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Amazon Bedrock with Amazon Q Developer-Zero to Hero | Python

Name: Amazon Bedrock with Amazon Q Developer-Zero to Hero | Python
Rating: 4.9 (139 reviews)

Code Amazon Bedrock with Amazon Q Developer in Lambda using Python (Boto3). Use Q as Co-Pilot to code. Novice to Expert

Created byKaran Gupta

Last updated 5/2025

English

What you'll learn

Master the fundamentals of generative AI and its business applications
Navigate the Amazon Bedrock console with confidence
Master Amazon Q Developer setup and integration
Create custom images with AI using simple text prompts
Optimize AWS service utilization with Amazon Q
Develop effective code generation skills using AI
Optimize code performance with Amazon Q insights
Improve collaborative development workflows
Implement responsible AI practices and ethical considerations
Complete hands-on, real-world AI projects using Amazon Bedrock

Course content

10 sections • 71 lectures • 7h 2m total length

Introduction1:19

Introduction to Bedrock4:10
PartyRock - Experience Bedrock like ChatGPT12:48
Understanding Bedrock UI7:24
Enabling Model Access4:21
Post Model Access1:15
Explore post model access in AWS Bedrock after authorization. Some models, like Anthropic, Llama, and Meta, require AWS team approval, while normal use uses available models.
Can Amazon Q really help me in coding? - HandsOn8:09
Using Chat Playground5:34
Using Text Playground3:15
Using Image Playground3:02

Understanding Q Developer3:48
Functionality of Q Developer in Industry3:53
Discover how Amazon Q developer serves as a co-pilot for building AWS projects, diagnosing console errors, transforming code across Java versions, and integrating with IDEs and chats.
Chatting with Amazon Q4:03
Can we ask for code directly in Lambda?9:35
Demonstrate invoking a bedrock model with Python in Lambda using Boto3, comparing code whisperer and Amazon Q developer workflows to generate a calculator function and handle errors.

How to use Amazon Q in Lambda with Bedrock?3:01
Bedrock with Text Model - Amazon Titan HandsOn9:34
Configure a Lambda function using Boto3 to invoke Amazon Bedrock Titan for text prompts, grant Bedrock access, and test end-to-end text generation via the Bedrock runtime.
Bedrock with Text Model - Claude Sonnet HandsOn10:06
Bedrock with Text Model - Llama HandsOn8:10
How to troubleshoot error if occurred in code?6:59
Bedrock with Image Model - Titan Image HandsOn19:04
Bedrock with Image Model - Stability.Ai8:09

Understanding Inference Parameters8:06
Explore how inference parameters control the randomness, diversity, and predictability of text produced by foundational models, and learn how temperature, top p (nuclear sampling), and top k sampling shape outputs.
Temperature Hands-On6:02
Temperature Code With Q developer - I HandsOn8:31
Learn to configure temperature in Amazon Titan Bedrock using a Python lambda function, adjusting 0–1 to control randomness and test prompts.
Temperature Code With Q developer - II HandsOn6:30
Top P Hands-On4:59
Top P Code With Q developer - I HandsOn8:40
Explore how to configure top-p in a llama bedrock setup using Python and Boto3, test prompts, and deploy a function to generate controlled responses.
Top P Code With Q developer - II HandsOn9:00
Top K Hands-On4:59
Top K Code With Q developer - I HandsOn8:18
Top K Code With Q developer - II HandsOn5:52
Most Probable Solution Hands-On6:13
Learn to compute the most probable answer by tuning temperature, top p, and top k across Bedrock, Mistral AI, and cloud models, with hands-on AWS Lambda integration.
Troubleshooting Scenario- Access Denied Error4:07
Troubleshooting Scenario- Timeout Error6:13
Troubleshooting Scenario- Validation Exception7:08
Troubleshooting Scenario- ModelAccess Error7:38

Understanding Builder Tools4:32
Explore builder tools like prompt management, agents, knowledge base, and prompt flow to configure foundation models for specific use cases.
Prompt Management7:08
Knowledge Base - Using Root User6:57
IAM Users & Roles5:55
IAM Policy3:21
Knowledge Base - Using IAM User6:57
Understanding Agents3:54
Agents Walkthrough3:56
Creating Agents using Assistant8:43
Defining Agents Manually7:37
Adding Action Groups & Guardrails in Agents5:22
Learn to add action groups and guardrails to a manual agent, configure user input for leaves with date and days parameters, and manage safety, memory, and model options.
Understanding Prompt Flow5:50
Prompt Flow with KB5:01
PromptFlow with Prompt Management3:18
PromptFlow with Prompt Creation & KB7:42
PromptFlow with Condition5:02
Build a condition flow in PromptFlow using the condition builder to test if the input cloud is AWS, and respond with AWS supported or other clouds not supported.

Requirements

Basic Understanding of Coding - Python is required
From Basics to Details: We start from the fundamentals of AI/ GenAI/ AWS / Amazon Q, explaining core concepts and terminology in a clear and concise manner.
An AWS account
Basic understanding of cloud computing concepts (helpful but not mandatory)
Enthusiasm to learn about AI and its practical applications
Hands-On Learning: Hands-on labs and exercises are provided throughout the course to reinforce learning and allow you to practice what you've learned in a real-world environment.

Description

Master Generative AI Development with Amazon Bedrock & Amazon Q

Course Overview

Dive into the cutting-edge world of generative AI development using Amazon's latest tools - Amazon Bedrock and Amazon Q. This comprehensive course will teach you how to build, deploy, and optimize AI-powered applications using Amazon's most advanced AI services.

What You'll Learn

Set up and configure Amazon Bedrock for AI model deployment
Integrate foundation models like Claude, Llama 2, and Amazon Titan
Develop with Amazon Q's AI-assisted coding capabilities
Build production-ready applications using AWS AI services
Implement best practices for prompt engineering and AI safety
Create scalable and cost-effective AI solutions

Course Content

Section 1: Getting Started with Amazon Bedrock

Introduction to Amazon Bedrock architecture
Setting up your development environment
Understanding foundation models and their capabilities
API integration and authentication

Section 2: Building with Foundation Models

Text generation and completion
Image generation and manipulation
Code generation and optimization
Fine-tuning models for specific use cases

Section 3: Amazon Q Developer Experience

AI-assisted code development
Code review and optimization
Documentation generation
Security best practices implementation

Section 4: Inference Parameters Code with Q for Bedrock

Building a code with AI assistant
Creating an AI-powered content generator
Developing an image generation application
Implementing a code refactoring system

Section 5: Additional Configuration for Models

System Prompts
Max Length
Stop Sequence
Guardrails and Builder Tools

Prerequisites

Basic understanding of Python programming
Familiarity with AWS services
AWS account with appropriate permissions

Who This Course is For

Software developers looking to integrate AI into their applications
Cloud engineers wanting to expand their AWS AI expertise
Technical leads evaluating AI solutions for their organizations
DevOps engineers interested in AI infrastructure

Who this course is for:

For Beginners: Individuals with little to no experience in AI/ GenAI/ AWS / Amazon Q. Fresh graduates or students looking to kickstart their careers in AI/ GenAI/ AWS / Amazon Q or software development.
Everyone can pick up this course, at their own pace.
For Intermediate Users: Professionals who have some familiarity with AI but want to deepen their understanding and skills. Developers or sysadmins who have worked with AI in basic capacities but seek to expand their knowledge and capabilities.
For Advanced Users: Experienced AI engineers, software architects, or team leads who want to refine their AI skills and stay updated with the latest best practices.
For Career Changers: Individuals transitioning from other IT roles (such as system administration, software development, or quality assurance) to AI / GenAI / Amazon Q

Amazon Bedrock with Amazon Q Developer-Zero to Hero | Python

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 1min

Code & Playground - Explore & Relate with it9 lectures • 50min

Amazon Q Developer in Lambda4 lectures • 21min

Phase 1 - Using Code with different Models for PlayGround7 lectures • 1hr 5min

Foundation Concepts - Theoretical7 lectures • 30min

Phase 2 - Configurations - Inference Parameters15 lectures • 1hr 42min

Phase 3 - Additional Configurations7 lectures • 42min

Bedrock in Console - Safeguards4 lectures • 19min

Bedrock Builder Tools - Console16 lectures • 1hr 31min

Bonus1 lecture • 1min

Requirements

Description

Who this course is for: