AI in Production: Gen AI and Agentic AI at scale
2025-09-19
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 13.39 GB | Duration: 18h 37m
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:EntrepreneursEnterprise 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 1You'll launch a Next.js SaaS product on Vercel and AWS,with AWS App Runner and Clerk for user management and subscriptions. Week 2You'll become an AI platform engineer on AWS,deploying serverless infrastructure using:Lambda, Bedrock, API Gateway, S3, CloudFront, Route 53Write Infrastructure as Code with TerraformSet up CI/CD pipelines with GitHub Actions- for hands-free deployments and one-click promotions.Week 3You'll gain broad industry skills for GenAI in production:Deploy a Cyber Security Analyst agent with MCP to Azure & GCPStand up SageMaker inferenceBuild data ingest to S3 vectorsDeploy a Researcher Agent using OpenAI OSS models on Bedrock + MCPWeek 4You'll go fully agentic in production:Architect multi-agent systems with:Aurora Serverless, Lambda, SQSJWT-authenticated CloudFront frontendsLangFuse observabilityOverview of AWS Agent CoreBy the end, you'll know how to:Pick the right architectureLock down securityMonitor costsDeliver continuous updatesEverything needed to run scalable, reliable AI apps in production.Course sections (Weeks & Projects)Week 1SaaS App Live in Production with Vercel, AWS, Next.js, Clerk, App RunnerProject: SaaS Healthcare AppWeek 2AI Platform Engineering on AWS with Bedrock, Lambda, API Gateway, Terraform, CI/CDProject: Digital Twin Mk IIWeek 3Gen AI in Production with Azure, GCP, AWS SageMaker, S3 Vectors, MCPProject: Cybersecurity AnalystWeek 4Agentic AI in Production: Build and deploy a Multi-Agent System on AWS (Aurora Serverless, Lambda, SQS),with LangFuse and Bedrock AgentCoreCapstone 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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