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
Explore Cloud (AWS / GCP / Azure) in the interactive universe or train with live cohorts.
Want this Cloud (AWS / GCP / Azure) learning path turned into your own plan?
A roadmap shows the destination. In 1:1 mentorship I turn it into a week-by-week learning path for your background, review your projects, and prepare you for interviews until you land the role. Online, in your time zone.
Start with one 60-minute 1:1 call: .
Prefer a structured live course? Join the MLOps · AIOps · LLMOps · Agentic AI masterclass →