AW330: Developing Generative AI Applications on AWS™

Agenda:

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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 API

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

Target audience:

• Software developers interested in leveraging large language models without fine-tuning

Prerequisites:

To participate in the course AW330 Developing Generative AI Applications on AWS™ at qSkills™, you should have completed the following AWS™ training:

• AW110 AWS™ Technical Essentials

In addition, you should meet the following prerequisites:

•  Intermediate-level proficiency in Python

Description:

Other Info: