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AWS Tools and Services for AI · LearnSpace
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courseraIT и технологии

AWS Tools and Services for AI

Курс от LearnKartS
Средний≈ 9.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

The next generation of cloud applications is powered by intelligent systems—and AWS Tools and Services for AI is your gateway into building them. This course takes you from the core foundations of Generative AI into the technologies that drive modern innovation. You’ll explore diffusion models for content generation, transformer-based Large Language Models (LLMs) behind conversational AI, and multi-modal models that unify text, images, and data into a single intelligent pipeline. You’ll then step into enterprise-grade AI development using foundation models with Amazon Bedrock, master high-impact prompt creation through structured prompt engineering, and accelerate productivity with AI-powered assistance from Amazon Q. The journey advances into Retrieval-Augmented Generation (RAG), where models learn from real-time data—along with its strengths and limitations. You’ll work with AWS AI managed services, select the right AWS databases for embedding storage, apply model evaluation techniques, and learn how to continuously evaluate and refine models after deployment. By the end, you won’t just learn AI—you’ll be ready to deploy it with confidence at scale

Навыки, которые вы освоите

Retrieval-Augmented GenerationGenerative AIAmazon BedrockModel OptimizationPrompt EngineeringLLM ApplicationModel DeploymentEmbeddingsModel EvaluationMultimodal PromptsArtificial IntelligenceGenerative Model ArchitecturesAmazon Web ServicesArtificial Intelligence and Machine Learning (AI/ML)Prompt Engineering Tools

Программа курса

2 модулей · 80 учебных материалов

01Fundamentals of Generative AI46 материалов

Core Concepts of Generative AI and Amazon Bedrock

Course IntroductionВидеоLearning ObjectivesВидеоFundamentals of Generative AI ВидеоDemo : Getting Familiar with AWS AI ServicesВидеоDiffusion ModelsВидео

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LearnKartS

Преподаватель курса

AWS Tools and Services for AI
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 9.8 ч

2 модулей

Язык: Английский

Субтитры: Арабский, Французский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Хинди, Японский, Казахский, Венгерский, Польский

Часть программы вашего университета
Transformer-Based Large Language Models (LLMs)Видео
Multi-modal ModelsВидео
Core Concepts of Generative AIЗадание
Key Features of Amazon BedrockВидео
Amazon Bedrock – Use CasesВидео
Bedrock Cost ConsiderationsЧтение
Demo- Bedrock overviewВидео
Amazon Bedrock AgentsЧтение
Amazon BedrockЗадание
Understanding GPTВидео
Implementing Generative AI with Amazon BedrockВидео
Gen AI: Measuring Business Efficiency and ValueЧтение
Core Concepts of Generative AI and Amazon BedrockЗадание
SummaryВидео

Language Models, Prompt Engineering, and Amazon Q

Learning ObjectivesВидеоKey Concepts in Language Models: Part-1ВидеоKey Concepts in Language Models: Part-2ВидеоPrompts and Prompt EngineeringВидеоDesigning a PromptВидеоHyperparameterВидеоChoosing Top-p, Top-k and TemperatureВидеоLanguage Models and Prompt EngineeringЗаданиеPrompt Engineering TechniquesВидеоOptimizing PromptsЧтениеDemo- Prompt Engineering TechniquesВидеоPrompt Engineering RisksВидеоPrompt TemplatesВидеоDemo-Prompt Optimization ParametersВидеоPrompt Engineering TechniquesЗаданиеGenerative AI Foundation ModelsВидеоPractical Applications of Generative AIВидеоAmazon QВидеоKey Components of Amazon QВидеоGenerative AI Foundation Models and Amazon QЗаданиеDeveloper Fundamentals on Amazon QВидеоDemo- Amazon Q WalkthorughВидеоParty Rock - Playground for Gen AI appsЧтениеSummaryВидеоGenerative AI Foundations and Implementation with Amazon Bedrock & Amazon QDIALOGUELanguage Models, Prompt Engineering, and Amazon QЗаданиеApplying Prompt Engineering and Generative AI Using AWS AI ServicesDIALOGUE
02RAG (Retrieval-Augmented Generation) and Optimizing AI Model34 материалов

Retrieval-Augmented Generation (RAG) and AWS AI Services

Learning ObjectivesВидеоRAG (Retrieval-Augmented Generation)ВидеоApplications of RAGВидеоRAG – Advantages and DisadvantagesВидеоRetrieval-Augmented Generation (RAG)ЗаданиеAWS AI Managed Services Part-1ВидеоDemo- Amazon Transcribe in ActionВидеоDemo- Amazon Translate WalkthroughВидеоDemo-Simplifying Text Analysis with ComprehendВидеоDemo- Exploring Amazon PollyВидеоAWS AI Managed Services Part-2ВидеоDemo- Understanding Amazon LexВидеоDemo-Amazon Rekognition WalkthroughВидеоDemo- Amazon Textract OverviewВидеоAWS AI Managed ServicesЗаданиеAWS Database Options for Storing EmbeddingsВидеоAmazon Mechanical TurkЧтениеAmazon PersonalizeЧтениеRetrieval-Augmented Generation (RAG) and AWS AI ServicesЗаданиеSummaryВидео

Model Optimization

Learning ObjectivesВидеоModel Pre-Training and Fine-Tuning with AWS ВидеоFine-Tuning for Conversational AIВидеоDemo- Fine-Tuning Bedrock ModelВидеоModel OptimizationЗаданиеModel Evaluation TechniquesВидео
Industry-Specific Use Cases for Model EvaluationЧтение
Evaluate and Refine Models Post-deploymentВидео
Advanced Guidelines for Continuous Model OptimizationЧтение
SummaryВидео
Advanced AI Workflows: RAG, Model Fine-Tuning, Evaluation, and Continuous Optimization on AWSDIALOGUE
Model OptimizationЗадание
Fine-Tuning and Evaluating AI Models Using AWS ServicesDIALOGUE
Course CompletionВидео