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[2026] Complete AWS Bedrock Generative AI Course + Projects
Rating: 4.6 out of 5(6,028 ratings)
15,753 students

[2026] Complete AWS Bedrock Generative AI Course + Projects

Multi Agents, Document Chat, Image Diffusion, Reinforcement Fine-Tuning, Voice Agents, Agent Memory, AgentCore + more
Created byPatrik Szepesi
Last updated 6/2026
English

What you'll learn

  • AWS Bedrock
  • Learn how to Extract Information from Videos, Images, Audio, and Documents with Bedrock Data Automation
  • Create Multi Agentic Workflows on AWS Bedrock
  • Add Long Term + Short Term Memory to your Agents
  • Generative AI
  • Build Serverless Machine Learning applications
  • LLM Guardrails
  • Deploy Your Agents in Production to AWS Bedrock AgentCore
  • How to Use Reinforcement Fine Tuning with AWS Bedrock to Train Your Own Models
  • Fine Tune LLMs in the cloud
  • LLM as Judge
  • Build production ready applications with Aws Serverless stack such as Bedrock, Lambda and Api Gateway and S3
  • Train and Evaluate LLMs with your own custom data
  • Building Production Grade Voice agents on AWS
  • Understand short-term vs long-term agent memory and when to use each
  • AWS Bedrock Pricing
  • Summarise your meeting notes in a matter of seconds
  • Serverless Architecture
  • API Gateway
  • AWS Bedrock 2026 Updates
  • Deploy Multi Agentic Workflows with Streamlit
  • Intelligent Prompt Routing, and How to save Time on Money on your LLM Inferences
  • Image Generation
  • Text Summarisation with NLP
  • Text Creation through NLP
  • Stability AI and diffusion models for image generation
  • Use lifecycle hooks to load and save agent memory automatically
  • Anthropic AI
  • Learn How to Build true Bidirectional Voice Agents on AWS
  • Connect your data in S3 with AWS Bedrock
  • Set up Lambda Layers
  • Evaluate your LLMs with AWS Bedrock Evaluator
  • Prompt Management, Version Control, Prompt Promotion
  • Build a web search agent with AWS Strands and DuckDuckGo
  • How to Write Good Prompts with Bedrock
  • Use Agent Collaborations for tasks
  • Chat with Documents
  • Using AWS Batch Inference to Get Cheaper Inference on your LLM Calls
  • Identify if an Image Was Created with AI
  • Prompt Optimization
  • Create Bedrock Data Automation Blueprints for your Custom Domain
  • Building Interruptible Voice Agents
  • Add long-term memory with semantic and preference strategies
  • Deploy agents to production using Bedrock AgentCore Runtime
  • Implement short-term memory for conversation tracking
  • AWS Strands Framework
  • Model Quotas
  • Inference Profiles

Course content

13 sections137 lectures15h 41m total length
  • Course Overview8:45
  • Course Projects Explained0:30
  • Capstone Project 1 Overview: Building Multi Agentic Workflows10:12
  • Capstone Project 2: Building Interruptible Voice Agents6:56
  • Github Link to all Code and Resources0:04
  • Course Updates0:24

Requirements

  • little Python knowledge
  • AWS account
  • Computer with WIFI
  • willingness to learn

Description

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


Who Is This Course For?

  • Aspiring AI developers who want practical, hands-on skills with AWS

  • Cloud engineers looking to add Generative AI to their toolkit

  • Curious beginners who want to understand and build with modern AI — no prior experience required

Whether you're new to AI or an experienced cloud engineer, this course gives you practical skills, real projects, and a strong foundation for working with AWS Bedrock and modern AI systems.

Join us and explore the future of Generative AI with AWS Bedrock — from prompts to production.

Who this course is for:

  • Anyone(with a little programming knowledge) who wants to learn Generative AI on AWS