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Introduction to Generative AI and Amazon Bedrock
Build practical generative AI skills by learning the core concepts, common use cases, and trade-offs of generative AI through practical examples using Amazon Bedrock.
28 Lessons
6h
Updated today
Join 3 million developers at
Join 3 million developers at
LEARNING OBJECTIVES
- Develop a working knowledge of Amazon Bedrock, foundation models, and generative AI inference workflows.
- Build hands-on experience with generative AI applications using prompt engineering, RAG, and fine-tuning techniques.
- Become familiar with Bedrock Agents, Prompt Flows, and multi-agent orchestration for advanced AI application design.
- Gain hands-on experience designing scalable, event-driven generative AI architectures on AWS.
- Understand observability, security, guardrails, and responsible AI practices for enterprise-grade AI systems.
KEY OUTCOMES
Build Production-Grade AI Applications
Develop and deploy enterprise-level generative AI applications using Amazon Bedrock and AWS services, ensuring scalability and reliability.
Optimize Model Performance
Apply advanced inference strategies to manage costs and improve output quality in real-time generative AI applications.
Design Effective Prompts
Craft and refine prompts that maximize the performance of foundation models, enhancing the quality of generated content.
Evaluate AI Model Suitability
Assess and select foundation models based on specific workload requirements, ensuring optimal performance and cost-effectiveness.
Why choose this course?
Transform Your AI Skills Today
In a world where generative AI is reshaping industries, staying relevant is crucial. Developers face the fear of being left behind as AI technologies evolve rapidly.
The Stakes Are High
Without mastering generative AI, developers risk stagnation in their careers. Even skilled engineers can struggle to leverage AI effectively, missing out on opportunities for innovation and advancement.
Your Path to Mastery
This course offers hands-on experience with Amazon Bedrock, guiding you through prompt engineering, model evaluation, and advanced application design. Build scalable AI systems and integrate with AWS services seamlessly.
Elevate Your Career Now
Join a community of forward-thinking developers and position yourself at the forefront of AI innovation. Enroll today and take the next step in your professional journey.
Learning Roadmap
1.
Introduction
Introduction
Explore generative AI’s transformative potential and Amazon Bedrock’s foundational model capabilities.
2.
Prompt Engineering and Model Selection
Prompt Engineering and Model Selection
Master prompt design, model evaluation, and inference optimization for effective AI deployment.
3.
Customizing Models and Knowledge Retrieval
Customizing Models and Knowledge Retrieval
3 Lessons
3 Lessons
Explore strategies for adapting models and building knowledge bases in Amazon Bedrock.
4.
Building AI Agents with Amazon Bedrock
Building AI Agents with Amazon Bedrock
6 Lessons
6 Lessons
Explore the development and management of autonomous AI agents using Amazon Bedrock.
5.
Integrating Bedrock with the AWS Ecosystem
Integrating Bedrock with the AWS Ecosystem
6 Lessons
6 Lessons
Explore data storage, compute integration, orchestration, monitoring, and CI/CD for Amazon Bedrock applications.
6.
Amazon Bedrock AgentCore and Production Agent Pipelines
Amazon Bedrock AgentCore and Production Agent Pipelines
2 Lessons
2 Lessons
Explore Amazon Bedrock's AgentCore for efficient AI agent deployment and management.
7.
Security and Responsible AI in Bedrock
Security and Responsible AI in Bedrock
4 Lessons
4 Lessons
Explore safety mechanisms, data protection, and compliance strategies for generative AI applications.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Complete more lessons to unlock your certificate
Developed by MAANG Engineers
ABOUT THIS COURSE
Generative AI workloads are moving from prototypes into production systems, and Amazon Bedrock is one AWS service that teams use to build enterprise AI applications. Engineers working on production AI systems need to move beyond prompt-level demos and understand how to design and build scalable, reliable, and secure AI systems that integrate with cloud infrastructure, data stores, identity systems, monitoring, and deployment pipelines.
I developed this course from my experience with distributed cloud systems. One pattern I kept seeing was that many developers understood LLM concepts, but struggled to operationalize AI systems in production environments. This course was designed to bridge that gap. You’ll work through prompt engineering, RAG pipelines, fine-tuning, Bedrock Agents, Prompt Flows, multimodal workflows, and multi-agent orchestration. The course also covers integrations with Lambda, OpenSearch, SageMaker, API Gateway, and monitoring tools to build event-driven AI architectures with observability, security, and built-in guardrails.
If you want practical experience building production-grade generative AI systems on AWS, this course is the best place to start.
ABOUT THE AUTHOR
Naeem ul Haq
Educative co-founder and CTO. Ex-Microsoft (Azure). Full-Stack, Cloud, Product & Engineering Leadership.
Trusted by 3 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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