Cloud

4 modules are currently being reworked. Watch this section over the next few days.

Master the hyperscaler that actually runs your Kubernetes clusters.

Kubernetes doesn’t exist in a vacuum. It runs on AWS, GCP, or Azure — and you need to know the cloud platform underneath. This track takes you from cloud fundamentals to managed Kubernetes operations and then into the architecture and operating-model decisions that show up in real production environments.

Learning Path

Rosetta Stone (cross-provider concepts)
        │
        ├─── AWS ──────────────────────────┐
        │    Essentials (12) → EKS (5)     │
        │                                  │
        ├─── Google Cloud ─────────────────┤
        │    Essentials (12) → GKE (5)     │
        │                                  │
        └─── Azure ────────────────────────┤
             Essentials (12) → AKS (4)     │
                                           │
                    ┌──────────────────────-┘
                    ▼
        Architecture & Enterprise
        ├── Architecture Patterns (4)
        ├── Advanced Operations (10)
        ├── Managed Services (10)
        └── Enterprise & Hybrid (10)

Pick one provider deeply first — you do not need all three. Learn the essentials for the cloud you actually use, then go deep on its managed Kubernetes offering before you spend time comparing clouds.

Start Here If

you already understand basic Kubernetes workloads and want the provider context around them
your next questions are about VPCs, IAM, managed control planes, or production cloud networking
you need one real cloud path rather than vague multi-cloud familiarity

Do Not Start Here First If

you still need terminal, Git, container, or YAML confidence
you have never operated a basic cluster locally or in a learning environment
you are trying to learn “all cloud” before you can reason clearly about one provider

If that is your situation, start with Prerequisites first and then come back here with stronger Kubernetes basics.

Safest Default Route

Hyperscaler Rosetta Stone
   |
Pick one provider
   |
Provider Essentials
   |
Managed Kubernetes (EKS / GKE / AKS)
   |
Architecture & Enterprise

Do not try to learn all three providers at once unless you have a specific architecture reason. Most learners should go deep on one provider first and treat the others as later translation work, not simultaneous study.

Which Cloud Route Fits You?

If your goal is…	Start with…	Then move to…
become productive on the cloud your team already uses	the matching provider essentials section	that provider’s managed Kubernetes path
compare providers without drowning in details	Hyperscaler Rosetta Stone	one provider essentials path only
support managed Kubernetes on AWS in a production team	AWS Essentials	EKS Deep Dive
support managed Kubernetes on Google Cloud in a production team	GCP Essentials	GKE Deep Dive
support managed Kubernetes on Azure in a production team	Azure Essentials	AKS Deep Dive
reason about enterprise tradeoffs, hybrid design, and operating models	one provider path first	Architecture Patterns -> Advanced Operations -> Enterprise & Hybrid

The mistake to avoid is trying to use Architecture & Enterprise as your entry point. Those sections assume you already understand at least one provider’s primitives.

Equivalent depth means this:

any one of AWS Essentials -> EKS, GCP Essentials -> GKE, or Azure Essentials -> AKS is enough to make you productive on that provider
none of those paths makes you “multi-cloud ready” by itself
the cross-provider insight comes later, once one provider already feels operationally normal to you

Sections

AWS (17 modules)

Section	Modules	Description
AWS Essentials	12	IAM, VPC, EC2, S3, Route53, ECR, ECS, Lambda, Secrets, CloudWatch, CI/CD, CloudFormation
EKS Deep Dive	5	EKS architecture, networking, identity, autoscaling, production

Google Cloud (17 modules)

Section	Modules	Description
GCP Essentials	12	IAM, VPC, Compute, Cloud Storage, DNS, Artifact Registry, Cloud Run, Functions, Secret Manager, Monitoring, Cloud Build, Deployment Manager
GKE Deep Dive	5	GKE architecture, networking, Workload Identity, Autopilot, Fleet

Azure (16 modules)

Section	Modules	Description
Azure Essentials	12	Entra ID, VNet, VMs, Blob Storage, Azure DNS, ACR, ACI, Functions, Key Vault, Monitor, DevOps, Bicep
AKS Deep Dive	4	AKS architecture, networking, identity, production

Architecture & Enterprise (34 modules)

Section	Modules	Description
Hyperscaler Rosetta Stone	1	Cross-provider concept mapping
Architecture Patterns	4	Managed vs self-managed, multi-cluster, cloud IAM, VPC topologies
Advanced Operations	10	Multi-account, transit hubs, cross-cluster networking, DR, active-active
Managed Services	10	Databases, caching, messaging, ML services, analytics
Enterprise & Hybrid	10	Landing zones, hybrid connectivity, compliance, migration, fleet management

84 modules total. Not everything goes into Kubernetes — the essentials tracks cover standalone containers, serverless, and when K8s is overkill.

Prerequisites

Fundamentals — Cloud Native 101, Docker, basic K8s (~6-8 hours if you only need the cloud-facing subset)
Linux — recommended for networking and security modules
Certifications — recommended (CKA/CKAD give hands-on K8s experience before cloud-specific deep dives)

Common Failure Modes In Cloud

trying to learn all three providers before you can operate one of them well
jumping into enterprise and hybrid sections before a provider essentials path is solid
confusing provider familiarity with platform-engineering depth
treating managed-cloud convenience as proof that you understand the underlying operational tradeoffs

Good Next Steps After Cloud

go to Platform Engineering if you want SRE, GitOps, delivery automation, and platform design on top of cloud infrastructure
go to On-Premises if you want to compare managed-cloud assumptions with private-infrastructure realities
go to AI/ML Engineering if your cloud path is mainly about serving, training, or operating ML workloads on managed platforms

If you are responsible for production at scale, treat Advanced Operations as part of the real cloud path rather than an optional appendix. Provider essentials and managed-Kubernetes basics get you running; the operations and enterprise sections are where multi-account design, networking boundaries, resilience, and governance start to look like production.

Choose Your Exit Ramp

After Cloud, if you want to…	Go to…	Why
run internal platforms, improve delivery, and standardize operations	Platform Engineering	cloud skill alone does not teach platform discipline
evaluate private infrastructure, repatriation, or hybrid tradeoffs	On-Premises	managed services hide many concerns that become explicit on private infrastructure
support ML workloads and AI systems on cloud Kubernetes	AI/ML Engineering	the AI/ML track gives the application and model layer this track does not