AI Cloud Engineer Roadmap
Free, self-paced path through AWS, Kubernetes, Terraform, CI/CD, SRE, and a deployed AI agent. Same curriculum beCloudReady runs in its live cohort, every step linked to the exact modules and labs.
Cloud Foundations & AWS Core
Start the AI Cloud Engineer roadmap with AWS fundamentals: VPC networking, EC2, IAM, and RDS — the building blocks every later chapter (containers, Terraform, CI/CD, SRE, AI agents) assumes you already have.
Containers & Orchestration
Containerize an app with Docker and deploy it onto a Kubernetes cluster on EKS — Docker, Helm, and the orchestration layer the rest of the roadmap runs on.
Infrastructure as Code with Terraform
Rebuild your AWS infrastructure as Terraform instead of console clicks — the foundation the CI/CD pipeline in Chapter 4 deploys on every push.
CI/CD & Delivery Pipelines
Wire your Terraform into GitHub Actions so every push plans and every merge applies — a full IaC pipeline with security scanning, not infrastructure run by hand.
SRE & Production Readiness
Instrument what you've built with Grafana and Prometheus, then run a live incident simulation — dashboards, alerting, and the production-readiness habits that separate a demo from a service.
AI, LLMs & Agents
Build and deploy a text-to-SQL RAG agent onto the Kubernetes cluster from earlier chapters — the capstone that ties networking, containers, IaC, CI/CD, and observability together into one AI project.
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