I am an AI product and solution architect working on the practical layer between GPU infrastructure and intelligent applications. My work turns GPU clusters, workload scheduling, model deployment, safe agent execution, and engineering knowledge into reusable platforms and public-facing technical material.

My public focus is Kubernetes-native AI workloads, GPU scheduling boundaries, model-serving workflows, and OPD-inspired agent learning. I care about clear ownership: the platform governs workloads, the scheduler places resources, the runtime executes distributed work, and the product layer makes the workflow understandable and repeatable.

The goal is not complexity for its own sake. It is to make shared AI infrastructure easier to operate, teach, and improve.

Workload Scheduling And GPU AI Infrastructure

• Kubernetes-native workload admission, queueing, quota, and placement.
• GPU visibility, resource isolation, developer workspaces, and serving templates.
• A clear boundary between platform orchestration, scheduling, and distributed runtime execution.

Model Runtime And Developer Workflows

• Practical serving paths around vLLM, TensorRT-LLM, Triton, and related runtime layers.
• Browser-based environments such as code-server and notebook-style workspaces.
• Public-safe troubleshooting for GPU visibility, workload startup, endpoint exposure, and runtime logs.

OPD And Agent Learning

• Trajectory learning that keeps supervision separate from execution.
• Human-reviewed promotion gates before traces become reusable behavior.
• Evaluation-first work traces without exposing private data or internal implementation details.

OCDP: One Click Model Deployment

OCDP is an AI infrastructure direction for turning heterogeneous GPU clusters into repeatable model deployment workflows. The public-facing idea is simple: expose GPU development environments, model-serving endpoints, workload templates, and platform governance through a browser-based control surface instead of asking users to operate Kubernetes and GPU nodes directly.

Workload Scheduling Notes

This work studies how AI workloads are admitted, placed, observed, and debugged across shared GPU clusters. Its focus is architectural boundaries rather than private implementation: queue admission, quota, readiness, runtime ownership, and auditable evidence for training or serving jobs.

OPD / Personal Agent Learning

This direction explores how work traces become safer reusable behavior: collect traces, evaluate them, separate supervision from execution, and promote only reviewed patterns. It treats unverified automation as a learning input, not production autonomy.

• GitHub: github.com/superobk
• LinkedIn: linkedin.com/in/bensonou