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

Instructor: Packt - Course Instructors

Included with

Learn more

10 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

10 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

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There are 10 modules in this course

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.

What's included

3 videos1 reading

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.

What's included

3 videos1 assignment

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.

What's included

2 videos1 assignment

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.

What's included

6 videos1 assignment

6 videos Total 42 minutes

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

1 assignment Total 15 minutes

Introduction to AWS Bedrock - Assessment 15 minutes

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.

What's included

7 videos1 assignment

7 videos Total 58 minutes

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

1 assignment Total 15 minutes

Working with Bedrock KnowledgeBase as Vector Store - Assessment 15 minutes

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.

What's included

6 videos1 assignment

6 videos Total 49 minutes

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

1 assignment Total 15 minutes

Getting Started with Bedrock Agents - Assessment 15 minutes

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.

What's included

7 videos1 assignment

7 videos Total 66 minutes

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

1 assignment Total 15 minutes

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

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.

What's included

3 videos1 assignment

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.

What's included

6 videos1 assignment

6 videos Total 30 minutes

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

1 assignment Total 15 minutes

Redshift as a KnowledgeBase for Structured Data - Assessment 15 minutes

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.

What's included

7 videos3 assignments

7 videos Total 55 minutes

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

3 assignments Total 90 minutes

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

Instructor

Packt - Course Instructors

Packt

1,395 Courses 375,627 learners

Offered by

Packt

Why people choose Coursera for their career

Felipe M.

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Frequently asked questions

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

What you'll learn

Skills you'll gain

Details to know

See how employees at top companies are mastering in-demand skills

There are 10 modules in this course

Course Introduction & Background

What's included

Fundamental Concepts in LLM-Driven Apps & AI

What's included

Dependent Software Installation

What's included

Introduction to AWS Bedrock

What's included

Working with Bedrock KnowledgeBase as Vector Store

What's included

Getting Started with Bedrock Agents

What's included

Introduction to Multi-Agent Collaboration Using Bedrock

What's included

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

What's included

Redshift as a KnowledgeBase for Structured Data

What's included

Workflows for Generative AI Apps Using Bedrock Flows

What's included

Instructor

Offered by

Why people choose Coursera for their career

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

Open new doors with Coursera Plus

Advance your career with an online degree

Join over 3,400 global companies that choose Coursera for Business

Frequently asked questions

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?

More questions