their architecture, capabilities, and limitations. From there, you’ll apply prompt engineering strategies to improve model outputs and build more effective AI interactions. The course then introduces Retrieval-Augmented Generation (RAG) pipelines, teaching you how to connect LLMs with external data sources to deliver grounded, accurate responses. You’ll also learn how to customize models using fine-tuning techniques and evaluate when to use fine-tuning, RAG, or hybrid approaches for different business scenarios. In the final modules, you’ll build and deploy generative AI applications using Azure AI Foundry and Azure OpenAI services while learning deployment, monitoring, and cost management strategies. By the end of this course, you’ll have practical experience building AI-powered applications using modern Azure AI tools and workflows.

Навыки, которые вы освоите

Fine-tuningGenerative Model ArchitecturesOpenAIModel TrainingSolution ArchitectureAI WorkflowsChatGPTSemantic Web

Программа курса

4 модулей · 77 учебных материалов

01Understanding Large Language Models (LLMs)21 материалов

Course Introduction

Building Solutions with Large Language Models on AzureВидеоLearning Paths and Prerequisites for Working with LLMsЧтение

Introduction to LLMs

Introduction to LLMs and prompt engineeringВидеоThe impact of LLMsВидео

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Microsoft

Преподаватель курса

Working with large language models using Azure
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Обучение на Coursera

≈ 21.8 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Пушту, Индонезийский, Испанский, Дари, Японский

Часть программы вашего университета
Overview of LLM interactionЧтение
Exploring LLM architectureЧтение
LLM fundamentals: From tokens to sequential modelsЧтение
The blueprint of modern LLMs: The transformer architectureЧтение
A look inside an LLM: From prompt to responseВидео
Interacting with LLMs: BasicsЗадание
Insights from LLM interactionsЧтение
LLM architecture: Practice QuizЗадание

Prompt engineering and optimization

Why Prompt Engineering MattersВидеоTechniques in prompt engineeringЧтениеCrafting effective promptsВидеоCreating successful promptsЗаданиеPrompt engineering success strategiesЧтениеPrompt engineering skills: Practice QuizЗаданиеInterpreting and Refining LLM Outputs DIALOGUEApplying LLM Fundamentals and Prompt Engineering in PracticeВидеоGraded Quiz: LLM Fundamentals and Prompt EngineeringЗадание
02Implementing RAG pipelines18 материалов

Introduction to RAG pipelines

Introduction to RAG: Grounding AI with dataВидеоRAG pipelines explainedВидеоUnderstanding RAG frameworksЧтениеData sources for RAG: Azure AI Search and the MarketplaceВидеоExploring RAG pipelinesЗаданиеIntroduction to RAG techniquesЧтениеBasic RAG pipeline setupЗаданиеReviewing your first RAG pipelineЧтениеRAG fundamentals: Practice QuizЗадание

Building advanced RAG pipelines

Advanced RAG pipeline techniquesЧтениеAdvanced RAG configurationsВидеоOptimizing RAG implementationsЗаданиеEffective RAG optimization strategiesЧтениеAdvanced RAG skills evaluation: Practice QuizЗадание Integrating and scaling RAGDIALOGUE
03Fine-tuning and customizing LLMs18 материалов

Fine-tuning LLMs for specific domains

The art of fine-tuningВидеоFine-tuning techniquesЧтениеA guided tour of Azure's fine-tuning interfaceВидеоFine-tuning practiceЗаданиеLearnings from fine-tuning LLMsЧтениеCustomized LLM implementationЗаданиеEvaluating your custom fine-tuned modelЧтениеFine-tuning comprehension: Practice QuizЗадание

Domain-specific customizations

Strategies for domain integrationЧтениеA framework for evaluating custom modelsЧтениеFrom customization to application: A domain-specific LLM labЗаданиеIntegrating domain expertise into your applicationВидеоAnalyzing domain specific LLMsЧтениеReal world use assessment: Practice QuizЗадание
04Developing generative applications with Azure20 материалов

Building generative AI applications

Introduction to application development: From model to productВидеоHarnessing Generative AI: From models to productsВидеоFoundations for generative applicationsЧтениеVisualizing an application with prompt flowВидеоApplication design basicsЗаданиеBuilding successful generative AI appsЧтениеApplication development with AzureЗаданиеKey concepts in prompt flow developmentЧтениеEvaluating generative application architectures: Practice QuizЗадание

Deploying and managing applications

Deployment and management techniquesЧтениеDeploying on Azure AI FoundryВидеоApplication deployment and monitoringЗаданиеEffective management of AI applicationsЧтениеDeployment and management skills: Practice QuizЗаданиеOptimizing application performanceDIALOGUE
Case study: Implementing advanced RAG in a corporate settingЧтение
End-to-End RAG Pipelines: From Setup to OptimizationВидео
RAG Pipeline Design, Optimization, and Evaluation AssessmentЗадание
Customizing for impactDIALOGUE
RAG vs. fine-tuning: A strategic decision frameworkЧтение
Mastering Customization: From Fine-Tuning to Strategic Decision-MakingВидео
Customization Strategies and Model Evaluation AssessmentЗадание
The MLOps lifecycle for generative AIЧтение
Thinking Like an AI Engineer: Optimizing Real-World AI ApplicationsDIALOGUE
Module 4 summary: Your journey as an AI application developerВидео
Generative AI Application Development and Deployment AssessmentЗадание
From Models to Production: Mastering Generative AI ApplicationsВидео