Develop Generative AI Applications on AWS

Course Overview

The course focuses on the practical implementation of prompt engineering techniques, responsible AI practices with Amazon Bedrock Guardrails, open source framework integration, and architectural patterns for real-world business applications.

Discover opportunities in Malaysia’s growing digital economy. With billions of dollars invested by global tech giants like AWS, Microsoft, Google, and Oracle, Malaysia is rapidly emerging as a hub for digital innovation. The demand for certified Data and AI professionals has never been higher, making it the perfect time to elevate your career.

Course Objectives

By the end of the training, participants will be able to:

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.
Optimize model responses by applying prompt engineering techniques.
Evaluate generative AI application components.
Implement responsible AI practices to protect generative AI.

Who Should Attend?

This training is ideal for software developers interested in leveraging large language models without fine-tuning.

Pre-requisite

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

Course Outline

Module 1: Exploring Components of Generative AI Applications on AWS

Understanding generative AI concepts
Identifying AWS generative AI stack components
Designing generative AI application components

Module 2: 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

Module 3: Applying Prompt Engineering for Developers

Introducing prompt engineering
Introducing prompt techniques
Optimizing prompts for better results

Module 4: 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

Module 5: Customizing 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

Module 6: 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

Module 7: Evaluating Generative AI Application Components

Evaluating application components
Evaluating model output
Evaluating RAG output
Optimizing latency and cost
Hands-on lab: Evaluating Retrieval Augmented Generation (RAG) Applications

Module 8: 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

Module 9: Using Tools and Agents in Generative AI Applications

Using tools
Understanding AI agents
Understanding open source agentic frameworks
Understanding agent interoperability

Module 10: 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