Workload / Training
Training and fine-tuning
How to size a job across experimentation, scaling and production handoff.
Pilot with a representative slice
Record the base model, dataset and sequence length, target quality, batch size and framework. Run a small baseline with reproducible settings before projecting a full job. For fine-tuning, distinguish parameter-efficient methods from full-model updates.
Measure the scaling transition
Track useful work per wall-clock hour across single-node and multi-node runs. Include data staging, checkpointing and restart intervals. A change in algorithm, precision or target quality invalidates a simple comparison.
Budget for the whole pipeline
Include data ingestion, validation, eval runs, artifact retention and final handoff. Have someone own reproducibility and the escalation path. Model a deadline and capacity reservation.