Invisible Infrastructure

The best infrastructure is the one you never think about. When deploying AI microservices, the goal is to make the jump from a Jupyter notebook to a production Kubernetes cluster feel like a natural evolution, not a traumatic event.

Scaling the “Heavy” Bits

AI workloads are notoriously resource-intensive. Scaling a FastAPI container is easy; scaling a GPU-dependent inference service while managing memory fragmentation and cold-start latency is a different beast.

Kubernetes as an Enabler

Using K8s, I’ve established self-healing pipelines that manage these stateful workloads. By abstracting the “where” and “how” of deployment, we allow the engineering team to focus entirely on the logic of the models themselves.

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