Turning a training script into a scheduled pipeline, data validation gates, distributed training, checkpointing, and failure recovery.
3 questions in MLOps & ML Platform.
Split it at points where the intermediate output is worth keeping, make each stage idempotent so a retry is safe, put validation gates before training that fail the run loudly rather than warning, pass artefacts by immutable reference between stages, and set retry semantics per stage rather than globally.
Periodic checkpoints of model weights, optimiser state and data-loader position written atomically to durable storage; a resume path that is exercised rather than assumed; and interruption handling that checkpoints on the preemption signal. Each is justified by the expected cost of lost compute, not by principle.
Usually later than people reach for it. The first answer is a bigger single machine, because within one host the interconnect is far faster than any network and there is no distributed failure mode. Distribute when the model no longer fits one accelerator, or when a single-machine run is so long it blocks the work.