Deep-dive guides, real code, and structured learning paths — from core workloads to AI infrastructure.
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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.
HAMi (CNCF Sandbox) emerged at KubeCon EU 2026 as the reference implementation for GPU resource management across NVIDIA, AMD, Huawei, and Cambricon accelerators.
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.
5 certification tracks · 196 lessons · Real code, real manifests, real exam prep.
Kubernetes security fundamentals — threat models, cluster hardening, platform security, and compliance frameworks. The conceptual foundation before CKS.
6 modules · 39 lessons
Your first step into Kubernetes — core concepts, cloud native architecture, and the CNCF ecosystem. The foundational certification before CKA and CKAD.
4 modules · 25 lessons
Recent Posts
How KEDA's Kafka and SQS scalers calculate lag and queue depth, with TriggerAuthentication patterns and production edge cases.
How KEDA extends Kubernetes HPA with 65+ scalers, scale-to-zero, and a two-phase architecture for event-driven pod autoscaling.
ScaledJob creates one Kubernetes Job per event, scales dynamically, and lets long-running batch workloads terminate cleanly.
How the KEDA HTTP Add-on intercepts traffic to scale HTTP workloads to zero, and when the Prometheus scaler is better.
Scraping KEDA operator metrics, building Grafana dashboards for scaling events, and diagnosing common ScaledObject issues in production.
Using KEDA's Prometheus scaler to drive autoscaling from any PromQL query — replacing Prometheus Adapter with a simpler, more flexible approach.
Armada treats multiple Kubernetes clusters as a single resource pool for GPU-intensive AI workloads, with global queue management, gang scheduling, and production-scale throughput.
Combining KEDA's event-driven pod scaling with Karpenter's just-in-time node provisioning for a fully reactive, cost-efficient Kubernetes autoscaling stack.
NVIDIA's GPU sharing mechanisms — MIG, time-slicing, and MPS — are gaining traction as teams run multiple inference workloads per GPU.