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
• Demonstration: Fine-Tuning a Basic Text Prompt
• Model-Specific Prompt Techniques
• Addressing Prompt Misuses
• Mitigating Bias
• Demonstration: Image Bias-Mitigation

Module 5: Amazon Bedrock Application Components

• Applications and Use Cases
• Overview of Generative AI Application Components
• Foundation Models and the FM Interface
• Working with Datasets and Embeddings
• Demonstration: Word Embeddings
• Additional Application Components
• 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
• Demonstration: 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
• Demonstration: Bedrock with LangChain Using a Prompt that Includes Context

Module 8: Architecture Patterns

• Introduction to Architecture Patterns
• Text Summarization
• Demonstration: Text Summarization of Small Files with Anthropic Claude
• Demonstration: Abstractive Text Summarization with Amazon Titan Using LangChain
• Question Answering
• Demonstration: Using Amazon Bedrock for Question Answering
• Chatbots
• Demonstration: Conversational Interface – Chatbot with AI21 LLM
• Code Generation
• Demonstration: Using Amazon Bedrock Models for Code Generation
• LangChain and Agents for Amazon Bedrock
• Demonstration: Integrating Amazon Bedrock Models with LangChain Agents

• Data Scientists
• Machine Learning Engineers
• Software Developers
• Innovation Teams in Enterprises
• IT Professionals

• A foundational grasp of how machine learning algorithms and neural networks function is essential.
• Proficiency in a programming language, with Python being the preferred choice, to implement AI models effectively.
• Experience using AWS services such as S3, EC2, and SageMaker will be beneficial.
• Hands-on experience with deep learning frameworks like TensorFlow or PyTorch is important for working with Generative AI models.
• Familiarity with data processing techniques and ETL (Extract, Transform, Load) tasks is necessary for handling datasets in AI projects.

• Gain a foundational understanding of Generative AI and its applications.
• Learn how to plan and execute Generative AI projects effectively.
• Master the use of Amazon Bedrock for AI model development and deployment.
• Develop skills in prompt engineering, including advanced techniques and bias mitigation.
• Understand the components of Generative AI applications and how to secure them.
• Explore Amazon Bedrock Foundation Models for inference and data protection.
• Integrate AWS tools with LangChain to optimize Large Language Model (LLM) performance.
• Apply architecture patterns for various Generative AI use cases, including text summarization, question answering, chatbots, and code generation.