#mlops

Overall system architecture of a production-grade Agentic AI Platform — 6 runtime layers and 3 cross-cutting planes

AI platform monitoring, observability, evaluation, compliance, and domain-specific operations guide

A hybrid ML architecture that trains on SageMaker and serves on EKS

EKS-based 5-stage pipeline that automatically promotes Langfuse traces to training data and connects GRPO/DPO preference tuning with Canary deployment.

Custom model deployment, fine-tuning pipelines, MLOps orchestration, continuous training pipelines

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