Description
What you’ll learn
-
Deploy SaaS LLM apps to production on Vercel, AWS, Azure, and GCP, using Clerk
-
Design cloud architectures with Lambda, S3, CloudFront, SQS, Route 53, App Runner and API Gateway
-
Integrate with Amazon Bedrock and SageMaker, and build with GPT-5, Claude 4, OSS, AWS Nova and HuggingFace
-
Rollout to Dev, Test and Prod automatically with Terraform and ship continuously via GitHub Actions
-
Deliver enterprise-grade AI solutions that are scalable, secure, monitored, explainable, observable, and controlled with guardrails.
-
Create Multi-Agent systems and Agentic Loops with Amazon Bedrock AgentCore and Stands Agents
This is the course that more of my students have asked for than any other course — put together.
One student called it:
“The missing course in AI.”
This course is for:
-
Entrepreneurs
-
Enterprise engineers
-
…and everyone in between.
It’s not just about RAG — although we’ll work with RAG.
It’s not just about Agents — but there will be many Agents.
It’s not just about MCP — but yes, there will be plenty of MCP too.
This course is about:
RAG, Agents, MCP, and so much more… deployed to production.
Live.
Enterprise-grade.
Scalable, resilient, secure, monitored — and explained.
You’ll ship real-world, production-grade AI with LLMs and agents across Vercel, AWS, GCP, and Azure, going deepest on AWS.
Across four weeks you’ll take four products to production:
Week 1
You’ll launch a Next.js SaaS product on Vercel and AWS,
with AWS App Runner and Clerk for user management and subscriptions.
Week 2
You’ll become an AI platform engineer on AWS,
deploying serverless infrastructure using:
-
Lambda, Bedrock, API Gateway, S3, CloudFront, Route 53
-
Write Infrastructure as Code with Terraform
-
Set up CI/CD pipelines with GitHub Actions
— for hands-free deployments and one-click promotions.
Week 3
You’ll gain broad industry skills for GenAI in production:
-
Deploy a Cyber Security Analyst agent with MCP to Azure & GCP
-
Stand up SageMaker inference
-
Build data ingest to S3 vectors
-
Deploy a Researcher Agent using OpenAI OSS models on Bedrock + MCP
Week 4
You’ll go fully agentic in production:
-
Architect multi-agent systems with:
-
Aurora Serverless, Lambda, SQS
-
JWT-authenticated CloudFront frontends
-
LangFuse observability
-
Overview of AWS Agent Core
-
By the end, you’ll know how to:
-
Pick the right architecture
-
Lock down security
-
Monitor costs
-
Deliver continuous updates
Everything needed to run scalable, reliable AI apps in production.
Course sections (Weeks & Projects)
Week 1
SaaS App Live in Production with Vercel, AWS, Next.js, Clerk, App Runner
Project: SaaS Healthcare App
Week 2
AI Platform Engineering on AWS with Bedrock, Lambda, API Gateway, Terraform, CI/CD
Project: Digital Twin Mk II
Week 3
Gen AI in Production with Azure, GCP, AWS SageMaker, S3 Vectors, MCP
Project: Cybersecurity Analyst
Week 4
Agentic AI in Production: Build and deploy a Multi-Agent System on AWS (Aurora Serverless, Lambda, SQS),
with LangFuse and Bedrock AgentCore
Capstone Project: SaaS Financial Planner
Who this course is for:
- If you’re excited about the idea of deploying Gen AI and Agents live in production – then this course is for you.





Reviews
There are no reviews yet.