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Developing Generative AI Applications on AWS

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The Developing Generative AI Applications on AWS course introduces participants to building generative AI applications using AWS services like Amazon SageMaker, AWS Lambda, and other AI-driven tools for real-time solutions.

  • 17
    Oct
    2 days, Thu 7:00 AM PDT - Fri 3:00 PM PDT
    Online
    • $1,400.00 incl. Tax
  • 29
    Oct
    2 days, Tue 7:00 AM PDT - Wed 3:00 PM PDT
    Online
    • $1,400.00 incl. Tax
  • 26
    Nov
    2 days, Tue 7:00 AM PST - Wed 3:00 PM PST
    Online
    • $1,400.00 incl. Tax
  • 12
    Dec
    2 days, Thu 7:00 AM PST - Fri 3:00 PM PST
    Online
    • $1,400.00 incl. Tax

Description

This course is designed to introduce generative artificial intelligence (AI) to software developers interested in using large language models (LLMs) without fine-tuning. 

The course provides an overview of generative AI, planning a generative AI project, getting started with Amazon Bedrock, the foundations of prompt engineering, and the architecture patterns to build generative AI applications using Amazon Bedrock and LangChain.

Course 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 foundation models (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 an 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 LLMs, prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents
for Amazon Bedrock
• Describe architecture patterns that you can implement with Amazon Bedrock for building generative AI applications
• Apply the concepts to build and test sample use cases that use the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation
(RAG) approach

Intended Audience

  • Software Developer
  • Systems Architect or Engineer with python experience

 

Prerequisites

  • AWS Technical Essentials
  • Intermediate-level proficiency in Python

Course Outline

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

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

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

4) Foundations of Prompt Engineering

  • Basics of foundation models
  • Fundamentals of prompt engineering
  • Basic prompt techniques
  • Advanced prompt techniques
  • Model-specific prompt techniques
  • Demonstration Finetuning a basic text prompt
  • Addressing prompt misuses
  • Mitigating bias
  • Demonstration Image bias mitigation

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 finetuning
  • Securing generative AI applications
  • Generative AI application architecture

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

7) LangChain

  • Optimizing LLM performance
  • Using models with LangChain
  • Constructing prompts
  • Demonstration Bedrock with LangChain using a prompt that includes context
  • Structuring documents with indexes
  • Storing and retrieving data with memory
  • Using chains to sequence components
  • Managing external resources with LangChain agents

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
  •  Chatbot
  •  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

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