Design a Shared Distributed ML Training Platform

Design a distributed machine-learning training platform shared by multiple teams. Explain how teams submit training work, obtain compute resources, access datasets, track runs, and recover from failures. Clarify the supported training frameworks, distributed execution model, and isolation requirements before choosing infrastructure. ### What a Strong Answer Covers - A reproducible training-job specification and lifecycle from submission to published artifacts. - Resource quotas, fair sharing, and placement for jobs that require several workers to run together. - Dataset and artifact versioning, team access boundaries, and useful experiment tracking. - Checkpointing, distributed-worker failure handling, and the tradeoff between utilization and predictable completion. ### Follow-up Questions - How would you avoid admitting several distributed jobs that each obtain only some of the workers they need? - What state must a checkpoint preserve for a training run to resume correctly?