#ml-pipeline

End-to-end ML lifecycle management with Kubeflow + MLflow + vLLM + ArgoCD GitOps

An architecture that automates open-weight model onboarding through a seven-stage pipeline, from HuggingFace leaderboard scanning and benchmark reproduction to instance performance profiling, deployment guide generation for multiple targets, and global Spot capacity acquisition, with human involvement at approval gates