GPU ML Ops — Managing Training and Serving at Scale

Why it matters

GPU workloads have specific ops needs. Understanding shapes production ML infra.

The architecture

Training: Slurm, Kubernetes+Kubeflow, Ray, Determined.

Serving: K8s + specialized serving.

How it works end to end

Checkpointing: frequent (every hour); async to avoid slowdown.

Monitoring: DCGM for GPU + framework metrics (loss, throughput).

Cost tracking: per-experiment + per-team.

Experiment tracking: MLflow, W&B, Neptune.