platform-expert

name	platform-expert
description	Platform Engineering discipline expertise (SRE, GitOps, DevSecOps, MLOps, Platform Eng). Use for discipline content, tool recommendations, architecture patterns. Triggers on "SRE", "GitOps", "DevSecOps", "MLOps", "platform engineering", "SLO", "ArgoCD", "Falco".

Platform Expert

Consolidated knowledge for all Platform Engineering disciplines and toolkits. Use when writing, reviewing, or verifying platform content.

Disciplines Overview

Discipline	Modules	Core Concepts
SRE	7	SLOs, error budgets, toil, incident management, capacity planning
Platform Engineering	6	IDPs, golden paths, self-service, developer experience, SPACE metrics
GitOps	6	Declarative config, reconciliation, ArgoCD, Flux, progressive delivery
DevSecOps	6 + guide	Shift-left, CI/CD security, supply chain, runtime, security culture
MLOps	6	Feature stores, model serving, monitoring, Kubeflow, MLflow
AIOps	6	Anomaly detection, event correlation, observability AI
Release Engineering	5	Release pipelines, feature flags, canary, blue-green
Chaos Engineering	5	Failure injection, game days, steady state hypothesis
FinOps	6	Cloud cost optimization, showback, rightsizing, spot instances
Data Engineering	6	Streaming, batch, data mesh, Kafka, Spark on K8s
Advanced Networking	6	DNS at scale, CDN, WAF, BGP, load balancing, zero trust
AI/GPU Infrastructure	6	GPU scheduling, vGPU, model serving infra, training clusters
Engineering Leadership	6	Incident command, postmortems, on-call, ADRs, stakeholders

SRE Core

Service Level Hierarchy: SLI (what you measure) → SLO (target) → SLA (contract). SLA < SLO always.

Error Budget: 1 - SLO target. At 99.9% = 43.2 min/month. Healthy budget → ship fast. Depleted → freeze + fix.

Four Golden Signals: Latency, Traffic, Errors, Saturation.

Toil: Manual, repetitive, automatable work. Target <50% of SRE time. Measure and eliminate.

GitOps Core

Four Principles: Declarative, versioned, pulled automatically, continuously reconciled.

Tool Comparison:

	ArgoCD	Flux
UI	Rich web UI	CLI-first
Multi-cluster	App-of-apps	Kustomization
RBAC	Built-in	K8s native
Notifications	Built-in	Controllers

DevSecOps Core

Pipeline Stages: Source (secrets scan) → Build (SAST, SCA, image scan) → Test (DAST) → Deploy (policy, sign, admit)

Supply Chain: SBOM (know ingredients), Sigstore/Cosign (verify origin), SLSA (provenance levels), admission control (enforce at deploy).

Runtime: Falco (detect), Tetragon (enforce at kernel), KubeArmor (least privilege), NetworkPolicies (segment).

Platform Engineering Core

IDP Layers: Infrastructure (Crossplane, Terraform) → Platform (K8s, service mesh) → Developer (Backstage, golden paths)

Maturity Levels: L1 (ad-hoc) → L2 (standardized) → L3 (self-service) → L4 (optimized) → L5 (innovation)

SPACE Framework: Satisfaction, Performance, Activity, Communication, Efficiency.

MLOps Core

ML Lifecycle: Data → Features → Train → Validate → Serve → Monitor → Retrain

Drift Types: Data drift (input distribution), concept drift (relationship changes), prediction drift (output shifts)

Key Tools: Kubeflow (pipelines), MLflow (tracking), Feast (features), KServe (serving), Evidently (monitoring)

Toolkits (17 categories, 96 modules)

Category	Key Tools
Observability	Prometheus, OpenTelemetry, Grafana, Loki, Jaeger
GitOps & Deploy	ArgoCD, Argo Rollouts, Flux, Helm/Kustomize
CI/CD	Dagger, Tekton, Argo Workflows
Security	Vault+ESO, OPA, Falco, Tetragon, KubeArmor, Supply Chain
Networking	Cilium, Service Mesh (Istio)
Scaling	Karpenter, KEDA, Velero
Platforms	Backstage, Crossplane, cert-manager
Developer Exp	k9s, Telepresence, DevPod, Gitpod
ML Platforms	Kubeflow, MLflow, Feast, vLLM, Ray Serve
Code Quality	SonarQube, Semgrep, CodeQL, Snyk, Trivy
Storage	Rook/Ceph, MinIO, Longhorn
K8s Distros	k3s, k0s, MicroK8s, Talos, OpenShift
IaC	Terraform, OpenTofu, Pulumi, Ansible, CloudFormation, Bicep

KubeDojo Module Counts

Section	Modules	Path
Foundations	32	docs/platform/foundations/
Disciplines	71	docs/platform/disciplines/
Toolkits	96	docs/platform/toolkits/
Total	199