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所在平台: Udemy |
课程主页: https://www.udemy.com/course/amazon-bedrock-aws-generative-ai-beginner-to-advanced/
课程评论：没有评论
课程名称：亚马逊Bedrock、亚马逊Q与AWS生成式人工智能 [2025] 课程概述： 本课程旨在深入探讨亚马逊Bedrock、亚马逊Q及AWS的生成式人工智能（GenAI），并通过实际案例帮助学员掌握相关技术。课程内容覆盖多个实际应用场景，包括媒体行业的海报设计、制造业的文本摘要、聊天机器人构建等。学员将学习到如何利用API网关、S3、Stable Diffusion等基础模型，构建复杂的云端应用。 主要课程内容： 1. **生成式人工智能的演变**：介绍AI和机器学习的基础知识，以及人工神经网络的基本原理。 2. **生成式人工智能与基础模型概念**：深入了解生成式AI工作原理（如提示、推理、上下文窗口等）和基础模型的运作机制。 3. **亚马逊Bedrock深入解析**：详尽的控制台操作、架构介绍、定价及推理参数。 4. **具体应用案例**： - 媒体行业海报设计 - 制造行业文本摘要 - 使用Bedrock构建聊天机器人 - 员工HR问答应用 - 无服务器电子学习应用 - 零售银行代理的构建 5. **生成式AI项目生命周期**：涵盖用例选择、基础模型选择、提示工程、基础模型的微调等关键步骤。 其他亮点： - 无需特定课程前置要求，仅需基础的AWS知识。 - 提供Python、AWS Lambda和API网关的基础知识回顾。 - 课程会随GenAI和Bedrock的发展进行持续更新，确保学员掌握行业前沿技术，为职业转型做好准备。 使用的服务： - 亚马逊Bedrock - 亚马逊Q - 各类基础模型（Claude、Stable Diffusion等） - AWS服务（Lambda、DynamoDB、API Gateway等） 本课程由一位资深AWS解决方案架构师和畅销Udemy讲师授课，适合想要在生成式人工智能领域发展职业的学员。
Amazon Bedrock, Amazon Q and AWS GenAI Course:***Hands - On Use Cases implemented as part of this course***Use Case 1 - Generate Poster Design for Media Industry using API Gateway, S3 and Stable Diffusion Foundation ModelUse Case 2 - Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation ModelUse Case 3 - Build a Chatbot using Amazon Bedrock - DeepSeek, Langchain and Streamlit.Use Case 4- Build an Employee HR Q & A Application with Retrieval Augmented Generation (RAG) - Claude FM + Langchain (Ochestrator)+ FAISS (Vector DB) + StreamlitUse Case 5 - Serverless e-Learning App using Bedrock Knowledge Base + Claude FM + AWS Lambda + API GatewayUse Case 6 - Build a Retail Banking Agent using Amazon Bedrock Agents and Knowledge Bases - Claude Sonnet + AWS Lambda + DynamoDB + Bedrock Agents + Knowledge Bases + OpenAPI SchemaUse Case 7 - Amazon Q Business - Build a Marketing Manager App with Amazon Q BusinessUse Case 8 - Amazon Q Developer - Overview of the Code Generation capabilities of Amazon Q Developer - Over the SDLCWelcome to the most comprehensive guide on Amazon Bedrock and Generative AI on AWS from a practising AWS Solution Architect and best-selling Udemy Instructor.This course will start from absolute basics on AI/ML, Generative AI and Amazon Bedrock and teach you how to build end to end enterprise apps on Image Generation using Stability Diffusion Foundation, Text Summarization using Cohere, Chatbot using Llama 2,Langchain, Streamlit and Code Generation using Amazon CodeWhisperer.The focus of this course is to help you switch careers and move into lucrative Generative AI roles.There are no course pre-requisites for this course except basic AWS Knowledge. I will provide basic overview of AI/ML concepts and have included Python, AWS Lambda and API Gateway refresher at end of course in case you are not familiar with python coding or these AWS services.I will continue to update this course as the GenAI and Bedrock evolves to give you a detailed understanding and learning required in enterprise context, so that you are ready to switch careers.Detailed Course OverviewSection 2 - Evolution of Generative AI: Learn fundamentals about AI, Machine Learning and Artificial Neural Networks (Layers, Weights & Bias).Section 3 - Generative AI & Foundation Models Concepts: Learn about How Generative AI works (Prompt, Inference, Completion, Context Window etc.) & Detailed Walkthrough of Foundation Model working.Section 4 - Amazon Bedrock - Deep Dive: Do detailed Console Walkthough, Bedrock Architecture, Pricing and Inference Parameters.Section 5 - Use Case 1: Media and Entertainment Industry: Generate Movie Poster Design using API Gateway, S3 and Stable Diffusion Foundation ModelSection 6 - Use Case 2: Text Summarization for Manufacturing Industry using API Gateway, S3 and Cohere Foundation ModelSection 7 - Use Case 3: Build a Chatbot using Bedrock - DeepSeek, Langchain and StreamlitSection 8 - Use Case 4- Build a Employee HR Q & A Application with Retrieval Augmented Generation (RAG) - Amazon Bedrock (Claude Foundation Model) + Langchain (Ochestrator)+ FAISS (Vector DB) + StreamlitSection 9 - Serverless e-Learning App using Bedrock Knowledge Base + Claude FM + AWS Lambda + API Gateway Section 10 - Build a Retail Banking Agent using Amazon Bedrock Agents and Knowledge Bases, Dynam0DB, LambdaSection 11 - GenAI Project Lifecycle: Phase 1 - Use Case Selection - Discuss about various phases of GenAI and How to identify right use caseSection 12 - GenAI Project Lifecycle: Phase 2 - Foundation Model Selection - Theory and Handson using AWS Bedrock Model Evaluation ServiceSection 13 - GenAI Project Lifecycle: Phase 3 - Prompt Engineering - Factors Impacting Prompt design, Prompt design Techniques (Zero Shot, One Shot.), Good practices for writing prompts for Claude, Titan and Stability AI Foundation ModelsSection 14 - GenAI Project Lifecycle: Phase 4 - Fine Tuning of Foundation Models - Theory and Hands-OnSection 15 - Code Generation using AWS CodeWhisperer and CDK - In TypescriptSection 16 - Python Basics RefresherSection 17 - AWS Lambda RefresherSection 18 - AWS API Gateway RefresherServices Used in the Course:Amazon BedrockAmazon Q Deepseek and Nova Pro Foundation ModelCohere Foundation ModelStability Diffusion ModelClaude Foundation Model from AnthropicClaude SonnetAmazon Bedrock AgentsBedrock Knowledge BaseLangchain - Chains and Memory ModulesFAISS Vector StoreAWS Code Generation using AWS Code Whisperer API GatewayAWS LambdaAWS DynamoDBOpen API SchemaStreamlitS3Prompt design Techniques (Zero Shot, One Shot.) for Claude, Titan and Stability AI Foundation Models (LLMs)Fine Tuning Foundation Models - Theory and Hands-OnPythonEvaluation of Foundation Models - Theory and Hands-OnBasics of AI, ML, Artificial Neural NetworksBasics of Generative AIEverything related to AWS Amazon Bedrock
