Curso: Developing Generative AI Applications on AWS

Objectives

• Describe generative AI and how it aligns to machine learning.
• Define the importance of generative AI and explain its potential risks and benefits.
• Identify business value from generative AI use cases.
• Discuss the technical foundations and key terminology for generative AI.
• Explain the steps for planning a generative AI project.
• Identify some of the risks and mitigations when using generative AI.
• Understand how Amazon Bedrock works.
• Familiarize yourself with basic concepts of Amazon Bedrock.
• Recognize the benefits of Amazon Bedrock.
• List typical use cases for Amazon Bedrock.
• Describe the typical architecture associated with an Amazon Bedrock solution.
• Understand the cost structure of Amazon Bedrock.
• Implement a demonstration of Amazon Bedrock in the AWS Management Console.
• Define prompt engineering and apply general best practices when interacting with FMs.
• Identify the basic types of prompt techniques, including zero-shot and few-shot learning.
• Apply advanced prompt techniques when necessary for your use case.
• Identify which prompt-techniques are best-suited for specific models.
• Identify potential prompt misuses.
• Analyze potential bias in FM responses and design prompts that mitigate that bias.
• Identify the components of a generative AI application and how to customize a foundation model (FM).
• Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs.
• Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications.
• Describe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock.
• Describe architecture patterns that can be implemented with Amazon Bedrock for building generative AI applications.
• Apply the concepts to build and test sample use cases that leverage the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach.

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 Amazon 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

Day 2

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
• Lab: Invoke Amazon 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
• Chatbots
• 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 AP