AI Engineer Production Track: Deploy LLMs & Agents at Scale | Udemy

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AI Engineer Production Track: Deploy LLMs & Agents at Scale | Udemy [Update 06/2026]
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Genre: eLearning

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
  

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.

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