AWS Bedrock gives you access to Claude, Llama, and other foundation models through a single API. This course shows you how to use Bedrock alongside S3, Lambda, and AWS Knowledge Bases to build production AI systems.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on aws for ai you can find — even without producing a single minute of custom video.
This course builds AI architectures using Lambda and App Runner — the patterns that scale automatically without managing servers.
AWS Bedrock is AWS's managed foundation model service. This course treats it as the primary AI access layer with direct API comparisons to the model providers.
You'll need an AWS account (free tier is sufficient for most exercises). This course doesn't simulate AWS — it uses the real thing.
Each day is designed to finish in about an hour of focused reading plus hands-on work. No live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
AWS account setup, IAM users and roles, the AWS console, and the principle of least privilege. The security foundation everything else builds on.
Setting up Bedrock access, calling Claude and other foundation models through the Bedrock API, comparing Bedrock to direct API access.
Building event-driven AI pipelines: S3 triggers Lambda, Lambda calls Bedrock, results stored back to S3. The pattern for batch AI processing at any scale.
AWS Knowledge Bases as a managed RAG service. Ingesting documents, configuring vector search, and querying with contextual retrieval.
Packaging a Python AI application, deploying to App Runner, setting environment variables, configuring IAM permissions, and monitoring with CloudWatch.
Instead of shooting our own videos, we link to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Getting started with AWS Bedrock — calling foundation models, configuring access, and building your first Bedrock-powered app.
Building event-driven pipelines with S3 and Lambda — the core serverless pattern for AWS data processing.
How to use AWS Knowledge Bases for managed RAG — ingesting documents, configuring retrieval, and querying with context.
Understanding IAM roles, policies, and the principle of least privilege — the security foundation for all AWS development.
Deploying containerized applications with App Runner — the easiest path to a production deployment on AWS.
Comparing cloud AI services across the three major providers — when to use each and where AWS has competitive advantages.
The best way to go deeper on any topic is to read canonical open-source implementations. These repositories implement the core patterns covered in this course.
Official AWS Bedrock sample code. The canonical reference for every Bedrock use case including RAG, agents, and model evaluation.
The official AWS Python SDK. Every AWS API call in this course uses boto3 — understanding it is essential for AWS AI development.
Comprehensive guide and code samples for building generative AI applications on AWS infrastructure.
Full-stack AI chatbot reference architecture on AWS — the production pattern for deploying Bedrock-powered applications.
You already work with AWS and want to add AI capabilities using Bedrock. This course connects what you know about AWS to the AI-specific services.
You build AI applications and need to host them on AWS. This course gives you the cloud architecture knowledge to deploy at scale.
You design AWS infrastructure and need to understand the AI services available and how to integrate them into enterprise architectures.
The 2-day in-person Precision AI Academy bootcamp covers AWS, cloud AI deployment, and production architecture — hands-on with Bo. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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