Welcome to KubeDojo

The Kubernetes ecosystem keeps expanding. What started as a container orchestrator now sits at the center of platform engineering, AI/ML infrastructure, security, and GitOps. New tooling, new patterns, and new challenges emerge every quarter. For anyone building and operating on Kubernetes, there's always something worth digging into: a scheduling pattern that changes how you think about GPU workloads, a Helm chart structure that survives real multi-environment deployments, a security model that holds up when the cluster scales.

That's what KubeDojo is about. Deep dives into the Kubernetes ecosystem, grounded in real code, real trade-offs, and the kind of practical insights that come from building in production. Think of it as a practitioner's notebook, written for other practitioners.

What KubeDojo Is

KubeDojo is a content platform focused on the Kubernetes ecosystem. Certifications, Helm chart packaging, AI/ML workloads, infrastructure patterns that survive contact with production. It all belongs here.

The scope is deliberately wide within that orbit. Kubernetes is the center, but the ecosystem doesn't stop at the cluster boundary. Networking, security, observability, CI/CD, and the tooling that surrounds it are all in scope. If a topic matters to someone building and operating Kubernetes infrastructure, it belongs here.

What ties the content together isn't a technology. It's an approach.

Grounded in real experience. Code examples, configurations, and architecture decisions are drawn from hands-on work: real projects, real experiments, real debugging sessions. Not everything comes straight from production, but everything comes from actually building and testing things, not from hypothetical scenarios.

Depth over breadth. A single article that thoroughly covers one concept, with real code blocks, real command output, and real gotchas, is worth more than ten that skim the surface. If a topic needs 2,500 words and a dozen code blocks, it gets them. If it can be said in 800 words, it doesn't get padded.

Honest about trade-offs. One engineer explaining to another. Opinionated where it matters, candid about what worked and what didn't. When something is overengineered for the problem at hand, I'll say so. When a tool or pattern genuinely improved my workflow, I'll explain why with specifics — not with adjectives.

Structured learning paths. Most content is organized into series that build on each other and cover a topic end to end. Each series has a clear scope, a defined audience, and a progression that takes you from foundational concepts to production patterns.

What You'll Find Here

Kubernetes certifications. If you're preparing for CKAD, CKA, or CKS, you'll find coverage of every exam domain with hands-on exercises and non-obvious gotchas that may cost points on exam day. I've gone through the certification process myself and want to share the tips, the traps, and the mindset that made the difference.

Helm charts and packaging. Building, testing, publishing, and maintaining charts that teams depend on. The decisions that make a chart maintainable at scale: dependency management, value schema design, test strategies, and the upgrade paths nobody thinks about until they break. I've been through enough chart migrations to have opinions here, and I'll share the war stories alongside the patterns.

AI/ML on Kubernetes. GPU scheduling, training pipelines, model serving, and the infrastructure decisions that shape ML workloads in production. This space is moving fast. AMD's GPU Operator now brings GPU partitioning and lifecycle management to Kubernetes, Strix Halo APUs are opening up interesting possibilities for edge and homelab AI workloads, and autoscaling inference is still a surprisingly open problem. There's a lot to dig into from a platform engineer's perspective.

Infrastructure patterns. GitOps with FluxCD, CI/CD, observability, security, and the operational realities that only become visible after you've operated a system long enough to discover which abstractions hold up and which ones were premature. How to structure a FluxCD repository for multi-cluster delivery, how to handle secrets in a GitOps pipeline, how to design disaster recovery that actually recovers. The unglamorous but critical work.

And more. Operator development, custom controllers, service mesh, policy engines... The Kubernetes ecosystem is broad, and KubeDojo's scope will grow with it. If a topic connects to building and operating on Kubernetes, it's fair game.

Who's Behind This

Over 20 years of building and operating software systems, from backend services and data platforms to cloud-native infrastructure on Kubernetes. Today I architect platforms with GitOps delivery, write operators and AI-powered tooling in Rust, and run GPU scheduling and model-serving infrastructure. The things I write about here are the same things I work on day to day.

But I don't have all the answers. Not by a long shot. The Kubernetes ecosystem is vast, and it keeps growing. What I can offer is honest reporting from the trenches: what I've tried, what worked, what didn't, and what I'm still figuring out. I'd rather share an imperfect but genuine insight than pretend I've got it all mapped out.

KubeDojo is an independent project. No vendor agenda, no editorial calendar driven by marketing goals. Just the things I find genuinely useful, shared with people who might find them useful too.

What's Next

KubeDojo is just getting started. The Kubernetes certification landscape overview is already live, and the CKAD certification series launches next: many articles covering every exam domain, each built around real code. From there, the scope expands into Helm packaging, AI/ML infrastructure, and whatever else the ecosystem throws at us.

There's always more to explore. Kubernetes sits at the center of so much infrastructure work today, and the ecosystem around it keeps evolving: the tooling, the patterns, the community. I'm excited to keep learning and sharing what I find along the way.

If you're an engineer who builds on Kubernetes, operates clusters, or is curious about exploring the ecosystem, welcome. Subscribe to the newsletter to follow along as new content ships.

Real code. Honest trade-offs. Lessons from the field.