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Starts 18 June 2025 10:35

Ends 18 June 2025

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Building Generative AI Applications Using Amazon Bedrock (Includes Labs)

Master building generative AI applications with Amazon Bedrock APIs and AWS-LangChain integration. Implement RAG applications, AI Assistants, and custom safeguards while exploring architecture patterns for text generation and question answering.
Amazon Web Services via AWS Skill Builder

Amazon Web Services

479 Courses


9 hours

Optional upgrade avallable

Advanced

Progress at your own speed

Paid Course

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Overview

This course is designed for application developers interested in building generative artificial intelligence (generative AI) applications using either the Amazon Bedrock APIs or AWS-LangChain integration. In this course, you will explore the architecture patterns and implementations to support generative AI use cases such as generating and summarizing text, retrieval augmented generation (RAG), and question answering.

Syllabus

  • Introduction to Generative AI with Amazon Bedrock
  • Overview of generative AI concepts
    Introduction to Amazon Bedrock
  • Understanding Amazon Bedrock APIs
  • API functionalities and capabilities
    Authentication and setup for API access
  • AWS-LangChain Integration
  • Overview of LangChain framework
    Setup and configuration of AWS-LangChain integration
  • Architecture Patterns for Generative AI Applications
  • Common architecture patterns
    Best practices for scalability and performance
  • Implementing Text Generation
  • Techniques for text generation
    Lab: Creating a text generation application using Amazon Bedrock
  • Implementing Text Summarization
  • Approaches to text summarization with AI
    Lab: Building a text summarization tool with AWS-LangChain
  • Retrieval Augmented Generation (RAG)
  • Understanding RAG and its applications
    Lab: Developing a RAG system using Amazon Bedrock and AWS-LangChain
  • Building Question Answering Systems
  • Strategies for effective question answering
    Lab: Creating a question answering application with integrated features
  • Deployment and Maintenance
  • Deploying generative AI applications on AWS
    Continuous monitoring and optimization
  • Real-World Use Cases and Case Studies
  • Examining real-world applications of generative AI
    Lessons learned from successful implementations
  • Course Summary and Future Exploration
  • Key takeaways and milestones
    Next steps and additional resources in generative AI development
  • Final Project
  • Design and implement a comprehensive generative AI application
    Presentation and peer review of the final project

Subjects

Programming