AI Engineer Production Track: Deploy LLMs & Agents at Scale

Descripción

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

Lo que aprenderás

• 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

Requisitos

• 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.