Developing Generative AI Applications on AWS

Do you want generative AI to enhance customer experiences and resolve challenging business issues? Developing Generative AI Applications on AWS teaches you how to create generative AI applications.

During this two-day course, a knowledgeable instructor with experience in Python will walk you through the fundamentals, advantages, and related jargon of generative artificial intelligence as a developer. In order to create generative AI applications using AWS services, you will discover the fundamentals of prompt engineering, how to plan a generative AI project, and more. By the end of the course, you will acquire the knowledge and abilities required to create applications that can answer queries, create and summarize text, and communicate with users via a chatbot interface.

After Completing Developing Generative AI course, students will be able to:

  • 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 casesc
  • 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)v
  • 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

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Key Features of Developing Generative AI Applications on AWS course:

  • Comprehensive Curriculum: The course covers major aspects of Generative AI.

  • Hands-On Labs: Participants will have the opportunity to work on real-world scenarios and projects, gaining practical experience with AWS tools and services.

  • Experienced Instructors: The course is taught by industry experts with years of experience in AWS, ensuring high-quality instruction.

  • Flexible Schedule: The course is offered in both online and in-person formats, with options for full-time or part-time study.

  • Certification: Participants will receive a certificate of completion at the end of the course.

Who can participate in the training?

  • Software developers who would want to use LLM Models without fine tuning.

Prerequisites:

  • Well aware about Python or must have gone through an intermediate course on it.
  • AWS Technical Essentials.

    Course Outline: Download Course Outline

    • Overview of ML
    • Basics of generative AI
    • Generative AI use cases
    • Generative AI in practice
    • Risks and benefits

    • Generative AI fundamentals
    • Generative AI in practice
    • Generative AI context
    • Steps in planning a generative AI project
    • Risks and mitigation

    • Introduction to Amazon Bedrock
    • Architecture and use cases
    • How to use Amazon Bedrock
    • Demonstration: Setting Up Bedrock Access and Using Playgrounds

    • 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

    • 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

    • 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

    • 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

    • 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 Agent

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    Course ID: 20748

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