Introduction to Amazon Bedrock

The Introduction to Amazon Bedrock course is designed to help learners build practical skills in developing generative AI applications using Amazon Bedrock. Tailored for cloud engineers, developers, solutions architects, AI practitioners, DevOps professionals, and technology enthusiasts, this course provides a comprehensive introduction to foundation models, prompt engineering, Retrieval-Augmented Generation (RAG), Bedrock Agents, and the broader Amazon Bedrock ecosystem. Through conceptual lessons, guided demonstrations, hands-on labs, practical projects, and real-world implementation scenarios, you'll learn how to build, deploy, secure, and monitor generative AI applications on AWS.

Throughout the course, you'll gain hands-on experience working with Amazon Bedrock, foundation models, Bedrock APIs, Bedrock Knowledge Bases, Bedrock Agents, AWS Lambda, Amazon S3, Amazon Lex, API Gateway, CloudWatch, IAM, and other AWS services commonly used in AI-powered solutions. You'll learn how to interact with foundation models, design effective prompts, implement text generation use cases, build Retrieval-Augmented Generation (RAG) applications, integrate Bedrock with AWS services, apply responsible AI practices, secure AI workloads, and optimize performance and costs. The course culminates in practical application-building exercises and a final project that allows you to apply the concepts learned throughout the course in a realistic business scenario.

Course Modules & Learning Outcomes

Introduction to Amazon Bedrock

Build a strong foundation in Generative AI, Large Language Models (LLMs), and Amazon Bedrock. Learn how Bedrock is architected, explore supported foundation models, and understand how organizations can leverage managed AI services to accelerate application development.

Getting Started with Amazon Bedrock

Learn how to access and interact with Amazon Bedrock using the AWS Management Console, AWS CLI, APIs, and Python with Boto3. You'll explore inference profiles, understand how Bedrock APIs work, and gain practical experience invoking foundation models.

Foundation Models and Prompt Engineering

Understand the capabilities and use cases of various foundation models available through Bedrock. Learn the fundamentals of prompt engineering, prompt design best practices, and techniques for improving model responses and output quality.

Text Generation and Model Interaction

Explore how foundation models process requests through concepts such as tokens, context windows, and inference parameters. Gain hands-on experience generating text, controlling model behavior, and optimizing outputs for different use cases.

Practical Generative AI Applications

Implement common generative AI workloads including text summarization, question answering, sentiment analysis, and code generation. Learn how to evaluate outputs and select appropriate prompting techniques for different business requirements.

Integrating Amazon Bedrock with AWS Services

Learn how to build cloud-native AI solutions by integrating Bedrock with services such as Amazon S3, AWS Lambda, API Gateway, and Amazon Lex. You'll create end-to-end workflows that combine generative AI capabilities with AWS infrastructure.

Building Real-World Applications

Apply your skills by developing a marketing email generation application. You'll design the solution architecture, implement backend logic, connect Bedrock APIs, and build practical AI-powered workflows.

Retrieval-Augmented Generation (RAG) with Bedrock Knowledge Bases

Learn how to extend foundation models with organizational knowledge using embeddings, vector stores, and retrieval workflows. Explore Bedrock Knowledge Bases and build a simple RAG application that delivers more accurate and context-aware responses.

Governance, Responsible AI, and Security

Understand the principles of ethical AI, data privacy, governance, and compliance. Learn how to implement Bedrock Guardrails, content filtering, fallback responses, IAM access controls, encryption, and network security for enterprise AI workloads.

Monitoring, Optimization, and Operations

Explore observability and operational best practices for generative AI applications. Learn how to use CloudWatch, analyze usage and performance metrics, troubleshoot issues, optimize costs, and maintain reliable AI services in production environments.

Bedrock Agents and Advanced Capabilities

Discover how Bedrock Agents enable foundation models to take actions and interact with external systems. You'll gain hands-on experience building agent-based workflows and explore advanced topics such as multimodal AI, conversational AI, release management strategies, and Bedrock AgentCore.

Final Project

Bring together the concepts learned throughout the course by designing and implementing a complete real-world generative AI application using Amazon Bedrock and AWS services.

Course Features

• Hands-on labs and guided demonstrations using the AWS Console, CLI, APIs, and Python to build real-world AI solutions.
• Practical projects including a marketing email generation application and a comprehensive final project.
• Comprehensive introduction to Amazon Bedrock, foundation models, and generative AI application development on AWS.
• Practical coverage of prompt engineering, text generation, summarization, question answering, sentiment analysis, and code generation.
• Production-focused best practices covering security, governance, responsible AI, monitoring, cost optimization, Bedrock Agents, and advanced AI architectures.

Who Should Enroll?

• Developers looking to build generative AI applications using Amazon Bedrock.
• Cloud engineers and solutions architects interested in AI-powered application development on AWS.
• AI practitioners seeking hands-on experience with foundation models and prompt engineering.
• DevOps and platform engineers supporting AI workloads in cloud environments.
• Technology professionals interested in Retrieval-Augmented Generation (RAG), Bedrock Agents, and modern AI application architectures.
• Anyone looking to gain practical experience building, deploying, securing, and monitoring generative AI solutions on AWS.

Build the practical skills needed to develop, integrate, secure, and operate generative AI applications using Amazon Bedrock while gaining hands-on experience with foundation models, prompt engineering, RAG architectures, Bedrock Agents, and production-ready AWS AI workflows.