AWS-GENAI-APP

Developing Generative AI Applications on AWS Training

This course introduces generative artificial intelligence (Gen AI) to software developers interested in using large language models (LLMs) without fine-tuning. After an overview of AI, attendees learn how to plan an AI project, work with Amazon Bedrock, and apply prompt engineering best practices.  In addition, students understand the architecture patterns used to build AI applications with Amazon Bedrock and LangChain.

Course Details

Duration

2 days

Prerequisites

  • Completed "AWS Technical Essentials"
  • Intermediate-level proficiency in Python

Target Audience

  • Software developers interested in using LLMs without fine-tuning

Skills Gained

  • 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
Course Outline
  • 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
  • Foundations of Prompt Engineering
    • Basics of foundation models
    • Fundamentals of prompt engineering
    • Basic prompt techniques
    • Advanced prompt techniques
    • Model-specific prompt techniques
    • Addressing prompt misuses
    • Mitigating bias
  • 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
  • Amazon Bedrock Foundation Models
    • Introduction to Amazon Bedrock foundation models
    • Using Amazon Bedrock FMs for inference
    • Amazon Bedrock methods
    • Data protection and auditability
  • LangChain
    • Optimizing LLM performance
    • 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
    • Question answering
    • Chatbot
    • Code generation
    • LangChain and agents for Amazon Bedroc
Upcoming Course Dates
USD $1,300
Online Virtual Class
Scheduled
Date: May 15 - 16, 2024
Time: 9 AM - 5 PM ET
Partner Registration

The course you are registering for is being delivered by our sister company - ExitCertified. All logistics related to course delivery will be managed by the ExitCertified team. If you have a dedicated Web Age representative, please feel to reach out to them with any questions/concerns you may have.

You'll now be redirected to https://www.exitcertified.com to complete the enrollment process.

USD $1,300
Online Virtual Class
Scheduled
Date: Jun 12 - May 16, 2024
Time: 9 AM - 5 PM ET
Partner Registration

The course you are registering for is being delivered by our sister company - ExitCertified. All logistics related to course delivery will be managed by the ExitCertified team. If you have a dedicated Web Age representative, please feel to reach out to them with any questions/concerns you may have.

You'll now be redirected to https://www.exitcertified.com to complete the enrollment process.