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

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1 - Introduction

1 -Introduction.mp4

01:19

2 - Connect With Me!!.html

2 - Code & Playground - Explore & Relate

1 -Introduction to Bedrock.mp4

04:10

2 -PartyRock - Experience Bedrock like ChatGPT.mp4

12:48

3 -Understanding UI.mp4

07:24

4 -Enabling Model Access.mp4

04:21

5 -Post Model Access.mp4

01:15

6 -Can Amazon Q really help me in coding - HandsOn.mp4

08:09

7 -Using Chat Playground.mp4

05:34

8 -Using Text Playground.mp4

03:15

9 -Using Image Playground.mp4

03:02

3 - Amazon Q Developer in Lambda

1 -Understanding Q Developer.mp4

03:48

2 -Functionality of Q Developer in Industry.mp4

03:53

3 -Chatting with Amazon Q.mp4

04:03

4 -Can we ask for code directly in Lambda.mp4

09:35

4 - Phase 1 - Using Code with different Models for PlayGround

1 -How to use Amazon Q in Lambda with Bedrock.mp4

03:01

2 -Bedrock with Text Model - Amazon Titan HandsOn.mp4

09:34

3 -Bedrock with Text Model - Claude Sonnet HandsOn.mp4

10:06

4 -Bedrock with Text Model - Llama HandsOn.mp4

08:10

5 -How to troubleshoot errors if occurred in code.mp4

06:59

6 -Bedrock with Image Model - Titan Image HandsOn.mp4

19:04

7 -Bedrock with Image Model - Stability.ai.mp4

08:09

5 - Foundation Concepts - Theoretical

1 -What is AI.mp4

05:59

2 -What is Generative AI.mp4

04:03

3 -Opportunities available in GenAI.mp4

04:09

4 -AI, GenAI, ML, Deep Learning Relation.mp4

05:20

5 -FM vs LLM.mp4

04:18

6 -Understanding Prompt Engineering.mp4

04:10

7 -Types of Prompt Engineering.mp4

02:14

6 - Phase 2 - Configurations - Inference Parameters

1 -Understanding Inference Parameters.mp4

08:06

2 -Temperature Hands-On.mp4

06:02

3 -Temperature Code With Q developer - I HandsOn.mp4

08:31

4 -Temperature Code With Q developer - II HandsOn.mp4

06:30

5 -Top P Hands-On.mp4

04:59

6 -Top P Code With Q developer - I HandsOn.mp4

08:40

7 -Top P Code With Q developer - II HandsOn.mp4

09:00

8 -Top K Hands-On.mp4

04:59

9 -Top K Code With Q developer - I HandsOn.mp4

08:18

10 -Top K Code With Q developer - II HandsOn.mp4

05:52

11 -Most Probable Solution Hands-On.mp4

06:13

12 -Troubleshooting Scenario- Access Denied Error.mp4

04:07

13 -Troubleshooting Scenario- Timeout Error.mp4

06:13

14 -Troubleshooting Scenario- Validation Exception.mp4

07:08

15 -Troubleshooting Scenario- ModelAccess Error.mp4

07:38

7 - Phase 3 - Additional Configurations

1 -Additional Configurations.mp4

06:10

2 -System Prompt.mp4

04:22

3 -Code for System Prompt with Amazon Q Dev.mp4

08:35

4 -Maximum Length.mp4

02:43

5 -Code for Maximum Length with Amazon Q Dev.mp4

09:22

6 -Stop Sequence.mp4

03:24

7 -Code for Stop Sequence with Amazon Q Dev.mp4

07:41

8 - Bedrock in Console - Safeguards

1 -Understanding Safeguards.mp4

04:55

2 -Guardrails SetUp.mp4

07:53

3 -Using Guardrails.mp4

02:54

4 -Watermark Detection.mp4

03:32

9 - Bedrock Builder Tools - Console

1 -Understanding Builder Tools.mp4

04:32

2 -Prompt Management.mp4

07:08

3 -Knowledge Base - Using Root User.mp4

06:57

4 -IAM Users & Roles.mp4

05:55

5 -IAM Policy.mp4

03:21

6 -Knowledge Base - Using IAM User.mp4

06:57

7 -Understanding Agents.mp4

03:54

8 -Agents Walkthrough.mp4

03:56

9 -Creating Agents using Assistant.mp4

08:43

10 -Defining Agents Manually.mp4

07:37

11 -Adding Action Groups & Guardrails in Agents.mp4

05:22

12 -Understanding Prompt Flow.mp4

05:50

13 -Prompt Flow with KB.mp4

05:01

14 -PromptFlow with Prompt Management.mp4

03:18

15 -PromptFlow with Prompt Creation & KB.mp4

07:42

16 -PromptFlow with Condition.mp4

05:02

Description

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

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

Who is this 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

What You Need to Know?

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.

More details

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

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