Design training job submission to a shared Kubernetes cluster
WHAT IT TESTS: Multi-tenant ML infrastructure with usability, fairness, observability. ANSWER OUTLINE: Gateway with artifact caching; namespace quotas; GPU schedulers like Volcano; Prometheus metrics and cost attribution.
WHAT IT TESTS: End-to-end design of a shared Kubernetes platform that lets data scientists submit distributed training jobs without managing raw YAML, enforcing fairness and cost control. ANSWER OUTLINE: A good answer layers a submission gateway and artifact cache over namespace-isolated worker pools, uses GPU-aware batch schedulers like Volcano or Kueue to prevent fragmentation, and exposes Prometheus metrics with per-team cost attribution plus centralized logging.
Read the original → kubeflow.org
- #mlops
- #kubernetes
- #system-design
- #kubeflow
- #scheduling
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