What background knowledge is necessary to enroll in the course?

This course assumes a basic understanding of cloud computing, programming (particularly Python), and AWS services. Familiarity with AI concepts such as machine learning and neural networks is also beneficial. Prior knowledge of Docker and AWS CLI is recommended.

Who is this course for?

This course is ideal for developers, data scientists, and AI enthusiasts who want to gain practical experience building AI agents. It is also suitable for professionals looking to advance their careers in AI or cloud computing, particularly in organizations that leverage AWS services.

How long does it take to complete the course?

The course is designed to be completed in approximately 6 hours, depending on your prior knowledge and pace of learning.

Can I preview a course before enrolling?

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

When will I have access to the lectures and assignments?

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

What will I get when I enroll?

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Build Real world End-to-End AI Agents using AWS Bedrock

Build Real world End-to-End AI Agents using AWS Bedrock

位教师：Packt - Course Instructors

包含在中

了解更多

10个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

10个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Master how to create autonomous AI agents capable of performing complex tasks
Gain expertise in deploying AI applications using AWS Bedrock and integrating them with other AWS services
Learn how to implement multi-agent collaboration for solving complex, real-world problems
Explore how to design and optimize AI workflows using Bedrock Flows for generative applications

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有10个模块

This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. You will learn to build advanced AI agents using AWS Bedrock, a platform for creating generative AI applications. The course introduces key concepts like Retrieval-Augmented Generation (RAG) and function orchestration to enhance AI models with external data. As you progress, you'll gain hands-on experience deploying chatbots, creating knowledge bases, and integrating AWS services like Lambda and DynamoDB.. Throughout the course, you’ll dive deeper into working with multi-agent systems and their applications in real-world scenarios like product inventory management and mortgage processing. By the end, you'll have the skills to build fully functional, scalable AI agents that can interact with complex data sources. This course is perfect for developers with some Python and cloud experience. Knowledge of basic cloud computing concepts is helpful, but no prior experience with AWS Bedrock is required. Ideal for those aiming to create sophisticated AI solutions.

In this module, we will introduce the foundational concepts of the course and provide an overview of the key industry roles emerging due to AI advancements. We will also review essential prerequisites to ensure you are well-prepared to proceed through the course successfully.

涵盖的内容

3个视频1篇阅读材料

In this module, we will explore key concepts in AI app development, focusing on RAG architecture and its importance. Additionally, you will learn how function calling and orchestration enhance workflows, as well as gain an understanding of agentic AI and its ability to enable task automation.

涵盖的内容

3个视频1个作业

In this module, we will guide you through the installation of the necessary tools and frameworks, including Docker and AWS CLI. This ensures that your development environment is properly configured for engaging in practical labs throughout the course.

涵盖的内容

2个视频1个作业

In this module, we will introduce you to AWS Bedrock, a powerful platform for building generative AI applications. You'll learn to navigate the console, interact with services via Python/Boto3, and deploy AI applications such as a chatbot on AWS ECS.

涵盖的内容

6个视频1个作业

6个视频总计42分钟

Overview of AWS Bedrock 4分钟
Lab - Bedrock Console Walkthrough 4分钟
Lab - Getting Started with Python/Boto3 for Bedrock 8分钟
Lab - Deploy Streamlit Chatbot as Docker Container to AWS ECS 17分钟
Lab - Invoke Bedrock's MultiModal LLMs Using Python/Boto3 Library 7分钟
Assignment - Deploy Image Generation using Stable Diffusion to AWS ECS 2分钟

1个作业总计15分钟

Introduction to AWS Bedrock - Assessment 15分钟

In this module, we will cover how to utilize the Bedrock KnowledgeBase as a vector store to enhance AI model responses. You’ll learn to build and manage a KnowledgeBase and integrate it with Lambda functions and Python SDK to interact with AI applications.

涵盖的内容

7个视频1个作业

7个视频总计58分钟

Overview of AWS Bedrock KnowledgeBase 4分钟
Lab - Create Your First KnowledgeBase for Q&A 7分钟
Lab - Test Your KnowledgeBase Using Web Console 2分钟
Lab - Invoke KnowledgeBase with Python SDK & Lambda Functions Using API Gateway 16分钟
Assignment - Setup Electric Vehicle Infrastructure KnowledgeBase 2分钟
Assignment Solution - Part 1 | Deploy Lambda Functions with API Gateway 15分钟
Assignment Solution - Part 2 | Deploy Chatbot to AWS ECS 11分钟

1个作业总计15分钟

Working with Bedrock KnowledgeBase as Vector Store - Assessment 15分钟

In this module, we will introduce Bedrock agents and guide you through setting up essential data sources for these agents. You’ll learn to deploy and manage agents, giving you the tools to create intelligent systems capable of executing tasks autonomously.

涵盖的内容

6个视频1个作业

6个视频总计49分钟

Overview of Bedrock Agents 2分钟
Use-Case Overview - Product Inventory Assistant for ECommerce Platforms 5分钟
Lab - Setup DynamoDB and Bedrock KnowledgeBase as Data Sources 7分钟
Lab - Deploy & Test Lambda Functions to Access DynamoDB 13分钟
Lab - Deploy Bedrock Agent - Full Implementation 18分钟
Lab - Invoke Agents Using Python/Boto3 SDK 5分钟

1个作业总计15分钟

Getting Started with Bedrock Agents - Assessment 15分钟

In this module, we will dive into the concept of multi-agent collaboration, where agents work together to solve complex tasks. You will set up multiple agents and configure them for tasks like mortgage assistance, culminating in deploying a chatbot to AWS ECS.

涵盖的内容

7个视频1个作业

7个视频总计66分钟

Introduction to Multi-Agent Collaboration 3分钟
Overview of Mortgage Assistant Lab 2分钟
Lab - Setup First Sub-Agent Part-1 9分钟
Lab - Setup First Sub-Agent Part-2 13分钟
Lab - Setup Your Second Sub-Agent 16分钟
Lab - Setup Supervisor Agent 10分钟
Lab - Deploy a Mortgage Assistant Chatbot to AWS ECS 12分钟

1个作业总计15分钟

Introduction to Multi-Agent Collaboration Using Bedrock - Assessment 15分钟

In this module, we will guide you through the development of a hotel booking assistant, integrating AWS Bedrock with Lambda functions and DynamoDB. You will complete a full deployment to AWS ECS for performance and scalability.

涵盖的内容

3个视频1个作业

In this module, we will explore using Amazon Redshift as a structured data source to enhance the Bedrock KnowledgeBase. You'll learn how to set up Redshift, integrate it with Bedrock, and query it using Lambda functions to support AI agents.

涵盖的内容

6个视频1个作业

6个视频总计30分钟

Introduction to Structured Data Source for KnowledgeBase 2分钟
Lab - Setup Redshift Serverless for Rental Apartment Listings 8分钟
Lab - Setup KnowledgeBase with Redshift Data Source 4分钟
Lab - Setup Rental Assistant Agents Using Bedrock 5分钟
Remember This Before Using Redshift with Bedrock KnowledgeBase 1分钟
Lab - Assignment | Deploy Lambda Functions to Query Redshift for Bedrock Agents 9分钟

1个作业总计15分钟

Redshift as a KnowledgeBase for Structured Data - Assessment 15分钟

In this module, we will introduce you to Bedrock Flows, simplifying the creation of workflows for AI applications. You will build and deploy workflows for use cases like eCommerce feedback, ensuring efficient and reliable AI responses integrated with Bedrock KnowledgeBase.

涵盖的内容

7个视频3个作业

7个视频总计55分钟

Introduction to Bedrock Flows 2分钟
Lab - Build Your First Workflow for User Feedback for Ecommerce Stores 15分钟
Lab - Integrate Bedrock KnowledgeBase with Flows for Reliable Responses 11分钟
Introduction to Multi-turn Conversations 3分钟
Lab - Deploy Agents with Bedrock Flows | Part 1 4分钟
Lab - Deploy Agents with Bedrock Flows | Part 2 13分钟
Lab - Invoke Bedrock Flows Using Python/Boto3 SDK 8分钟

3个作业总计90分钟

Workflows for Generative AI Apps Using Bedrock Flows - Assessment 15分钟
Full Course Assessment 60分钟
Full Course Practice Assessment 15分钟

位教师

Packt - Course Instructors

Packt

1,528 门课程 402,494 名学生

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Amazon Web Services
Building AI Agents with Amazon Bedrock AgentCore
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Deploy AI Agents with Amazon Bedrock – Step-by-Step Guide
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状态：免费试用
Amazon Web Services
Generative AI Applications with Amazon Bedrock
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Getting Started with Amazon Bedrock
课程

人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

自 2018开始学习的学生

''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'

Jennifer J.

自 2020开始学习的学生

''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'

Larry W.

自 2021开始学习的学生

''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'

Chaitanya A.

''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'

通过 Coursera Plus 开启新生涯

无限制访问 10,000+ 世界一流的课程、实践项目和就业就绪证书课程 - 所有这些都包含在您的订阅中

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常见问题

AI Agent Development with AWS Bedrock focuses on building autonomous AI agents using AWS Bedrock services. This course teaches how to create intelligent systems capable of performing tasks independently, leveraging AWS's cloud infrastructure. It is relevant due to the growing demand for AI professionals and the increasing need for automated systems in industries like eCommerce, healthcare, and finance.

This course is designed to equip learners with the skills needed to develop AI agents using AWS Bedrock. You will learn fundamental concepts such as the Retrieval-Augmented Generation (RAG) architecture, function calling, orchestration, and multi-agent collaboration. It also covers hands-on labs for deploying and interacting with AI models using AWS services.

Upon completion, you'll be able to build and deploy AI agents using AWS Bedrock. You will have experience setting up AI workflows, creating KnowledgeBases, deploying Lambda functions, and working with various AI models for real-world applications such as chatbots, inventory management, and product recommendations.

Build Real world End-to-End AI Agents using AWS Bedrock

Build Real world End-to-End AI Agents using AWS Bedrock

您将学到什么

您将获得的技能

要了解的详细信息

了解顶级公司的员工如何掌握热门技能

该课程共有10个模块

Course Introduction & Background

涵盖的内容

Fundamental Concepts in LLM-Driven Apps & AI

涵盖的内容

Dependent Software Installation

涵盖的内容

Introduction to AWS Bedrock

涵盖的内容

Working with Bedrock KnowledgeBase as Vector Store

涵盖的内容

Getting Started with Bedrock Agents

涵盖的内容

Introduction to Multi-Agent Collaboration Using Bedrock

涵盖的内容

Lab - Develop Hotel Booking Assistant with AWS Bedrock, Dynamo & Lambda Functions

涵盖的内容

Redshift as a KnowledgeBase for Structured Data

涵盖的内容

Workflows for Generative AI Apps Using Bedrock Flows

涵盖的内容

位教师

提供方

从 Software Development 浏览更多内容

Building AI Agents with Amazon Bedrock AgentCore

Deploy AI Agents with Amazon Bedrock – Step-by-Step Guide

Generative AI Applications with Amazon Bedrock

Getting Started with Amazon Bedrock

人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

通过 Coursera Plus 开启新生涯

通过在线学位推动您的职业生涯

加入超过 3400 家选择 Coursera for Business 的全球公司

常见问题

What is AI Agent Development with AWS Bedrock? Why is it relevant?

What is this course about?

What will I be able to do after completing this course?

更多问题