https://github.com/ashutoshrajgupta/gen-ai-applications-using-amazon-bedrock

This repository contains notes and key concepts from the "Generative AI Application using Amazon Bedrock" course provided by AWS Skill Builder. It also includes theoretical overviews of the AWS services used in generative AI application development.
https://github.com/ashutoshrajgupta/gen-ai-applications-using-amazon-bedrock

aws-apigateway aws-bedrock aws-bedrock-agents aws-cloudwatch aws-lambda aws-s3 postman python3

Last synced: 4 months ago
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This repository contains notes and key concepts from the "Generative AI Application using Amazon Bedrock" course provided by AWS Skill Builder. It also includes theoretical overviews of the AWS services used in generative AI application development.

• Host: GitHub
• URL: https://github.com/ashutoshrajgupta/gen-ai-applications-using-amazon-bedrock
• Owner: AshutoshRajGupta
• Created: 2025-06-01T15:25:39.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-06-01T17:53:00.000Z (4 months ago)
• Last Synced: 2025-06-02T03:11:46.501Z (4 months ago)
• Topics: aws-apigateway, aws-bedrock, aws-bedrock-agents, aws-cloudwatch, aws-lambda, aws-s3, postman, python3
• Language: Python
• Homepage:
• Size: 964 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Generative AI Application using Amazon Bedrock

This repository contains notes and key concepts from the **"Generative AI Application using Amazon Bedrock"** course provided by **AWS Skill Builder**. It also includes theoretical overviews of the AWS services used in generative AI application development.

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## 📘 Course Overview

The course introduces the fundamentals of generative AI and how to build applications using **Amazon Bedrock**, a managed service that provides access to foundation models (FMs) from top AI model providers through a unified API.

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## 🧠 What I Learned

- Core concepts of **Generative AI** and its real-world use cases

- How to build GenAI apps using **Amazon Bedrock**

- Working with **Foundation Models (FMs)** for:

  - Text generation

  - Image generation

- Prompt engineering and model evaluation

- Building GenAI workflows with:

  - **Python (Boto3)**

  - **LangChain**

- Deploying applications with:

  - **AWS Lambda**

  - **API Gateway**

  - **Amazon S3 & SageMaker**

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## ☁️ AWS Services – Theory Overview

### 🔹 Amazon Web Services (AWS)

AWS is a cloud platform offering compute power, storage, databases, machine learning, and more. It allows developers to build and deploy applications globally without managing physical servers.

### 🔹 Amazon S3 (Simple Storage Service)

- Object storage service used for storing data like files, images, logs, and backups.

- Supports scalability, high availability, and durability.

- Common use cases: static website hosting, data lake, file uploads.

- In GenAI, S3 is used to store input/output data such as documents, prompts, and results.

### 🔹 AWS Lambda

- Serverless compute service that runs your code in response to events.

- No need to provision or manage servers.

- Supports languages like Python, Node.js, Java, etc.

- Useful in GenAI apps for tasks like triggering model inference, file processing, or data transformation.

### 🔹 Amazon API Gateway

- Fully managed service to create, publish, and secure REST and WebSocket APIs.

- Acts as an interface between front-end clients and back-end services (e.g., Lambda).

- Useful for exposing generative AI functions as public or private APIs.

### 🔹 Amazon Bedrock

- Fully managed service to access foundation models from providers like Anthropic (Claude), Amazon (Titan), Stability AI, Meta (Llama), and others.

- Enables text generation, summarization, Q&A, image generation, and more.

- No need to manage infrastructure or train models.

- Easy integration with other AWS services.

### 🔹 Amazon SageMaker (Optional for advanced users)

- Managed service to build, train, and deploy ML models at scale.

- Useful for custom model fine-tuning or hosting if needed beyond Bedrock's capabilities.

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## 🛠️ Tools & Technologies

- **Amazon Bedrock**

- **Boto3 (AWS SDK for Python)**

- **LangChain (for orchestration and chaining prompts)**

- **Amazon Lambda, S3, API Gateway**

- **CloudWatch (for monitoring Lambda/API calls)**

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## 🎯 Key Takeaways

- Serverless architecture with secure and scalable AI integration

- Access to best-in-class models without needing ML expertise

- Easy deployment of intelligent applications using AWS-native tools

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## ✅ Course Completion

This course was successfully completed on **AWS Skill Builder** as part of my learning journey into Generative AI.

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