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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mastering-aws-bedrock-build-intelligent-genai-applications/
课程评论：没有评论
课程名称：Amazon Bedrock 指南：生成式人工智能与 AI 代理 概述：本课程旨在帮助您充分利用 Amazon Bedrock 的工具和服务，构建智能应用程序。无论您是初学者还是有经验的开发者，本课程将通过概念讲解、配置设置和实践练习，逐步带您探索 AWS Bedrock 在构建智能应用中的潜力。 课程内容： 1. 知识库（KB）：深入了解向量嵌入和检索增强生成（RAG）的概念，这些都是优化大规模 AI 应用的基础。学习如何配置知识库，并通过实际案例与其他 AWS Bedrock 工具无缝集成。 2. RAG 操作：使用 Anthropic Claude 模型与 OpenSearch Serverless 作为向量存储执行 RAG 操作，同时也使用 OpenAI 的 ChatGPT 模型与内存向量存储进行 RAG 操作。 3. 数据检索者：了解不同的数据检索方法，以支持摘要生成的 RAG 设计，并进行相关的实践活动。 4. AI 代理：掌握 AWS Bedrock 代理的配置，以简化 AI 工作流程。通过实施操作组和处理参数的实践，理解代理在动态智能 AI 交互中的角色。 - 多模态营养 AI 代理：使用开源组件如 Haystack 和 HuggingFace，与多模态 Phi-3.5 进行多代理使用案例。 - 多代理旅行 AI 代理：使用开源框架 CrewAI 和 OpenAI ChatGPT 模型，实现规划与推理能力。 5. 网络安全/渗透测试的 AI 代理：通过使用 CrewAI 框架，实际操作检测网络漏洞。 6. 提示管理：学习创建、管理和优化提示，以微调 AI 响应，探索变量与策略的使用。 7. 工作流程：学习通过整合知识库、AI 代理与提示构建高级工作流程，设计无缝互动并管理复杂的应用逻辑。 8. 实践实验室：通过实践实验室应用所学知识，构建完整的解决方案，解决复杂问题。 9. 安全防护：了解 AI 系统中的安全与合规性重要性，学习如何实施健全的安全防护机制，包括关于幻觉、提示注入等主题的深度学习。 10. 模型评估：评估、比较并选择适合您用例的基础模型，准备将构建在 Amazon Bedrock 知识库上的 RAG 应用推向生产。 11. 批量推理：使用批量推理高效处理大量请求，通过单一请求生成响应并保存至 Amazon S3 存储桶。 12. 模型微调：对预训练的基础模型进行微调，以增强其广泛能力并定制适用于您小型语料库的模型。 本课程提供了全面的学习内容，帮助您在生成式人工智能领域中掌握关键技能和技术。
Unlock the Power of Amazon Bedrock to Build AI-Powered ApplicationsWelcome to Mastering Amazon Bedrock, a comprehensive course designed to help you harness the power of AWS Bedrock's tools and services. Whether you're a beginner or an experienced developer, this course will take you step-by-step through concepts, configurations, and hands-on exercises that showcase the potential of AWS Bedrock in building intelligent applications.What You'll Learn:Knowledge Bases (KB): Dive deep into the concept of vector embeddings and retrieval-augmented generation (RAG), essential for optimizing large-scale AI applications. Learn how to configure Knowledge Bases and integrate them seamlessly with other AWS Bedrock tools using practical examples to solidify your understanding.RAG with Amazon Bedrock - We will use Anthropic Claude Model with OpenSearch Serverless as vector storage to perform the RAG operationsRAG with Open Source - We will also use OpenAI's ChatGPT model with in memory vector storage to perform RAG operationsRetrievers - RAG pattern relies heavily on retrieval. There are many ways to retrieve data for summarization. We will learn and explore about different ways to retrieve the contents. Followed by a hands-on activity AI Agents: Master the configuration of AWS Bedrock agents to streamline AI workflows. Gain hands-on experience in implementing action groups, handling parameters, and orchestrating requests effectively to Knowledge Bases. Understand how agents serve as the backbone of dynamic and intelligent AI interactions. We will cover 2 use cases of AI Agents. Multimodal Nutritional AI Agent - We will use Open Source components like Haystack, FastRag, HuggingFace with Multimodel modal Phi-3.5-vision-instruct to run multi Agentic use case. We will also cover multi agentic Tools with Multi-Hop and ReAct Prompt.Multi-Agentic Travel AI Agent - We will use Open Source framework - CrewAI and OpenAI ChatGPT model with planning and reasoning ability using Tools with Multi-Hop and ReAct Prompt.AI Agents for Cybersecurity/Penetration Testing with GenAI Multi-Agentic Agent - Learn about AI Agents and do a Hand On to scan Web Vulnerabilities for Cyber Security Penetration Testing using Open Source framework, CrewAI. Prompt Management: Develop expertise in creating, managing, and optimizing prompts to fine-tune AI responses. Explore the use of variables and strategies for effective prompt engineering, a critical skill for delivering customized user experiences in AI applications.Flows: Learn to build advanced workflows by integrating Knowledge Bases, AI Agents, and Prompts. Flows allow you to design seamless interactions and manage complex application logic, ensuring efficient and scalable AI solutions.Hands-On Lab: Apply your knowledge through hands-on labs that walk you through building end-to-end solutions. Combine Knowledge Bases, AI Agents, Flows and Prompts to create practical, real-world AI applications that solve complex problems. Guardrails: Understand the importance of security and compliance in AI systems. Learn how to implement robust guardrails to ensure your applications adhere to best practices, remain reliable, and mitigate risks effectively. We will cover different Guardrails Topics like Hallucination, Prompt Injections and take a deep dive into each one of them.Guardrails with Amazon Bedrock - We'll do a hands-on Guardrails(text, image) on Bedrock platform.Guardrails with Open Source tools - We will also do a hands-on Guardrails with Open Source models like Prompt Guard (Llama Family), Phi3 Hallucination Judge from HuggingFace to detect Prompt Injection and Hallucination respectively on a Google Colab notebook.Evaluators: Evaluate, compare, and select the foundation model for your use case with Model Evaluation. Prepare your RAG applications for production that are built on Amazon Bedrock Knowledge Bases or your own custom RAG systems by evaluating the retrieve or retrieve and generate functions.We will cover topics like LLM-As-A-Judge, Context Relevancy using Amazon bedrock platform and open source toolsBatch Inference: With batch inference, you can submit multiple prompts and generate responses asynchronously. Batch inference helps you process a large number of requests efficiently by sending a single request and generating the responses in an Amazon S3 bucket. Model Fine Tune: We will fine-tune a pre-trained foundation model to take advantage of their broad capabilities while customizing a model on your own small, corpus.
