Developing Generative AI Applications on AWS (DGAIA)

• Exploring Components of Generative AI Applications on AWS
  • Understanding generative AI concepts
  • Identifying AWS generative AI stack components
  • Designing generative AI application components
• Programming with Amazon Bedrock
  • Guiding model response generation
  • Using Amazon Bedrock programmatically
  • Hands-on lab: Develop with Amazon Bedrock APIs Hands-on lab: Develop Streaming Patterns with Amazon Bedrock APIs
• Applying Prompt Engineering for Developers
  • Introducing prompt engineering
  • Introducing prompt techniques
  • Optimising prompts for better results
• Using Amazon Bedrock APIs in Common Architectures
  • Implementing architecture patterns with Amazon Bedrock APIs
  • Exploring common use cases
  • Adding conversational memory to extend context
  • Hands-on lab: Develop Conversation Patterns with Amazon Bedrock APIs
• Customising Generative AI Responses with RAG
  • Implementing Retrieval Augmented Generation (RAG)
  • Using Amazon Bedrock Knowledge Bases
  • Hands-on lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases
• Integrating Open Source Frameworks with Amazon Bedrock
  • Invoking a foundation model in Amazon Bedrock using LangChain
  • Using LangChain for context-aware responses
  • Hands-on lab: Develop a Generative AI Application Pattern using Open Source Frameworks and Amazon Bedrock Knowledge bases
• Evaluating Generative AI Application Components
  • Evaluating application components
  • Evaluating model output
  • Evaluating RAG output
  • Optimising latency and cost
  • Hands-on lab: Evaluating Retrieval Augmented Generation (RAG) Applications
• Implementing Responsible AI
  • Understanding responsible AI
  • Mitigating bias and addressing prompt misuses
  • Using Amazon Bedrock Guardrails
  • Hands-on lab: Securing Generative AI Applications Using Bedrock Guardrails
• Using Tools and Agents in Generative AI Applications
  • Using tools
  • Understanding AI agents
  • Understanding open source agentic frameworks
  • Understanding agent interoperability
• Developing Amazon Bedrock Agents
  • Implementing Amazon Bedrock Flows
  • Designing Amazon Bedrock Agents
  • Developing Amazon Bedrock Inline Agents
  • Designing multi-agent collaboration
  • Using Amazon Bedrock AgentCore
  • Hands-on lab: Developing Amazon Bedrock Agents Integrated with Amazon Bedrock Knowledge Bases and Guardrails
• Course Wrap-Up.

In this course, you will learn to do the following:

• Develop generative AI applications using Amazon Bedrock.
• Design architecture patterns of generative AI applications.
• Configure Amazon Bedrock APIs to invoke foundation models (FMs) programmatically.
• Develop agentic AI applications by integrating Amazon Bedrock tools and open source frameworks.
• Build custom solutions with Retrieval Augmented Generation (RAG) and Amazon Bedrock Knowledge Bases.
• Integrate open source SDKs with Amazon Bedrock to build business.
• Optimise model responses by applying prompt engineering techniques.
• Evaluate generative AI application components.
• Implement responsible AI practices to protect generative AI.

We recommend that attendees of this course have:

• Completed the Generative AI Essentials AWS instructor-led course
• Intermediate-level proficiency in Python
• Familiarity with AWS Cloud.