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Stop Learning Theory. Start Building Production Systems.

Most Kubernetes content stops at kubectl apply.

I go deeper — control plane internals, production debugging, GPU infrastructure for ML workloads, and the decisions that separate a working cluster from a production-grade one.

I manage H100 GPU clusters in production and write about what actually works when systems need to scale, serve, and not break at 3 AM.

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Production Kubernetes for ML/AI workloads: GPU infrastructure, control plane internals, and model serving patterns for engineers running inference at scale.

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