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所在平台: Udemy |
课程主页: https://www.udemy.com/course/building-multi-agentic-ai-workflows-on-aws-bedrock/
课程评论：没有评论
本课程“在AWS Bedrock上构建多智能体AI工作流”旨在教授学员如何利用多智能体协作来创建和部署前沿AI应用程序。课程以构建一个生产级别的旅行规划器为例，展示了Supervisor Agent如何协调任务流程，以及Collaborator和Helper Agents如何执行数据库查询、API调用和处理旅行偏好等具体任务。 课程内容涵盖了多智能体设计的核心原则，包括何时将任务分解为专门的智能体、智能体间的通信机制以及如何确保协作以实现快速响应。学员将深入学习AWS Bedrock中的大规模语言模型（LLMs），包括定制Prompt模板、重写默认参数以及优化AI输出以满足用户查询。 此外，课程还将演示如何将关键旅行数据存储在Amazon S3中，并利用AWS Lambda函数（Action Groups）构建无服务器的应用层，以实现轻量级且成本效益高的AI工作流。最后，课程将指导学员如何通过AWS API Gateway进行生产部署，构建一个健壮的接口，支持全球范围内的实时请求，并具备内置的可扩展性和安全性。 完成本课程后，学员将能够构建一个生产级的、多智能体的应用程序，该程序能够自动查找数据库记录、发出API请求并提供动态的旅行推荐。无论是初学者还是资深工程师，都能掌握如何协调Supervisor、Collaborator和Helper智能体，以构建真正适用于企业级的现实世界解决方案。
Do you want to harness the power of multi-agentic workflows to create cutting-edge AI applications-and deploy them at scale? This course is your gateway to building a fully operational, production-ready travel planner on AWS Bedrock, where multiple agents collaborate to deliver personalized, real-time recommendations. You'll see how Supervisor Agents coordinate the flow of tasks, while Collaborator and Helper Agents do the heavy lifting-making database lookups, handling API calls, and processing travel preferences on your behalf. By structuring your AI in this agent-centric way, you'll develop a scalable, modular system that adapts smoothly to complex, real-world scenarios.We begin with the fundamentals of multi-agentic design-when to break tasks into specialized agents, how to handle inter-agent communication, and ensuring seamless collaboration for lightning-fast responses. Next, we'll dive into AWS Bedrock's Large Language Models (LLMs), showcasing how to customize prompt templates, override default parameters, and optimize your AI's output for user queries. You'll learn how to store key travel data in Amazon S3 and build a serverless application layer using AWS Lambda functions-Action Groups-to keep your AI workflow lightweight and cost-effective. Finally, we'll demonstrate how to go production-ready by deploying via AWS API Gateway, providing a robust interface that can serve live requests from anywhere in the world with built-in scalability and security.By the end of this course, you'll have a production-grade, multi-agentic application capable of automatically looking up database records, making API requests, and delivering dynamic travel recommendations. Whether you're an aspiring AI developer or a seasoned engineer, you'll gain the hands-on skills to orchestrate Supervisor, Collaborator, and Helper Agents for real-world, enterprise-scale solutions. Join us and start building the next generation of AI with AWS Bedrock-all in a fully production-ready environment!
