Welcome to KubeDojo
What KubeDojo is and what you'll find here: deep dives into real code, honest explorations of the Kubernetes ecosystem, and structured learning paths to master every certification.
by KubeDojo
Master the Kubernetes Ecosystem
Deep-dive guides, real code, and structured learning paths — from core workloads to AI infrastructure.
Featured
The Kubernetes Certification Landscape
A practical map of the five CNCF Kubernetes certifications — what each one covers, how exams work, and which path fits your career.
by KubeDojo
llm-d Joins CNCF Sandbox: Kubernetes-Native Distributed LLM Inference
llm-d was accepted as a CNCF Sandbox project, providing Kubernetes-native distributed inference with KV-cache-aware routing, prefill/decode disaggregation, and accelerator-agnostic serving.
by KubeDojo
Learn Kubernetes the structured way
5 certification tracks · 196 lessons · Real code, real manifests, real exam prep.
CKA
Master every CKA exam competency — from cluster setup to troubleshooting. Real cluster operations, kubectl workflows, and hands-on exercises across 5 exam domains.
5 modules · 63 lessons
CKS
Master every CKS exam competency — from cluster hardening to runtime security. Real policies, security tools, attack scenarios, and hands-on exercises across 6 exam domains.
6 modules · 25 lessons
Recent Posts
Introduction to KEDA and Event-Driven Autoscaling
How KEDA extends Kubernetes HPA with 65+ scalers, scale-to-zero, and a two-phase architecture for event-driven pod autoscaling.
by KubeDojo
Message Queue Scaling with KEDA — Kafka and SQS
How KEDA's Kafka and SQS scalers calculate lag and queue depth, with TriggerAuthentication patterns and production edge cases.
by KubeDojo
Custom Metrics and Prometheus-Based Scaling with KEDA
Using KEDA's Prometheus scaler to drive autoscaling from any PromQL query — replacing Prometheus Adapter with a simpler, more flexible approach.
by KubeDojo
HTTP-Based Autoscaling with the KEDA HTTP Add-on
How the KEDA HTTP Add-on intercepts traffic to scale HTTP workloads to zero, and when the Prometheus scaler is better.
by KubeDojo
Batch Processing with KEDA ScaledJobs
ScaledJob creates one Kubernetes Job per event, scales dynamically, and lets long-running batch workloads terminate cleanly.
by KubeDojo
KEDA and Karpenter Together — Pod and Node Scaling Synergy
Combining KEDA's event-driven pod scaling with Karpenter's just-in-time node provisioning for a fully reactive, cost-efficient Kubernetes autoscaling stack.
by KubeDojo
Observability and Troubleshooting for KEDA
Scraping KEDA operator metrics, building Grafana dashboards for scaling events, and diagnosing common ScaledObject issues in production.
by KubeDojo
Agentic AI Workloads on Kubernetes
How kagent, Agent Sandbox, KEDA, and OPA/Kyverno form the production stack for agentic AI on Kubernetes.
by KubeDojo
GPU Sharing Strategies for Multi-Tenant Kubernetes: MIG, Time-Slicing, and MPS
NVIDIA's GPU sharing mechanisms — MIG, time-slicing, and MPS — are gaining traction as teams run multiple inference workloads per GPU.
by KubeDojo
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