AI Engineer Production Track: Deploy LLMs & Agents at Scale
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.
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.
Download Links
12.80 GB Total Size