Platform
AI infrastructure across training, serving, and operations
One operating layer for the model lifecycle
yunchao.org connects training, serving, and operational governance so teams can move from experiments to dependable products without rebuilding the same infrastructure glue.
G Train
Distributed training platform
- GPU cluster orchestration across cloud and private environments
- Automatic checkpointing for long-running model jobs
- Experiment metadata, artifact tracking, and scheduling policies
G Serve
Production inference engine
- Autoscaling endpoints for LLM, vision, and custom models
- Version routing, canary rollout, and rollback support
- Latency, cost, and quality telemetry for live workloads
G Flow
MLOps control plane
- Model registry, approvals, and release history
- Pipeline automation from validation to deployment
- Monitoring signals for drift, quality, and ownership
Research teams keep experiments reproducible, portable, and tied to the data and hardware context that produced them.
Engineering teams deploy models behind stable APIs with rollout controls and runtime observability.
Operations teams track ownership, cost, incident signals, and lifecycle status across production AI systems.