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

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

Dozent: Packt - Course Instructors

Bei enthalten

Mehr erfahren

10 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

10 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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

Kompetenzen, die Sie erwerben

Kategorie: Retrieval-Augmented Generation
Kategorie: Cloud Deployment
Kategorie: Tool Calling

Werkzeuge, die Sie lernen werden

Kategorie: Model Deployment
Kategorie: Vector Databases
Kategorie: Agentic Workflows
Kategorie: AI Workflows
Kategorie: AI Orchestration

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Februar 2026

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11 Aufgaben

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In diesem Kurs gibt es 10 Module

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.

Das ist alles enthalten

3 Videos1 Lektüre

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.

Das ist alles enthalten

3 Videos1 Aufgabe

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.

Das ist alles enthalten

2 Videos1 Aufgabe

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.

Das ist alles enthalten

6 Videos1 Aufgabe

6 VideosInsgesamt 42 Minuten

Overview of AWS Bedrock4 Minuten
Lab - Bedrock Console Walkthrough4 Minuten
Lab - Getting Started with Python/Boto3 for Bedrock8 Minuten
Lab - Deploy Streamlit Chatbot as Docker Container to AWS ECS17 Minuten
Lab - Invoke Bedrock's MultiModal LLMs Using Python/Boto3 Library7 Minuten
Assignment - Deploy Image Generation using Stable Diffusion to AWS ECS2 Minuten

1 AufgabeInsgesamt 15 Minuten

Introduction to AWS Bedrock - Assessment15 Minuten

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.

Das ist alles enthalten

7 Videos1 Aufgabe

7 VideosInsgesamt 58 Minuten

Overview of AWS Bedrock KnowledgeBase4 Minuten
Lab - Create Your First KnowledgeBase for Q&A7 Minuten
Lab - Test Your KnowledgeBase Using Web Console2 Minuten
Lab - Invoke KnowledgeBase with Python SDK & Lambda Functions Using API Gateway16 Minuten
Assignment - Setup Electric Vehicle Infrastructure KnowledgeBase2 Minuten
Assignment Solution - Part 1 | Deploy Lambda Functions with API Gateway15 Minuten
Assignment Solution - Part 2 | Deploy Chatbot to AWS ECS11 Minuten

1 AufgabeInsgesamt 15 Minuten

Working with Bedrock KnowledgeBase as Vector Store - Assessment15 Minuten

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.

Das ist alles enthalten

6 Videos1 Aufgabe

6 VideosInsgesamt 49 Minuten

Overview of Bedrock Agents2 Minuten
Use-Case Overview - Product Inventory Assistant for ECommerce Platforms5 Minuten
Lab - Setup DynamoDB and Bedrock KnowledgeBase as Data Sources7 Minuten
Lab - Deploy & Test Lambda Functions to Access DynamoDB13 Minuten
Lab - Deploy Bedrock Agent - Full Implementation18 Minuten
Lab - Invoke Agents Using Python/Boto3 SDK5 Minuten

1 AufgabeInsgesamt 15 Minuten

Getting Started with Bedrock Agents - Assessment15 Minuten

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.

Das ist alles enthalten

7 Videos1 Aufgabe

7 VideosInsgesamt 66 Minuten

Introduction to Multi-Agent Collaboration3 Minuten
Overview of Mortgage Assistant Lab2 Minuten
Lab - Setup First Sub-Agent Part-19 Minuten
Lab - Setup First Sub-Agent Part-213 Minuten
Lab - Setup Your Second Sub-Agent16 Minuten
Lab - Setup Supervisor Agent10 Minuten
Lab - Deploy a Mortgage Assistant Chatbot to AWS ECS12 Minuten

1 AufgabeInsgesamt 15 Minuten

Introduction to Multi-Agent Collaboration Using Bedrock - Assessment15 Minuten

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.

Das ist alles enthalten

3 Videos1 Aufgabe

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.

Das ist alles enthalten

6 Videos1 Aufgabe

6 VideosInsgesamt 30 Minuten

Introduction to Structured Data Source for KnowledgeBase2 Minuten
Lab - Setup Redshift Serverless for Rental Apartment Listings8 Minuten
Lab - Setup KnowledgeBase with Redshift Data Source4 Minuten
Lab - Setup Rental Assistant Agents Using Bedrock5 Minuten
Remember This Before Using Redshift with Bedrock KnowledgeBase1 Minute
Lab - Assignment | Deploy Lambda Functions to Query Redshift for Bedrock Agents9 Minuten

1 AufgabeInsgesamt 15 Minuten

Redshift as a KnowledgeBase for Structured Data - Assessment15 Minuten

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.

Das ist alles enthalten

7 Videos3 Aufgaben

7 VideosInsgesamt 55 Minuten

Introduction to Bedrock Flows2 Minuten
Lab - Build Your First Workflow for User Feedback for Ecommerce Stores15 Minuten
Lab - Integrate Bedrock KnowledgeBase with Flows for Reliable Responses11 Minuten
Introduction to Multi-turn Conversations3 Minuten
Lab - Deploy Agents with Bedrock Flows | Part 14 Minuten
Lab - Deploy Agents with Bedrock Flows | Part 213 Minuten
Lab - Invoke Bedrock Flows Using Python/Boto3 SDK8 Minuten

3 AufgabenInsgesamt 90 Minuten

Full Course Practice Assessment15 Minuten
Workflows for Generative AI Apps Using Bedrock Flows - Assessment15 Minuten
Full Course Assessment60 Minuten

Dozent

Packt - Course Instructors

Packt

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Warum entscheiden sich Menschen für Coursera für ihre Karriere?

Felipe M.

Lernender seit 2018

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Jennifer J.

Lernender seit 2020

„Bei einem spannenden neuen Projekt konnte ich die neuen Kenntnisse und Kompetenzen aus den Kursen direkt bei der Arbeit anwenden.“

Larry W.

Lernender seit 2021

„Wenn mir Kurse zu Themen fehlen, die meine Universität nicht anbietet, ist Coursera mit die beste Alternative.“

Chaitanya A.

„Man lernt nicht nur, um bei der Arbeit besser zu werden. Es geht noch um viel mehr. Bei Coursera kann ich ohne Grenzen lernen.“

Häufig gestellte Fragen

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.

Weitere Fragen

Besuchen Sie die das Hilfe-Center für Kursteilnehmer.

Finanzielle Unterstützung verfügbar,

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

kurs ist nicht verfügbar in Deutsch (Deutschland)

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

Was Sie lernen werden

Kompetenzen, die Sie erwerben

Werkzeuge, die Sie lernen werden

Wichtige Details

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

In diesem Kurs gibt es 10 Module

Course Introduction & Background

Das ist alles enthalten

Fundamental Concepts in LLM-Driven Apps & AI

Das ist alles enthalten

Dependent Software Installation

Das ist alles enthalten

Introduction to AWS Bedrock

Das ist alles enthalten

Working with Bedrock KnowledgeBase as Vector Store

Das ist alles enthalten

Getting Started with Bedrock Agents

Das ist alles enthalten

Introduction to Multi-Agent Collaboration Using Bedrock

Das ist alles enthalten

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

Das ist alles enthalten

Redshift as a KnowledgeBase for Structured Data

Das ist alles enthalten

Workflows for Generative AI Apps Using Bedrock Flows

Das ist alles enthalten

Dozent

von

Mehr von Software Development entdecken

Building AI Agents with Amazon Bedrock AgentCore

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

Generative AI Applications with Amazon Bedrock

AWS Intelligent Applications with Amazon Bedrock

Warum entscheiden sich Menschen für Coursera für ihre Karriere?

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

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Häufig gestellte Fragen

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?

Weitere Fragen