Deploy AI to AWS, GCP, Azure, Vercel with MLOps, Bedrock, SageMaker, RAG, Agents, MCP: scalable, secure and observable..

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

Requirements
While it's ideal if you can code in Python and have some experience working with LLMs, this course is designed for a very wide audience, regardless of background. I've included a whole folder of self-study labs that cover foundational technical and programming skills. If you're new to coding, there's only one requirement: plenty of patience!
The course runs best if you have a small budget for APIs and Cloud Providers of a few dollars. But we monitor expenses at every point, and it's always a personal choice.

Description
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
Well, perhaps I'm biased, but I'd say: anyone and everyone! If you're fascinated in the potential of Agents and hungry to have the skills to create powerful Agentic AI – then you've come to the right place. While it's most suited to those with programming experience, I've designed the course to work for all backgrounds.

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Happy Learning!!