AI Engineer MLOps Track: Deploy Gen AI & Agentic AI at Scale | Udemy [Update 09/2025]
English | Size: 14.9 GB
Genre: eLearning[/center]

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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