Platform Engineering

Principles, practices, and disciplines for running production systems on Kubernetes.

Kubernetes certifications teach you how to use Kubernetes. This track teaches you how to run production systems — the theory, disciplines, and leadership that separate operators from practitioners.

This is for people who:

• Have Kubernetes fundamentals (or certifications)
• Want to understand theory, not just tools
• Need to make technology decisions at work
• Want to implement best practices, not just pass exams

Looking for tool-specific guides? See Cloud Native Tools.

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Structure

platform/
├── foundations/                         # Theory that doesn't change (32 modules)
│   ├── systems-thinking/               # Mental models for complex systems
│   ├── reliability-engineering/        # Failure theory, redundancy, risk
│   ├── observability-theory/           # What to measure and why
│   ├── security-principles/           # Zero trust, threat modeling
│   ├── distributed-systems/           # CAP, consensus, consistency
│   ├── advanced-networking/           # Network theory, protocols, design
│   └── engineering-leadership/        # Technical leadership, org design
│
└── disciplines/                        # Applied practices (81 modules)
    ├── core-platform/
    │   ├── sre/                        # Operations, reliability, on-call
    │   ├── platform-engineering/       # Developer experience, self-service
    │   └── platform-leadership/        # Strategy, adoption, evangelism
    │
    ├── delivery-automation/
    │   ├── release-engineering/        # Build, release, deploy lifecycle
    │   ├── gitops/                     # Deployment, reconciliation
    │   └── iac/                        # IaC patterns, testing, drift
    │
    ├── reliability-security/
    │   ├── networking/                 # Network architecture, policy
    │   ├── chaos-engineering/          # Failure injection, resilience
    │   └── devsecops/                  # Security integration, compliance
    │
    ├── data-ai/
    │   ├── data-engineering/           # Pipelines, streaming, storage
    │   ├── mlops/                      # ML lifecycle, model serving
    │   ├── aiops/                      # AI-driven operations
    │   └── ai-infrastructure/          # GPU scheduling, model hosting
    │
    └── business-value/
        └── finops/                     # Cloud cost optimization

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Reading Order

Start with Foundations

Theory that applies everywhere. Read these first — they don’t change.

Track	Why Start Here
Systems Thinking	Mental models for complex systems
Reliability Engineering	Failure theory, redundancy, risk
Distributed Systems	CAP, consensus, consistency
Observability Theory	What to measure and why
Security Principles	Zero trust, threat modeling
Advanced Networking	Network theory, protocols, design
Engineering Leadership	Technical leadership, org design

Then Pick a Discipline

Applied practices — how to do the work.

Core Platform

Discipline	Modules	Best For
SRE	7	Operations, reliability, on-call
Platform Engineering	6	Developer experience, self-service
Platform Leadership	5	Strategy, adoption, evangelism

Delivery & Automation

Discipline	Modules	Best For
Release Engineering	5	Build, release, deploy lifecycle
GitOps	6	Deployment, reconciliation
Infrastructure as Code	6	IaC patterns, testing, drift management

Reliability & Security

Discipline	Modules	Best For
Networking	5	Network architecture, policy, design
Chaos Engineering	5	Failure injection, resilience
DevSecOps	7	Security integration, compliance

Data & AI

Discipline	Modules	Best For
Data Engineering	6	Pipelines, streaming, storage
MLOps	6	ML lifecycle, model serving
AIOps	6	AI-driven operations, automation
AI Infrastructure	6	GPU scheduling, model hosting

Business Value

Discipline	Modules	Best For
FinOps	6	Cloud cost optimization

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Module Format

Every module includes:

• Why This Matters — Real-world motivation
• Theory — Principles and mental models
• Current Landscape — Tools that implement this
• Hands-On — Practical implementation
• Best Practices — What good looks like
• Common Mistakes — Anti-patterns to avoid
• Further Reading — Books, talks, papers

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Status

Section	Modules	Description
Foundations	32	7 sections: Systems Thinking, Reliability Engineering, Observability Theory, Security Principles, Distributed Systems, Advanced Networking, Engineering Leadership
Disciplines	81	14 disciplines across Core Platform, Delivery & Automation, Reliability & Security, Data & AI, and Business Value
Total	113

Tool-specific implementation guides (96 modules) are in Cloud Native Tools.

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Prerequisites

Before starting this track, you should have:

• Kubernetes basics (or completed Prerequisites)
• Some production experience (helpful but not required)
• Curiosity about “why” not just “how”

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“Tools change. Principles don’t.”