Building Generative AI Applications using AWS Cloud 🚀

Introduction

• Presenter: Krishak
• Platform: YouTube channel
• Course: Crash course on building generative AI applications using AWS Cloud

Course Overview

• AWS Services: Various AWS services and how to use them to build generative AI applications
• AWS Bedrock Services: Focus on foundation models available as APIs in AWS Bedrock, abstraction from inferencing
• End-to-End Applications: Building generative applications using LangChain & deploying them
• Hugging Face with AWS SageMaker: Usage and deployment of LLM models with Hugging Face in SageMaker

Course Targets

• Light Target: 2,000 likes
• Comment Target: 200 comments

Gen AI Project Life Cycle

Steps Involved

1. Defining the Use Case: Identify the specific use case (e.g., RAG application, text summarization, chatbot).
2. Choosing the Right Model:
   • Foundation Models: Such as OpenAI’s models, LLaMA 2/3, Google Genie Pro.
   • Custom LLM: Build LLM from scratch if required.
3. Core Tasks:
   • Prompt Engineering
   • Fine-Tuning
   • Training with Human Feedback
4. Evaluation: Evaluating model performance, using appropriate metrics
5. Deployment & Integration:
   • Optimize & Deploy Models: Ensure efficient inferencing
   • Application Integration: Integrate model output with applications
   • LLMOps Platforms: Manage deployments effectively (e.g., inferencing techniques, LPUs)

Practical Implementation

Example Use Case: Blog Generation

Architecture Overview:

1. API Creation:
   • Use Amazon API Gateway to create API
   • Use Postman to hit the API with user queries
2. AWS Lambda Function:
   • Triggers upon API request
   • Interacts with Amazon Bedrock (foundation models)
3. Amazon Bedrock:
   • Provides foundation models (e.g., LLaMA, Claude, etc.)
   • Processes API query and generates blog content
4. Saving Generated Blogs:
   • Save output in AWS S3 as text or PDF files

Lambda and API Gateway Setup

• Lambda: AWS Lambda function to handle API requests and invoke Bedrock models
• API Gateway: Integrate Lambda with API Gateway for handling HTTP requests
• S3 Bucket: Storing generated content, setting appropriate permissions

Example Use Case: Deploying Hugging Face Models on AWS SageMaker

• Setup: Use AWS SageMaker to deploy Hugging Face models (e.g., DistilBERT for Q&A)
• Guidance:
  • Create and configure SageMaker domains
  • Deploy models and manage endpoints

Advanced Topics

Document Q&A Application with AWS Bedrock & LangChain

• Setup: Infrastructure for a document Q&A system with Rag setup, using multiple models (e.g., Claude, LLaMA)
• Data Injection: Load and parse PDF documents
• Vector Embedding & Store: Use Amazon Titan for embeddings, store in Faiss or Chroma DB
• LLM Integration: Create prompts, call LLM models, retrieve and process text data
• Streamlit: UI development using Streamlit to interact with the system

Productivity Tool: Amazon CodeWhisperer

• Functionality: AI code assistant similar to GitHub Copilot, but tailored for AWS services
• Setup: Installation and configuration in VS Code
• Comparison with GitHub Copilot:
  • GitHub Copilot: General-purpose code suggestions
  • Amazon CodeWhisperer: Optimized for AWS-related development

Conclusion

• Resources: Full code examples and additional resources available in specified links
• Tips: Follow the playlist for comprehensive understanding, keep track of AWS-related costs, and understand specific tool usage for better productivity and project management.