Free Download Agentic AI, Generative AI & LLM Aps in Production
Published 9/2025
Created by LLM Engineer,Ed Donner
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 30 Lectures ( 4h 27m ) | Size: 3.5 GB
Deploy Production-Grade LLMs and Agents on Vercel, AWS, GCP and Azure
What you'll learn
Deploy OpenAI LLM apps to production on Vercel, AWS, Azure, and GCP.
Design SaaS architectures with IAM, EC2, S3, CloudFront, Lambda, Route 53, ECS, and App Runner.
Build AI platforms on AWS Bedrock and SageMaker with secure API Gateway endpoints.
Automate infrastructure with Terraform and ship continuously via GitHub Actions.
Implement multi-cloud AI engineering, including Azure and GCP deployments.
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, but it's totally your choice. You can complete the entire course with no API spend. If you do wish to use frontier models, the typical spend would be under $5. You can choose to access more capabilities if you're comfortable spending a little more.
Description
Ship real, production-grade AI with OpenAI-powered LLMs and agents across AWS, GCP, and Azure, deploying customer-facing apps on Vercel.Across four weeks you'll take three products to production.In Week 1 you'll launch a Next.js SaaS on Vercel and AWS with IAM, EC2, S3, CloudFront, Lambda, Route 53, ECS, Beanstalk, and AWS App Runner.Week 2 turns you into an AI platform engineer on AWS Bedrock and SageMaker, wiring API Gateway, S3, CloudFront, Terraform, and CI/CD with GitHub Actions.Week 3 expands to multi-cloud AI engineering: deploy to Azure and GCP, stand up SageMaker pipelines, build data ingest to S3 vectors, and ship a Researcher Agent using OpenAI OSS models on Bedrock with MCP.Week 4 is pure agentic AI in production: architect multi-agent systems with Aurora Serverless, Lambda, SQS, hardened auth front-ends on CloudFront (JWT), deep observability via LangFuse, and an overview of AWS Agent Core and Google ADK.By the end you'll know how to pick the right architecture, lock down security, monitor costs, and continuously deliver updates-everything needed to run reliable, scalable AI apps in production.Course sections (Weeks & projects)Week 1Current title: SaaS application live on Vercel and AWSOption #1: SaaS on Vercel & AWS: Next.js, IAM, S3, EC2, CloudFrontOption #2: Build and launch SaaS application on Vercel & AWS using Next.js, IAM, S3, EC2, CloudFrontProject: SaaS Healthcare AppWeek 2Current title: AI Platform Engineering on AWSOption #1: AWS Platform: Bedrock, Lambda, API Gateway, Terraform, CI/CDOption #2: AWS Platform Engineering using Bedrock, Lambda, API Gateway, Terraform, CI/CDProject: Digital Twin Mk IIWeek 3Current title: AI Engineering and Data Ingest; AWS, Azure and GCPOption #1: AI Engineering on Azure, GCP & AWS: SageMaker, S3 Vectors, MCPOption #2: AI Engineering across Clouds: AWS, Azure and GCPProject: Cybersecurity Analyst & Continuous Research AgentWeek 4Current Title: Agentic AI in ProductionOption #1: Agentic AI: Build and Deploy a Multi-Agent on AWS (Aurora Serverless, Lambda, SQS) + LangFuseOption #2: Agentic AI in Production using Aurora Serverless, Lambda, SQS + LangFuseProject: SaaS Financial Planner - portfolio analysis expert
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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