[2026] Complete AWS Bedrock Generative AI Course + Projects

[2026] Complete AWS Bedrock Generative AI Course + Projects

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ABOUT THE COURSE:

AWS Bedrock is Amazon Web Services' fully managed platform for building Generative AI applications. It lets you work with powerful foundation models while abstracting away infrastructure complexity. This course will take you from zero to building production-ready AI applications — even if you're completely new to Generative AI.

Projects will integrate AWS Bedrock with API Gateway, AWS Lambda, S3, AgentCore, Postman, and other essential AWS services.

6 Real-World Projects You'll Build

• Meeting Summarization — Feed transcripts into a Bedrock-powered pipeline and get structured summaries with key decisions and action items

• Code Generation — Describe what you need in plain English, get working code back using foundation models on Bedrock

• Image Generation — Generate and control images using diffusion models via AWS Bedrock

• Intelligent Multi-Agent System — A hotel & restaurant recommender where Supervisor, Hotel, and Restaurant agents collaborate to deliver personalized results

• Voice-Powered Hotel Front Desk AI Agent — Real-time voice AI using AWS Bedrock Nova Sonic that listens, thinks, and responds

• Web Search Agent with Memory — A personal assistant built with AWS Strands Agents and Claude that searches the web via DuckDuckGo and remembers users across sessions

What You'll Learn

AWS Bedrock & Generative AI Foundations Understand what AWS Bedrock is, how it fits into the Generative AI ecosystem, and how modern AI applications are built using managed foundation models — explained in a beginner-friendly way.

Large Language Models (LLMs) Learn how LLMs work, what they excel at, and where their limitations lie. Explore real-world use cases including document understanding, chat systems, and code generation using Bedrock-hosted models.

Fine-Tuning & Reinforcement Fine-Tuning (RFT) Go beyond basic prompting. Learn how models can be improved using fine-tuning and RFT with human feedback, when and why to use these techniques, and how they fit into modern AI workflows.

Prompt Management & Optimization Design, manage, version, and optimize prompts for consistent, reliable results. Learn why prompt management becomes critical as applications scale and evolve.

Intelligent Model Routing Discover how to route requests intelligently between different models based on task type, cost, or quality — choosing the right model at the right time.

Batch Inference & Data Automation Process large volumes of data efficiently using batch inference, and automate document and data workflows using AWS Bedrock for scalable AI processing.

Image Generation with Diffusion Models Explore image generation concepts using diffusion models — including how to generate, control, and deploy image-based AI features at scale.

AI Safety, Evaluation & Watermark Detection Evaluate your LLMs for quality, accuracy, and toxicity. Understand AI safety concepts including watermark detection and responsible AI usage.

Serverless Deployment & Scaling Deploy Generative AI applications using a fully serverless stack — AWS Lambda, API Gateway, S3, and Bedrock — and scale AI applications without managing any servers.

AgentCore: From Prototype to Production Take your agents beyond local development. Learn what Amazon Bedrock AgentCore is, deploy agents on AgentCore Runtime, and monitor every tool call and decision with built-in observability — no servers to manage, production-ready from day one.

Agent Memory — Short-Term & Long-Term Give your agents the ability to remember. Implement short-term memory using get_last_k_turns so agents track conversation context within a session. Then add long-term memory with extraction strategies that automatically capture semantic facts, user preferences, and session summaries — so your agents personalize every interaction, even across sessions.

Guardrails & Responsible AI Learn how to use Amazon Bedrock Guardrails to add safety controls, filter harmful content, reduce risk, and enforce responsible AI behavior across your applications.

Model Comparison Compare different foundation models based on quality, speed, cost, capabilities, and use case fit so you can choose the right model for each task.

AWS Model Usage Quotas Understand AWS Bedrock model usage quotas, request limits, throttling, and how quotas affect real-world production applications.

Inference Profiles Learn how inference profiles work in Amazon Bedrock and how they help with scalable, reliable, and cross-region model invocation.

Marketplace Models on AWS Bedrock Explore Amazon Bedrock Marketplace Models and learn how to access additional foundation models beyond the default Bedrock catalog.

Advanced Bedrock Features Go beyond basic prompting and deployment by working with more production-focused AWS Bedrock capabilities used in real AI systems.

By the End of This Course, You Will Be Able To:

• Build and deploy Generative AI applications using AWS Bedrock

• Summarize documents and extract key decisions with AI pipelines

• Generate text, code, and images using foundation models

• Improve model behaviour with fine-tuning and reinforcement fine-tuning

• Manage and optimize prompts at scale

• Route requests intelligently between models

• Run batch inference jobs for large datasets

• Evaluate and monitor LLM outputs for safety and quality

• Deploy scalable, serverless AI systems on AWS

• Build intelligent multi-agent AI systems

• Create real-time voice AI agents using AWS Bedrock Nova Sonic

• Deploy agents to production on AgentCore Runtime

• Add short-term and long-term memory to agents

• Build web search agents with Strands, Claude Haiku, and DuckDuckGo

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