Detailed Course Outline
Day 1
Module 1: Introduction to Generative AI – Art of the Possible
- Overview of ML
- Basics of generative AI
- Generative AI use cases
- Generative AI in practice
- Risks and benefits
Module 2: Planning a Generative AI Project
- Generative AI fundamentals
- Generative AI in practice
- Generative AI context
- Steps in planning a generative AI project
- Risks and mitigation
Module 3: Getting Started with Amazon Bedrock
- Introduction to Amazon Bedrock
- Architecture and use cases
- How to use Amazon Bedrock
- Demonstration: Setting up Bedrock access and using playgrounds
Module 4: Foundations of Prompt Engineering
- Basics of foundation models
- Fundamentals of prompt engineering
- Basic prompt techniques
- Advanced prompt techniques
- Model-specific prompt techniques
- Demonstration: Fine-tuning a basic text prompt
- Addressing prompt misuses
- Mitigating bias
- Demonstration: Image bias mitigation
Day 2
Module 5: Amazon Bedrock Application Components
- Overview of generative AI application components
- Foundation models and the FM interface
- Working with datasets and embeddings
- Demonstration: Word embeddings
- Additional application components
- Retrieval Augmented Generation (RAG)
- Model fine-tuning
- Securing generative AI applications
- Generative AI application architecture
Module 6: Amazon Bedrock Foundation Models
- Introduction to Amazon Bedrock foundation models
- Using Amazon Bedrock FMs for inference
- Amazon Bedrock methods
- Data protection and auditability
- Lab: Invoke Bedrock model for text generation using zero-shot prompt
Module 7: LangChain
- Optimizing LLM performance
- Integrating AWS and LangChain
- Using models with LangChain
- Constructing prompts
- Structuring documents with indexes
- Storing and retrieving data with memory
- Using chains to sequence components
- Managing external resources with LangChain agents
Module 8: Architecture Patterns
- Introduction to architecture patterns
- Text summarization
- Lab: Using Amazon Titan Text Premier to summarize text of small files
- Lab: Summarize long texts with Amazon Titan
- Question answering
- Lab: Using Amazon Bedrock for question answering
- Chatbot
- Lab: Build a chatbot
- Code generation
- Lab: Using Amazon Bedrock models for code generation
- LangChain and agents for Amazon Bedrock
- Lab: Building conversational applications with the Converse API