Cloud (AWS / GCP / Azure)

Managed compute, storage, and AI services — where production workloads actually run.

Maturity level: L2 — Can Build

Six perspectives on Cloud (AWS / GCP / Azure)

Roadmap

Learn after Linux and alongside Terraform — most teams hire for AWS or GCP.

Architecture

Hosts clusters, storage, managed AI APIs, and networking for the full stack.

Company

Listed in the vast majority of MLOps, platform, and AI infra job descriptions.

Projects

Run at least one capstone entirely in a cloud account.

Interview

IAM, networking, managed services, and cost awareness.

Career

Bridge from local Docker skills to production deployments.

What & Why

What: Public cloud platforms providing VMs, Kubernetes, object storage, and managed ML/AI APIs.

Why: Job postings expect at least one cloud. MLOps, LLMOps, and FDE roles deploy on AWS, GCP, or Azure daily.

Build this

Deploy a containerized ML API on managed Kubernetes with object storage for artifacts.

Production reality

  • ! IAM misconfiguration
  • ! Runaway bills
  • ! Quota limits
  • ! Region outages
  • ! Over-permissive roles

Interview preparation

  • Design a secure VPC for an ML workload
  • How do you control cloud costs for GPU training?
  • Compare managed K8s offerings

Connected skills

KubernetesDockerTerraformCI/CDAI Security

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